{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "bc276da9-f5e8-47c5-8a0f-deba975e39c1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PregnantGlucoseDiastolic_BPSkin_FoldSerum_InsulinBMIDiabetes_PedigreeAgeClass
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" ], "text/plain": [ " Pregnant Glucose Diastolic_BP Skin_Fold Serum_Insulin BMI \\\n", "0 6 148.0 72.0 35.0 NaN 33.6 \n", "1 1 85.0 66.0 29.0 NaN 26.6 \n", "2 8 183.0 64.0 NaN NaN 23.3 \n", "3 1 89.0 66.0 23.0 94.0 28.1 \n", "4 0 137.0 40.0 35.0 168.0 43.1 \n", "\n", " Diabetes_Pedigree Age Class \n", "0 0.627 50 1 \n", "1 0.351 31 0 \n", "2 0.672 32 1 \n", "3 0.167 21 0 \n", "4 2.288 33 1 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df=pd.read_csv('Diabetes Missing Data (1).csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 2, "id": "48c22617-a84e-4b03-804a-b32bf512e57a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PregnantGlucoseDiastolic_BPSkin_FoldSerum_InsulinBMIDiabetes_PedigreeAgeClass
count768.000000763.000000733.000000541.000000394.000000757.000000768.000000768.000000768.000000
mean3.845052121.68676372.40518429.153420155.54822332.4574640.47187633.2408850.348958
std3.36957830.53564112.38215810.476982118.7758556.9249880.33132911.7602320.476951
min0.00000044.00000024.0000007.00000014.00000018.2000000.07800021.0000000.000000
25%1.00000099.00000064.00000022.00000076.25000027.5000000.24375024.0000000.000000
50%3.000000117.00000072.00000029.000000125.00000032.3000000.37250029.0000000.000000
75%6.000000141.00000080.00000036.000000190.00000036.6000000.62625041.0000001.000000
max17.000000199.000000122.00000099.000000846.00000067.1000002.42000081.0000001.000000
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" ], "text/plain": [ " Pregnant Glucose Diastolic_BP Skin_Fold Serum_Insulin \\\n", "count 768.000000 763.000000 733.000000 541.000000 394.000000 \n", "mean 3.845052 121.686763 72.405184 29.153420 155.548223 \n", "std 3.369578 30.535641 12.382158 10.476982 118.775855 \n", "min 0.000000 44.000000 24.000000 7.000000 14.000000 \n", "25% 1.000000 99.000000 64.000000 22.000000 76.250000 \n", "50% 3.000000 117.000000 72.000000 29.000000 125.000000 \n", "75% 6.000000 141.000000 80.000000 36.000000 190.000000 \n", "max 17.000000 199.000000 122.000000 99.000000 846.000000 \n", "\n", " BMI Diabetes_Pedigree Age Class \n", "count 757.000000 768.000000 768.000000 768.000000 \n", "mean 32.457464 0.471876 33.240885 0.348958 \n", "std 6.924988 0.331329 11.760232 0.476951 \n", "min 18.200000 0.078000 21.000000 0.000000 \n", "25% 27.500000 0.243750 24.000000 0.000000 \n", "50% 32.300000 0.372500 29.000000 0.000000 \n", "75% 36.600000 0.626250 41.000000 1.000000 \n", "max 67.100000 2.420000 81.000000 1.000000 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 3, "id": "e25aae36-4bb4-46d7-9dda-f368c1e2a32e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pregnant 0\n", "Glucose 5\n", "Diastolic_BP 35\n", "Skin_Fold 227\n", "Serum_Insulin 374\n", "BMI 11\n", "Diabetes_Pedigree 0\n", "Age 0\n", "Class 0\n", "dtype: int64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 4, "id": "a0f5d5e3-7394-485d-b194-cd7682f71b01", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "768" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(df)\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "9d79af6c-7121-48da-8e8d-897337f2ddc6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns dropped: ['Serum_Insulin']\n", "Remaining columns: Index(['Pregnant', 'Glucose', 'Diastolic_BP', 'Skin_Fold', 'BMI',\n", " 'Diabetes_Pedigree', 'Age', 'Class'],\n", " dtype='object')\n" ] } ], "source": [ "missing_percentage = df.isnull().mean() * 100\n", "threshold = 30 \n", "\n", "columns_to_drop = missing_percentage[missing_percentage > threshold].index\n", "\n", "df_cleaned = df.drop(columns=columns_to_drop)\n", "\n", "print(f\"Columns dropped: {list(columns_to_drop)}\")\n", "print(f\"Remaining columns: {df_cleaned.columns}\")" ] }, { "cell_type": "code", "execution_count": 17, "id": "25538e90-5d9e-40d2-82fa-e4dd5d51b097", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " A B C\n", "0 1.000000 5.0 cat\n", "1 2.000000 7.0 cat\n", "2 2.333333 7.0 dog\n", "3 4.000000 8.0 cat\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\alaam\\AppData\\Local\\Temp\\ipykernel_15508\\4252160193.py:7: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " data['A'].fillna(data['A'].mean(), inplace=True)\n", "C:\\Users\\alaam\\AppData\\Local\\Temp\\ipykernel_15508\\4252160193.py:9: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " data['B'].fillna(data['B'].median(), inplace=True)\n", "C:\\Users\\alaam\\AppData\\Local\\Temp\\ipykernel_15508\\4252160193.py:11: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " data['C'].fillna(data['C'].mode()[0], inplace=True)\n" ] } ], "source": [ "data = pd.DataFrame({\n", " 'A': [1, 2, None, 4],\n", " 'B': [5, None, 7, 8],\n", " 'C': ['cat', None, 'dog', 'cat']\n", " })\n", "\n", "data['A'].fillna(data['A'].mean(), inplace=True)\n", "\n", "data['B'].fillna(data['B'].median(), inplace=True)\n", "\n", "data['C'].fillna(data['C'].mode()[0], inplace=True)\n", "print(data)" ] }, { "cell_type": "code", "execution_count": 21, "id": "80c2a7ca-ab0d-4482-af08-fc9526354a90", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " A B\n", "0 1.0 NaN\n", "1 1.0 6.0\n", "2 3.0 6.0\n", "3 3.0 8.0\n", "4 5.0 8.0\n", " A B\n", "0 1.0 6.0\n", "1 3.0 6.0\n", "2 3.0 8.0\n", "3 5.0 8.0\n", "4 5.0 NaN\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\alaam\\AppData\\Local\\Temp\\ipykernel_15508\\2483755367.py:6: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " data_forward_fill = data.fillna(method='ffill')\n", "C:\\Users\\alaam\\AppData\\Local\\Temp\\ipykernel_15508\\2483755367.py:8: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " data_backward_fill = data.fillna(method='bfill')\n" ] } ], "source": [ "data = pd.DataFrame({\n", " 'A': [1, None, 3, None, 5],\n", " 'B': [None, 6, None, 8, None]\n", " })\n", "\n", "data_forward_fill = data.fillna(method='ffill')\n", "\n", "data_backward_fill = data.fillna(method='bfill')\n", "print(data_forward_fill)\n", "print(data_backward_fill)" ] }, { "cell_type": "code", "execution_count": 25, "id": "098b54f7-3ea6-4ea7-9974-af9b14fa6546", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " A B C\n", "0 1.0 5.0 9.0\n", "1 2.0 6.0 10.0\n", "2 3.0 7.0 11.0\n", "3 4.0 8.0 10.5\n" ] } ], "source": [ "from sklearn.impute import KNNImputer\n", "\n", "data = pd.DataFrame({\n", " 'A': [1, 2, None, 4],\n", " 'B': [5, None, 7, 8],\n", " 'C': [9, 10, 11, None]\n", " })\n", "\n", "imputer = KNNImputer(n_neighbors=2)\n", "data_imputed_knn = pd.DataFrame(imputer.fit_transform(data), columns=data.columns)\n", "print(data_imputed_knn)" ] }, { "cell_type": "code", "execution_count": 28, "id": "1ed34a60-1b2c-446a-9159-47b0e2dcef69", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PregnantGlucoseDiastolic_BPSkin_FoldSerum_InsulinBMIDiabetes_PedigreeAgeClass
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" ], "text/plain": [ " Pregnant Glucose Diastolic_BP Skin_Fold Serum_Insulin BMI \\\n", "0 6 148.0 72.0 35.0 NaN 33.6 \n", "1 1 85.0 66.0 29.0 NaN 26.6 \n", "2 8 183.0 64.0 NaN NaN 23.3 \n", "3 1 89.0 66.0 23.0 94.0 28.1 \n", "4 0 137.0 40.0 35.0 168.0 43.1 \n", "\n", " Diabetes_Pedigree Age Class \n", "0 0.627 50 1 \n", "1 0.351 31 0 \n", "2 0.672 32 1 \n", "3 0.167 21 0 \n", "4 2.288 33 1 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "df=pd.read_csv(\"Diabetes Missing Data (1).csv\")\n", "df.head()\n" ] }, { "cell_type": "code", "execution_count": 32, "id": "59cf4abb-8962-4478-a262-065e7a8ed823", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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84 rows × 18 columns

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" ], "text/plain": [ " Pregnant Glucose Diastolic_BP Skin_Fold Serum_Insulin BMI \\\n", "4 0 137.0 40.0 35.0 168.0 43.1 \n", "8 2 197.0 70.0 45.0 543.0 30.5 \n", "12 10 139.0 80.0 NaN NaN 27.1 \n", "13 1 189.0 60.0 23.0 846.0 30.1 \n", "18 1 103.0 30.0 38.0 83.0 43.3 \n", ".. ... ... ... ... ... ... \n", "691 13 158.0 114.0 NaN NaN 42.3 \n", "695 7 142.0 90.0 24.0 480.0 30.4 \n", "710 3 158.0 64.0 13.0 387.0 31.2 \n", "715 7 187.0 50.0 33.0 392.0 33.9 \n", "753 0 181.0 88.0 44.0 510.0 43.3 \n", "\n", " Diabetes_Pedigree Age Class Pregnant_Outlier Glucose_Outlier \\\n", "4 2.288 33 1 False False \n", "8 0.158 53 1 False False \n", "12 1.441 57 0 False False \n", "13 0.398 59 1 False False \n", "18 0.183 33 0 False False \n", ".. ... ... ... ... ... \n", "691 0.257 44 1 False False \n", "695 0.128 43 1 False False \n", "710 0.295 24 0 False False \n", "715 0.826 34 1 False False \n", "753 0.222 26 1 False False \n", "\n", " Diastolic_BP_Outlier Skin_Fold_Outlier Serum_Insulin_Outlier \\\n", "4 False False False \n", "8 False False True \n", "12 False False False \n", "13 False False True \n", "18 True False False \n", ".. ... ... ... \n", "691 True False False \n", "695 False False True \n", "710 False False True \n", "715 False False True \n", "753 False False True \n", "\n", " BMI_Outlier Diabetes_Pedigree_Outlier Age_Outlier Class_Outlier \n", "4 False True False False \n", "8 False False False False \n", "12 False True False False \n", "13 False False False False \n", "18 False False False False \n", ".. ... ... ... ... \n", "691 False False False False \n", "695 False False False False \n", "710 False False False False \n", "715 False False False False \n", "753 False False False False \n", "\n", "[84 rows x 18 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data=df.copy()\n", "def detect_outliers_iqr(column):\n", " Q1 = column.quantile(0.25)\n", " Q3 = column.quantile(0.75)\n", " IQR = Q3 - Q1\n", " lower_bound = Q1 - 1.5 * IQR\n", " upper_bound = Q3 + 1.5 * IQR\n", "\n", " return (column < lower_bound) | (column > upper_bound)\n", "\n", "for col in data.columns:\n", " data[f'{col}_Outlier'] = detect_outliers_iqr(data[col])\n", " 14\n", " 13\n", "\n", "outliers_only = data[data.filter(like='_Outlier').any(axis=1)]\n", "\n", "outliers_only\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "ddef379d-aa58-4f5e-a264-8f9cf08ec27c", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "df=pd.read_csv('Diabetes Missing Data (1).csv')\n", "plt.figure(figsize=(12,6))\n", "for i, col in enumerate(df.columns[:5]): \n", " plt.subplot(1, 5, i+1) \n", " sns.boxplot(x=df[col])\n", " plt.title(f\"Boxplot for {col}\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "id": "95e343cf-1c40-4590-a20d-cd0b0ae44b6d", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "data=df.copy()\n", "def detect_outliers_zscore(column, threshold=3):\n", " mean = np.mean(column)\n", " std_dev = np.std(column)\n", " z_scores = (column - mean) / std_dev\n", " return np.abs(z_scores) > threshold\n", "for col in data.columns:\n", " data[f'{col}_Outlier'] = detect_outliers_zscore(data[col])\n", " outliers_only = data[data.filter(like='_Outlier').any(axis=1)]\n", " outliers_only" ] }, { "cell_type": "code", "execution_count": 22, "id": "0845e7b0-e4cc-434b-9b78-59bf8196ba5c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sum of null values in each column:\n", "Pregnant 0\n", "Glucose 5\n", "Diastolic_BP 35\n", "Skin_Fold 227\n", "Serum_Insulin 374\n", "BMI 11\n", "Diabetes_Pedigree 0\n", "Age 0\n", "Class 0\n", "Pregnant_Outlier 0\n", "Glucose_Outlier 0\n", "Diastolic_BP_Outlier 0\n", "Skin_Fold_Outlier 0\n", "Serum_Insulin_Outlier 0\n", "BMI_Outlier 0\n", "Diabetes_Pedigree_Outlier 0\n", "Age_Outlier 0\n", "Class_Outlier 0\n", "dtype: int64\n" ] } ], "source": [ " null_counts = data.isnull().sum()\n", " print(\"Sum of null values in each column:\")\n", " print(null_counts)" ] }, { "cell_type": "code", "execution_count": 23, "id": "5837f1cf-ec0a-415c-85ca-599ce8690ac1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pregnant 0\n", "Glucose 0\n", "Diastolic_BP 0\n", "Skin_Fold 0\n", "Serum_Insulin 0\n", "BMI 0\n", "Diabetes_Pedigree 0\n", "Age 0\n", "Class 0\n", "Pregnant_Outlier 0\n", "Glucose_Outlier 0\n", "Diastolic_BP_Outlier 0\n", "Skin_Fold_Outlier 0\n", "Serum_Insulin_Outlier 0\n", "BMI_Outlier 0\n", "Diabetes_Pedigree_Outlier 0\n", "Age_Outlier 0\n", "Class_Outlier 0\n", "dtype: int64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ " from sklearn.impute import KNNImputer\n", " imputer = KNNImputer(n_neighbors=2)\n", " data_imputed_knn = pd.DataFrame(imputer.fit_transform(data), columns=data.columns)\n", " data_imputed_knn.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 59, "id": "792e66b0-812c-4f47-82bf-fac8c73f9e29", "metadata": {}, "outputs": [], "source": [ "df=pd.read_csv(\"Diabetes Missing Data (1).csv\")" ] }, { "cell_type": "code", "execution_count": 29, "id": "64c82a9b-2145-4b4f-8367-ef1e8610c392", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original Data:\n", "768\n", "\n", "Cleaned Data (without outliers):\n", "684\n" ] } ], "source": [ "data=df.copy()\n", "def remove_outliers_iqr(data):\n", " Q1 = data.quantile(0.25)\n", " Q3 = data.quantile(0.75)\n", " IQR = Q3 - Q1\n", " lower_bound = Q1 - 1.5 * IQR\n", " upper_bound = Q3 + 1.5 * IQR\n", " data_cleaned = data[~((data < lower_bound) | (data > upper_bound)).any(axis=1)]\n", " return data_cleaned\n", "cleaned_data = remove_outliers_iqr(data)\n", "print(\"Original Data:\")\n", "print(len(data))\n", "print(\"\\nCleaned Data (without outliers):\")\n", "print(len(cleaned_data))" ] }, { "cell_type": "code", "execution_count": 57, "id": "924dc4f7-ef1e-4fe3-93ff-8fb00c9d0b47", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original Data:\n", "768\n", "\n", "Cleaned Data (without outliers):\n", "731\n" ] } ], "source": [ "import numpy as np\n", "from sklearn.impute import KNNImputer\n", "data = df.copy() \n", " \n", "def remove_outliers_zscore(data, threshold=3):\n", " \n", " outliers = pd.DataFrame(False, index=df.index, columns=df.columns)\n", " \n", " \n", " for column in data.columns:\n", " if data[column].dtype in ['float64', 'int64']: # Only apply to numeric col\n", " mean = data[column].mean()\n", " std_dev = data[column].std()\n", " z_scores = (data[column] - mean) / std_dev\n", " \n", " \n", " outliers[column] = abs(z_scores) > threshold\n", " \n", " \n", " rows_to_keep = ~outliers.any(axis=1)\n", " \n", " \n", " return data[rows_to_keep]\n", "\n", "cleaned_data = remove_outliers_zscore(data, threshold=3)\n", "print(\"Original Data:\")\n", "print(len(data))\n", "print(\"\\nCleaned Data (without outliers):\")\n", "print(len(cleaned_data))" ] }, { "cell_type": "code", "execution_count": 63, "id": "dcd0d822-bcd7-4ba9-967e-6b4792372306", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from matplotlib import pyplot as plt \n", "import seaborn as sns\n" ] }, { "cell_type": "code", "execution_count": 64, "id": "41deacad-b155-4303-8a04-51593e7611e4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PregnantGlucoseDiastolic_BPSkin_FoldSerum_InsulinBMIDiabetes_PedigreeAgeClass
06148.072.035.0NaN33.60.627501
1185.066.029.0NaN26.60.351310
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" ], "text/plain": [ " Pregnant Glucose Diastolic_BP Skin_Fold Serum_Insulin BMI \\\n", "0 6 148.0 72.0 35.0 NaN 33.6 \n", "1 1 85.0 66.0 29.0 NaN 26.6 \n", "2 8 183.0 64.0 NaN NaN 23.3 \n", "3 1 89.0 66.0 23.0 94.0 28.1 \n", "4 0 137.0 40.0 35.0 168.0 43.1 \n", "\n", " Diabetes_Pedigree Age Class \n", "0 0.627 50 1 \n", "1 0.351 31 0 \n", "2 0.672 32 1 \n", "3 0.167 21 0 \n", "4 2.288 33 1 " ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('Diabetes Missing Data (1).csv')\n", "df.head()\n" ] }, { "cell_type": "code", "execution_count": 65, "id": "f191eb0a-e2cd-4bba-a2c5-efb56c7027fc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PregnantGlucoseDiastolic_BPSkin_FoldSerum_InsulinBMIDiabetes_PedigreeAgeClass
count768.000000763.000000733.000000541.000000394.000000757.000000768.000000768.000000768.000000
mean3.845052121.68676372.40518429.153420155.54822332.4574640.47187633.2408850.348958
std3.36957830.53564112.38215810.476982118.7758556.9249880.33132911.7602320.476951
min0.00000044.00000024.0000007.00000014.00000018.2000000.07800021.0000000.000000
25%1.00000099.00000064.00000022.00000076.25000027.5000000.24375024.0000000.000000
50%3.000000117.00000072.00000029.000000125.00000032.3000000.37250029.0000000.000000
75%6.000000141.00000080.00000036.000000190.00000036.6000000.62625041.0000001.000000
max17.000000199.000000122.00000099.000000846.00000067.1000002.42000081.0000001.000000
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" ], "text/plain": [ " Pregnant Glucose Diastolic_BP Skin_Fold Serum_Insulin \\\n", "count 768.000000 763.000000 733.000000 541.000000 394.000000 \n", "mean 3.845052 121.686763 72.405184 29.153420 155.548223 \n", "std 3.369578 30.535641 12.382158 10.476982 118.775855 \n", "min 0.000000 44.000000 24.000000 7.000000 14.000000 \n", "25% 1.000000 99.000000 64.000000 22.000000 76.250000 \n", "50% 3.000000 117.000000 72.000000 29.000000 125.000000 \n", "75% 6.000000 141.000000 80.000000 36.000000 190.000000 \n", "max 17.000000 199.000000 122.000000 99.000000 846.000000 \n", "\n", " BMI Diabetes_Pedigree Age Class \n", "count 757.000000 768.000000 768.000000 768.000000 \n", "mean 32.457464 0.471876 33.240885 0.348958 \n", "std 6.924988 0.331329 11.760232 0.476951 \n", "min 18.200000 0.078000 21.000000 0.000000 \n", "25% 27.500000 0.243750 24.000000 0.000000 \n", "50% 32.300000 0.372500 29.000000 0.000000 \n", "75% 36.600000 0.626250 41.000000 1.000000 \n", "max 67.100000 2.420000 81.000000 1.000000 " ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 66, "id": "a1cfc72a-2cfa-4ffc-a3b2-59f18079d011", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " plt.figure(figsize=(12, 10))\n", " sns.heatmap(df.corr(), annot=True, cmap='coolwarm')\n", " plt.title('Correlation Heatmap')\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 89, "id": "6163b0ae-cc36-4e77-a6a7-82fe0dc6919e", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from matplotlib import pyplot as plt \n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 96, "id": "41336d12-365b-4e7d-896b-e681fee3e5c7", "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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2Sz+SkpK0du1aPfDAA/Lz88v2+UxKSnK4pOSTTz5Rq1at5O/vb9+W77//vtPt2LZtW5UuXTofa583pmnmOj04OFg1a9bUjBkzNHPmTO3atSvb5RcrV67UtWvX1K9fP4dt4OPjo9atWzt8Hl09Ftzs5zk/x8/C2DZ52Yc5ceV406JFC124cEF//etf9cUXXzi9TGfZsmWKiIhQSEiIw/pn/Bu1cePGfKw1CorwCpf17t1bTZs21QsvvFDgf/gyBAcHO7wuUaKE/Pz85OPjk23c2ZcpKlas6HQs49dep06dkiSNGTNG3t7eDo8hQ4ZIUrYDlqvfqj537pzT2pCQEPv0gurZs6d8fHz097//XV999ZUef/zxHGv79Omj2bNna9CgQVq5cqW2b9+u2NhYlStXLteQmZdt9M9//lNjx47V0qVLFRERoeDgYPXo0UP/+9//cl2PmTNn6qmnntKdd96pzz77TNu2bVNsbKw6derktLcyZco4vLbZbJJkr83Ytjntf1f5+PioWbNmDo/q1avbt8vevXuzbZOAgACZppntc+Pss5zbeMbnOS0tTR07dtTnn3+u5557TmvXrtX27dvtgcyVfdezZ89sfU6fPl2madp/hbp48WJFRUXpvffeU8uWLRUcHKx+/frp5MmTLm+vzLLuIyl9P7n6PzRBQUHZtn2zZs3s4T7DuXPndO3aNb3xxhvZ1rFLly6Sbnw+P//8cz388MOqXLmyPvroI23dulWxsbEaOHCg0+PHrbqDwrFjx2Sz2bJ9FjIYhqG1a9cqMjJSr776qpo2bapy5cppxIgRunjxoqQb+7p58+bZtsPixYsdPo+uHgtu9vOcn+NnfraNdOO4mdd96Iyrx5u+fftq7ty5OnbsmB566CGVL19ed955p1avXm2vOXXqlL766qts63/bbbcVyvojf7jmFS4zDEPTp09Xhw4d9O6772abnhE4s37BqTBCXE6c/cN78uRJ+z+sGdd2jR8/Ptv1shnCw8MdXrt6Z4EyZcrot99+yzb+66+/Oiy7IPz8/NS7d29NmzZNgYGBOa5DfHy8li1bpkmTJmncuHH28Yzrx3KTl21UsmRJxcTEKCYmRqdOnbKftenevbt+/PHHHJfx0UcfqU2bNnr77bcdxjP+Yc6rjP2b0/4vDGXLlpWvr6/mzp2b4/TCsG/fPu3Zs0fz589XVFSUfTzrl7py6+GNN97I8dvrFSpUsNfOmjVLs2bN0vHjx/Xll19q3LhxOn36tFasWFEIa1I0SpcureLFi6tv374aOnSo05qM/+H46KOPVL16dS1evNjh5zinL10W9C4irjhx4oR27Nih1q1b53i9q5T+W4P3339fknTw4EH93//9n6Kjo3X16lXNmTPHvq8//fRT+1l5Z/JyLLjZz3N+jp95ceXKFa1Zs0Y1a9ZUlSpVJOV9HzqTl+PNgAEDNGDAAF2+fFmbNm3SpEmT1K1bNx08eFDVqlVT2bJl1bBhQ4e7vmSWEbpxaxFekSft27dXhw4dNHnyZIWGhjpMq1Chgnx8fLR3716HcWff9C0sH3/8sUaPHm0/yB07dkxbtmyxX9QfHh6u2rVra8+ePZo6dWqhLrtdu3aaNm2adu7c6XC/xow/IhAREVEoy3nqqad06tQptW7dOtsZ6QyGYcg0TfsZygzvvfee0y/AZJbfbVShQgX1799fe/bs0axZs5SYmCg/P78c+8va2969e7V169ZsnyNXhIeHq1KlSjnu/8L4B6Vbt26aOnWqypQpYw9HRSGj96zb55133rnpvK1atVKpUqX0ww8/aNiwYS4vs2rVqho2bJjWrl2rb7/9Nm8N32J+fn6KiIjQrl271LBhQ/uZa2cMw1CJEiUcQs/JkyfzdAzKepa/IK5cuaJBgwbp2rVreu6551yer06dOpowYYI+++wz7dy5U1L67aW8vLx0+PDhXC9tyu+xwNnPc1EeP1NTUzVs2DCdO3dO06ZNc+jf1X2Y05n+/BxvSpYsqc6dO+vq1avq0aOHvv/+e1WrVk3dunXT8uXLVbNmzVwvMSnMzw1ujvCKPJs+fbruuOMOnT592v6rEyn9gPHYY49p7ty5qlmzpho1aqTt27dr4cKFRdbL6dOn9cADD+iJJ55QfHy8Jk2aJB8fH40fP95e884776hz586KjIxU//79VblyZZ0/f1779+/Xzp079cknn+Rr2aNGjdIHH3ygrl27avLkyapWrZq+/vprvfXWW3rqqadUp06dQlnHxo0b3/RG3IGBgbr33ns1Y8YMlS1bVmFhYdq4caPef/99lSpV6qbLcHUb3XnnnerWrZsaNmyo0qVLa//+/frwww/VsmXLHIOrlB4EX3rpJU2aNEmtW7fWgQMHNHnyZFWvXt3pN85vplixYnrppZc0aNAg+/6/cOGCoqOj83TZQG5Gjhypzz77TPfee69GjRqlhg0bKi0tTcePH9eqVav0zDPP6M477yzwcurWrauaNWtq3LhxMk1TwcHB+uqrrxx+dZkTf39/vfHGG4qKitL58+fVs2dPlS9fXmfOnNGePXt05swZvf3224qPj1dERIT69OmjunXrKiAgQLGxsVqxYkWOZ9Q8yT/+8Q/dfffduueee/TUU08pLCxMFy9e1KFDh/TVV1/Zry/v1q2bPv/8cw0ZMkQ9e/bUzz//rJdeekmVKlW66aUtGQICAlStWjV98cUXateunYKDg+0/U7k5fvy4tm3bprS0NMXHx2vXrl32X0m//vrr6tixY47z7t27V8OGDVOvXr1Uu3ZtlShRQuvWrdPevXvtZ0/DwsI0efJkvfDCC/rpp5/UqVMnlS5dWqdOndL27dvtZ1Hzcixw5ee5MI6fp06dst827uLFi9q3b58++OAD7dmzR6NGjdITTzxhr83LPmzQoIE2bNigr776SpUqVVJAQIDCw8NdPt488cQT8vX1VatWrVSpUiWdPHlS06ZNU1BQkJo3by5Jmjx5slavXq277rpLI0aMUHh4uJKSknT06FEtX75cc+bMUZUqVfL9uUE+ue2rYvB4me82kFWfPn1MSQ53GzBN04yPjzcHDRpkVqhQwSxZsqTZvXt38+jRoznebeDMmTMO80dFRZklS5bMtrysdzbI+Lbuhx9+aI4YMcIsV66cabPZzHvuuceMi4vLNv+ePXvMhx9+2Cxfvrzp7e1tVqxY0Wzbtq05Z84cl9Y3J8eOHTP79OljlilTxvT29jbDw8PNGTNmZPsGbn7vNpATZ3cb+OWXX8yHHnrILF26tBkQEGB26tTJ3Ldvn1mtWjWHb+Q6u9uAabq2jcaNG2c2a9bMLF26tGmz2cwaNWqYo0aNMs+ePZtrv8nJyeaYMWPMypUrmz4+PmbTpk3NpUuXmlFRUQ53Bsjtm9tZP0OmaZrvvfeeWbt2bbNEiRJmnTp1zLlz52Z7z5xk/Uw5c+nSJXPChAlmeHi4WaJECTMoKMhs0KCBOWrUKPPkyZMOvWX9RnVO65Kx/T/55BP72A8//GB26NDBDAgIMEuXLm326tXLPH78eLZ1dvbNf9M0zY0bN5pdu3Y1g4ODTW9vb7Ny5cpm165d7ctISkoy//a3v5kNGzY0AwMDTV9fXzM8PNycNGmSefny5Vy3QU53G3C27Vzd9rl9xmNjY53eteTIkSPmwIEDzcqVK5ve3t5muXLlzLvuust8+eWXHepeeeUVMywszLTZbGa9evXMf/3rX/bjTWbO9lmGNWvWmE2aNDFtNpspKddvtGfs54xH8eLFzdKlS5t33HGHOXLkSPu30TPL+jN46tQps3///mbdunXNkiVLmv7+/mbDhg3Nv//97w533TDN9LtYREREmIGBgabNZjOrVatm9uzZ01yzZo29xtVjgas/z64cG3KSedsUK1bMDAwMNBs0aGA++eST5tatW53O4+o+3L17t9mqVSvTz8/PlGS2bt3aNE3XjzcLFiwwIyIizAoVKpglSpQwQ0JCzIcffjjbnUDOnDljjhgxwqxevbrp7e1tBgcHm3fccYf5wgsvONxhJC+fGxSMYZo3+RokAAAA4CG42wAAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAy/jD/5GCtLQ0/frrrwoICLglfwoQAAAAeWNe/yMWISEhKlYs93Orf/jw+uuvv+brz08CAADg1vr5559VpUqVXGv+8OE1ICBAUvrGCAwMdHM3AAAAyCohIUGhoaH23JabP3x4zbhUIDAwkPAKAADgwVy5xJMvbAEAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALONPE16DgiTDyDK4MS79AQAAAEv404RXAAAAWB/hFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWIZbw+umTZvUvXt3hYSEyDAMLV26NFvN/v37dd999ykoKEgBAQH6y1/+ouPHj9/6ZgEAAOB2bg2vly9fVqNGjTR79myn0w8fPqy7775bdevW1YYNG7Rnzx69+OKL8vHxucWdAgAAwBN4uXPhnTt3VufOnXOc/sILL6hLly569dVX7WM1atS4Fa0BAADAA7k1vOYmLS1NX3/9tZ577jlFRkZq165dql69usaPH68ePXrkOF9ycrKSk5PtrxMSEiRJPj4pMowUpaRkKjbT0p8dBgEAAHArpeQhi3lseD19+rQuXbqkV155RS+//LKmT5+uFStW6MEHH9T69evVunVrp/NNmzZNMTEx2cbnzl0lPz8/LV/uZKblvxVy9wAAAHBVYmKiy7WGaZpmEfbiMsMwtGTJEvtZ1V9//VWVK1fWX//6Vy1cuNBed99996lkyZL6+OOPnb6PszOvoaGh8vE5K8MIVHx8puJvd6U/t2pS2KsDAAAAFyUkJKhs2bKKj49XYGBgrrUee+a1bNmy8vLyUv369R3G69Wrp2+++SbH+Ww2m2w2W7bxpCRvSd7y9s40aFz/vprDIAAAAG4l7zxkMY+9z2uJEiXUvHlzHThwwGH84MGDqlatmpu6AgAAgDu59czrpUuXdOjQIfvrI0eOaPfu3QoODlbVqlX17LPP6pFHHtG9996riIgIrVixQl999ZU2bNjgvqYBAADgNm695nXDhg2KiIjINh4VFaX58+dLkubOnatp06bpl19+UXh4uGJiYnT//fe7vIyEhAQFBQVJipcUKIe13RiX/ty6Wb7XAQAAAAWTkddcuebVY76wVVQIrwAAAJ4tL+HVY695BQAAALIivAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALMOt4XXTpk3q3r27QkJCZBiGli5dmmPt4MGDZRiGZs2adcv6AwAAgGdxa3i9fPmyGjVqpNmzZ+dat3TpUv33v/9VSEjILeoMAAAAnsjLnQvv3LmzOnfunGvNiRMnNGzYMK1cuVJdu3a9RZ0BAADAE7k1vN5MWlqa+vbtq2effVa33XabS/MkJycrOTnZ/johIUGS5OOTIsNIUUpKpmIzLf3ZYRAAAAC3UkoesphHh9fp06fLy8tLI0aMcHmeadOmKSYmJtv43Lmr5Ofnp+XLncy0/LcCdAkAAICCSExMdLnWY8Prjh079I9//EM7d+6UYRguzzd+/HiNHj3a/johIUGhoaEaOLCjDCNQ8fGZir/dlf7cqokkKSjoxiR7XZaazHW51QAAAMA1Gb8pd4XHhtfNmzfr9OnTqlq1qn0sNTVVzzzzjGbNmqWjR486nc9ms8lms2UbT0ryluQtb+9Mg8b176tdH7xy5cYke12Wmsx1udUAAADANd55yFAeG1779u2r9u3bO4xFRkaqb9++GjBggJu6AgAAgDu5NbxeunRJhw4dsr8+cuSIdu/ereDgYFWtWlVlypRxqPf29lbFihUVHh5+q1sFAACAB3BreI2Li1NERIT9dca1qlFRUZo/f76bugIAAICncmt4bdOmjUzTdLk+p+tcAQAA8Ofg1r+wBQAAAOQF4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACW4eXuBv6oDCP92TSvD2yMuzGxdTPnNZnrcqsBAAD4k+LMKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACzDreF106ZN6t69u0JCQmQYhpYuXWqflpKSorFjx6pBgwYqWbKkQkJC1K9fP/3666/uaxgAAABu5dbwevnyZTVq1EizZ8/ONi0xMVE7d+7Uiy++qJ07d+rzzz/XwYMHdd9997mhUwAAAHgCL3cuvHPnzurcubPTaUFBQVq9erXD2BtvvKEWLVro+PHjqlq16q1oEQAAAB7EreE1r+Lj42UYhkqVKpVjTXJyspKTk+2vExISJEk+PikyjBSlpGQqNtPSn68P+vremGSvy1KTuS5fNU6W50pPDjUAAAB/ICl5CDqGaZpmEfbiMsMwtGTJEvXo0cPp9KSkJN19992qW7euPvrooxzfJzo6WjExMdnGFy5cKD8/v8JqFwAAAIUkMTFRffr0UXx8vAIDA3OttUR4TUlJUa9evXT8+HFt2LAh15VyduY1NDRUPj5nZRiBio/PVPztrvTnVk0kSUFBNybZ67LUZK7LV42T5bnSU577BgAAsIiEhASVLVvWpfDq8ZcNpKSk6OGHH9aRI0e0bt26m66QzWaTzWbLNp6U5C3JW97emQaN699Xuz545cqNSfa6LDWZ6/JV42R5rvSU574BAAAswjsPAcajw2tGcP3f//6n9evXq0yZMu5uCQAAAG7k1vB66dIlHTp0yP76yJEj2r17t4KDgxUSEqKePXtq586dWrZsmVJTU3Xy5ElJUnBwsEqUKOGutgEAAOAmbg2vcXFxioiIsL8ePXq0JCkqKkrR0dH68ssvJUmNGzd2mG/9+vVq06bNrWoTAAAAHsKt4bVNmzbK7ftiHvJdMgAAAHgIt/6FLQAAACAvCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDC93N4AisjEu/bl1M0mSYdyYZJrOazLXZatx8l55qgEAACgEnHkFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZbg1vG7atEndu3dXSEiIDMPQ0qVLHaabpqno6GiFhITI19dXbdq00ffff++eZgEAAOB2bg2vly9fVqNGjTR79myn01999VXNnDlTs2fPVmxsrCpWrKgOHTro4sWLt7hTAAAAeAIvdy68c+fO6ty5s9Nppmlq1qxZeuGFF/Tggw9KkhYsWKAKFSpo4cKFGjx48K1sFQAAAB7AreE1N0eOHNHJkyfVsWNH+5jNZlPr1q21ZcuWHMNrcnKykpOT7a8TEhIkST4+KTKMFKWkZCo209Kfrw/6+t6YZK/LUpO5Ll81TpbnSk8F7bvA65ZbT66uGwAAgBMpeQgMhmmaZhH24jLDMLRkyRL16NFDkrRlyxa1atVKJ06cUEhIiL3uySef1LFjx7Ry5Uqn7xMdHa2YmJhs4wsXLpSfn1+R9A4AAID8S0xMVJ8+fRQfH6/AwMBcaz32zGsGwzAcXpummW0ss/Hjx2v06NH21wkJCQoNDdXAgR1lGIGKj89U/O2u9OdWTSRJQUE3JtnrstRkrstXjZPludJTQfsu8Lrl1lMB1y1bHQAA+FPJ+E25Kzw2vFasWFGSdPLkSVWqVMk+fvr0aVWoUCHH+Ww2m2w2W7bxpCRvSd7y9s40aFz/vtr1wStXbkyy12WpyVyXrxony3Olp4L2XeB1y62nAq5btjoAAPCn4p2HIOCx93mtXr26KlasqNWrV9vHrl69qo0bN+quu+5yY2cAAABwF7eeeb106ZIOHTpkf33kyBHt3r1bwcHBqlq1qkaOHKmpU6eqdu3aql27tqZOnSo/Pz/16dPHjV0DAADAXfIVXmvUqKHY2FiVKVPGYfzChQtq2rSpfvrpJ5feJy4uThEREfbXGdeqRkVFaf78+Xruued05coVDRkyRL///rvuvPNOrVq1SgEBAflpGwAAABaXr/B69OhRpaamZhtPTk7WiRMnXH6fNm3aKLebHRiGoejoaEVHR+enTQAAAPzB5Cm8fvnll/b/XrlypYIyfV08NTVVa9euVVhYWKE1BwAAAGSWp/CacQ9WwzAUFRXlMM3b21thYWF6/fXXC605AAAAILM8hde0tPS/pFS9enXFxsaqbNmyRdIUAAAA4Ey+rnk9cuRIYfcBAAAA3FS+b5W1du1arV27VqdPn7afkc0wd+7cAjcGAAAAZJWv8BoTE6PJkyerWbNmqlSpUq5/rhUAAAAoLPkKr3PmzNH8+fPVt2/fwu4HAAAAyFG+/jzs1atX+ROtAAAAuOXyFV4HDRqkhQsXFnYvAAAAQK7yddlAUlKS3n33Xa1Zs0YNGzaUt7e3w/SZM2cWSnMAAABAZvkKr3v37lXjxo0lSfv27XOYxpe3AAAAUFTyFV7Xr19f2H0AAAAAN5Wva14BAAAAd8jXmdeIiIhcLw9Yt25dvhsCAAAAcpKv8JpxvWuGlJQU7d69W/v27VNUVFRh9AUAAABkk6/w+ve//93peHR0tC5dulSghvAntzEu/bl1M0lS5hP8pum8JnNdvmqcLM9ek8N7AQAA9yjUa14fe+wxzZ07tzDfEgAAALAr1PC6detW+fj4FOZbAgAAAHb5umzgwQcfdHhtmqZ+++03xcXF6cUXXyyUxgAAAICs8hVeg4KCHF4XK1ZM4eHhmjx5sjp27FgojQEAAABZ5Su8zps3r7D7AAAAAG4qX+E1w44dO7R//34ZhqH69eurSZMmhdUXAAAAkE2+wuvp06fVu3dvbdiwQaVKlZJpmoqPj1dERIQWLVqkcuXKFXafAAAAQP7uNjB8+HAlJCTo+++/1/nz5/X7779r3759SkhI0IgRIwq7RwAAAEBSPs+8rlixQmvWrFG9evXsY/Xr19ebb77JF7YAAABQZPJ15jUtLU3e3t7Zxr29vZWWllbgpgAAAABn8hVe27Ztq6efflq//vqrfezEiRMaNWqU2rVrV2jNAQAAAJnlK7zOnj1bFy9eVFhYmGrWrKlatWqpevXqunjxot54443C7hEAAACQlM9rXkNDQ7Vz506tXr1aP/74o0zTVP369dW+ffvC7g8AAACwy9OZ13Xr1ql+/fpKSEiQJHXo0EHDhw/XiBEj1Lx5c912223avHlzkTQKAAAA5Cm8zpo1S0888YQCAwOzTQsKCtLgwYM1c+bMQmsOAAAAyCxP4XXPnj3q1KlTjtM7duyoHTt2FLgpAAAAwJk8hddTp045vUVWBi8vL505c6bATWW4du2aJkyYoOrVq8vX11c1atTQ5MmTuR0XAADAn1SevrBVuXJlfffdd6pVq5bT6Xv37lWlSpUKpTFJmj59uubMmaMFCxbotttuU1xcnAYMGKCgoCA9/fTThbYcAAAAWEOezrx26dJFEydOVFJSUrZpV65c0aRJk9StW7dCa27r1q26//771bVrV4WFhalnz57q2LGj4uLiCm0ZAAAAsI48nXmdMGGCPv/8c9WpU0fDhg1TeHi4DMPQ/v379eabbyo1NVUvvPBCoTV39913a86cOTp48KDq1KmjPXv26JtvvtGsWbNynCc5OVnJycn21xl3RvDxSZFhpCglJVOxef3yg+uDvr43JtnrstRkrstXjZPludJTQfsu8Lrl1lMB161I+y6CfQIAAApXSh7+jTVM0zTz8ubHjh3TU089pZUrVypjVsMwFBkZqbfeekthYWF5ajY3pmnq+eef1/Tp01W8eHGlpqZqypQpGj9+fI7zREdHKyYmJtv4woUL5efnV2i9AQAAoHAkJiaqT58+io+Pd3pXq8zyHF4z/P777zp06JBM01Tt2rVVunTpfDWbm0WLFunZZ5/VjBkzdNttt2n37t0aOXKkZs6cqaioKKfzODvzGhoaKh+fszKMQMXHZyr+dlf6c6smkqSgoBuT7HVZajLX5avGyfJc6amgfRd43XLrqYDrVqR9e/A+yfG9AAD4k0lISFDZsmVdCq/5+gtbklS6dGk1b948v7O75Nlnn9W4cePUu3dvSVKDBg107NgxTZs2LcfwarPZZLPZso0nJXlL8pbDzRKM65f8Xh+8cuXGJHtdlprMdfmqcbI8V3oqaN8FXrfceirguhVp3x68T3J8LwAA/mRyu5tVVnn6wtatlpiYqGLFHFssXrw4t8oCAAD4k8r3mddboXv37poyZYqqVq2q2267Tbt27dLMmTM1cOBAd7cGAAAAN/Do8PrGG2/oxRdf1JAhQ3T69GmFhIRo8ODBmjhxortbAwAAgBt4dHgNCAjQrFmzcr01FgAAAP48PPqaVwAAACAzwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAy/BydwMAMtkYl/7cupl9yDDSn00zS02munzVOFleRk22OgAAPARnXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGV4fHg9ceKEHnvsMZUpU0Z+fn5q3LixduzY4e62AAAA4AZe7m4gN7///rtatWqliIgI/ec//1H58uV1+PBhlSpVyt2tAQAAwA08OrxOnz5doaGhmjdvnn0sLCzMfQ0BAADArTw6vH755ZeKjIxUr169tHHjRlWuXFlDhgzRE088keM8ycnJSk5Otr9OSEiQJPn4pMgwUpSSkqnYTEt/vj7o63tjkr0uS03munzVOFmeKz0VtO8Cr1tuPRVw3Yq0bw/eJ0Xad2HuEwAAilhKHv7RMUzTNIuwlwLx8fGRJI0ePVq9evXS9u3bNXLkSL3zzjvq16+f03mio6MVExOTbXzhwoXy8/Mr0n4BAACQd4mJierTp4/i4+MVGBiYa61Hh9cSJUqoWbNm2rJli31sxIgRio2N1datW53O4+zMa2hoqHx8zsowAhUfn6n4213pz62aSJKCgm5Mstdlqclcl68aJ8tzpaeC9l3gdcutpwKuW5H27cH7pEj79uR9UoR9F8k+AQAUuYSEBJUtW9al8OrRlw1UqlRJ9evXdxirV6+ePvvssxznsdlsstls2caTkrwlecvbO9Ogcf1mC9cHr1y5Mclel6Umc12+apwsz5WeCtp3gdctt54KuG5F2rcH75Mi7duT90kR9l0k+wQAUOS883DQ9ehbZbVq1UoHDhxwGDt48KCqVavmpo4AAADgTh4dXkeNGqVt27Zp6tSpOnTokBYuXKh3331XQ4cOdXdrAAAAcAOPDq/NmzfXkiVL9PHHH+v222/XSy+9pFmzZunRRx91d2sAAABwA4++5lWSunXrpm7durm7DQAAAHgAjz7zCgAAAGRGeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGV7ubgAAPNrGuPTn1s3sQ4aR/myaWWoy1WWrcfJeealxurw81BSobwDwIJx5BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGVYKrxOmzZNhmFo5MiR7m4FAAAAbmCZ8BobG6t3331XDRs2dHcrAAAAcBMvdzfgikuXLunRRx/Vv/71L7388su51iYnJys5Odn+OiEhQZLk45Miw0hRSkqmYjMt/fn6oK/vjUn2uiw1mevyVeNkea70VNC+C7xuufVUwHUr0r49eJ8Uad+evE+KsG+P3idF2HeR7hMAuAVS8nDQMUzTNIuwl0IRFRWl4OBg/f3vf1ebNm3UuHFjzZo1y2ltdHS0YmJiso0vXLhQfn5+RdwpAAAA8ioxMVF9+vRRfHy8AgMDc631+DOvixYt0s6dOxUbG+tS/fjx4zV69Gj764SEBIWGhmrgwI4yjEDFx2cq/nZX+nOrJpKkoKAbk+x1WWoy1+WrxsnyXOmpoH0XeN1y66mA61akfXvwPinSvj15nxRh3x69T4qwb0/eJ0Xadx7XDYDnyvhNuSs8Orz+/PPPevrpp7Vq1Sr5+Pi4NI/NZpPNZss2npTkLclb3t6ZBo3rl/xeH7xy5cYke12Wmsx1+apxsjxXeipo3wVet9x6KuC6FWnfHrxPirRvT94nRdi3R++TIuzbk/dJkfadx3UD4Lm88/Bz6tHhdceOHTp9+rTuuOMO+1hqaqo2bdqk2bNnKzk5WcWLF3djhwAAALiVPDq8tmvXTt99953D2IABA1S3bl2NHTuW4AoAAPAn49HhNSAgQLfffrvDWMmSJVWmTJls4wAAAPjjs8x9XgEAAACPPvPqzIYNG9zdAgAAANyEM68AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALMPL3Q0AAHArGEb6s2lmGtwYl/7culmeaxzqstQ4fa+81OTWkys1+ezb1XUD3IkzrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMjw6v06ZNU/PmzRUQEKDy5curR48eOnDggLvbAgAAgJt4dHjduHGjhg4dqm3btmn16tW6du2aOnbsqMuXL7u7NQAAALiBl7sbyM2KFSscXs+bN0/ly5fXjh07dO+997qpKwAAALiLR4fXrOLj4yVJwcHBOdYkJycrOTnZ/johIUGS5OOTIsNIUUpKpmIzLf35+qCv741J9rosNZnr8lXjZHmu9FTQvgu8brn1VMB1K9K+PXifFGnfnrxPirBvj94nRdi3J++TIu3bg/dJkfbtAfsEKGwpefh8GaZpmkXYS6ExTVP333+/fv/9d23evDnHuujoaMXExGQbX7hwofz8/IqyRQAAAORDYmKi+vTpo/j4eAUGBuZaa5nwOnToUH399df65ptvVKVKlRzrnJ15DQ0NlY/PWRlGoK6fvE337a7051ZNJElBQTcm2euy1GSuy1eNk+W50lNB+y7wuuXWUwHXrUj79uB9UqR9e/I+KcK+PXqfFGHfnrxPirRvD94nRdq3J++TIuzbk/YJCl9CQoLKli3rUni1xGUDw4cP15dffqlNmzblGlwlyWazyWazZRtPSvKW5C1v70yDxvXvq10fvHLlxiR7XZaazHX5qnGyPFd6KmjfBV633Hoq4LoVad8evE+KtG9P3idF2LdH75Mi7NuT90mR9u3B+6RI+/bkfVKEfXvSPkHh887DtvXo8GqapoYPH64lS5Zow4YNql69urtbAgAAgBt5dHgdOnSoFi5cqC+++EIBAQE6efKkJCkoKEi+ma8qBwAAwJ+CR9/n9e2331Z8fLzatGmjSpUq2R+LFy92d2sAAABwA48+82qR75IBAADgFvHoM68AAABAZoRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWIaXuxsAAACwEsNIfzbNTIMb49KfWzfLc41DXT5qnC4voya3nlypuUV9x8fLZZx5BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGVYIry+9dZbql69unx8fHTHHXdo8+bN7m4JAAAAbuDx4XXx4sUaOXKkXnjhBe3atUv33HOPOnfurOPHj7u7NQAAANxiHh9eZ86cqccff1yDBg1SvXr1NGvWLIWGhurtt992d2sAAAC4xbzc3UBurl69qh07dmjcuHEO4x07dtSWLVuczpOcnKzk5GT76/j4eEmSzXZehpGic+cyFV9KSH++Pujjc2OSvS5LTea6fNU4WZ4rPRW07wKvW249FXDdirRvD94nRdq3J++TIuzbo/dJEfbtyfukSPv24H1SpH178j4pwr49ep8UYd+evE8Ks+/z5y9KkkzT1E2ZHuzEiROmJPPbb791GJ8yZYpZp04dp/NMmjTJlMSDBw8ePHjw4MHDYo+ff/75pvnQo8+8ZjAMw+G1aZrZxjKMHz9eo0ePtr9OS0vT+fPn5e3trapVq+rnn39WYGCgJCkhIUGhoaEOYwAAALi1TNPUxYsXFRISctNajw6vZcuWVfHixXXy5EmH8dOnT6tChQpO57HZbLLZbA5jpUqVUkJC+qnqwMDAbEHV2RgAAABunaCgIJfqPPoLWyVKlNAdd9yh1atXO4yvXr1ad911l5u6AgAAgLt49JlXSRo9erT69u2rZs2aqWXLlnr33Xd1/Phx/e1vf3N3awAAALjFPD68PvLIIzp37pwmT56s3377TbfffruWL1+uatWq5el9bDabJk2a5HBJgbMxAAAAeC7DNF25JwEAAADgfh59zSsAAACQGeEVAAAAlkF4BQAAgGUQXgEAAGAZf/jwevToURmGod27d9vHwsLCNGvWrDy/V9++fTV16lT76/fff18dO3bMsT45OVlVq1bVjh077GM9e/bUzJkz87xsAAAAFHF47d+/vwzDyPY4dOjQTedxdh/XIUOGyDAM9e/fvwi7dm7v3r36+uuvNXz4cEnpwfTZZ5/VhQsXVLZsWXtA/uyzz1S/fn3ZbDY1adJEHTt21NixY+3vM3HiRE2ZMsX+F78AAADguiI/89qpUyf99ttvDo/q1avnOk9oaKgWLVqkK1eu2MeSkpL08ccfq2rVqkXdslOzZ89Wr169FBAQIEn67LPPZLPZ1K1bN73yyiuSpD179uiRRx5R3759tWfPHvXt21fz58/Xpk2btH//fklSw4YNFRYWpn//+99uWQ8AAAArK/LwarPZVLFiRYdH8eLFJUlfffWV7rjjDvn4+KhGjRqKiYlRWlqamjZtqqpVq8rPz0/vvPOOunXrpsDAQF25ckXVqlVTQkKC2rRpo5IlS6pevXpq1qyZSpUqpTJlyqht27Zq3769KlSoIH9/f913333Zerp69areffdd+fv7q0KFCnrkkUfUr18/lS9fXoGBgWrbtq327Nljr09LS9Mnn3zi8F6LFi3SY489pokTJ6p9+/aSpH//+9/q0KGDxo8fr7p162r8+PFq3769SpcurY8//tg+73333efwGgAAAK5x2zWvK1eu1GOPPaYRI0bohx9+0DvvvKP58+dr7969kqQBAwZIkl566SX169dPzZo1U+3atRUXF6etW7dq/PjxiouLU2pqqlJTUxUbG6u1a9cqLS1NP/74o1atWqVdu3bp3nvvlST99ttv9udTp06pcuXKiouL03/+8x+tWrVKy5cv1/Lly7Vjxw41bdpU7dq10/nz5yWlXzJw4cIFNWvWzN7/5s2bHV5n1GW9BjYyMlJXrlzR5s2b7WMtWrTQ9u3blZycXMhbFQAA4I+tyMPrsmXL5O/vb3/06tVLkjRlyhSNGzdOUVFRqlGjhjp06KCXXnpJBw8elJT+5ShJeuCBB3TnnXdq165dmjFjhq5cuaIaNWooMjJS9erV0+TJk/Xjjz+qdu3aaty4sT755BOdOHFCxYsXV+3atTVmzBhJ0saNGyVJb7/9tkqUKKGuXbuqbt26unDhgq5du6Zz584pMDBQtWvX1muvvaZSpUrp008/lZT+pa/ixYurfPnykqQLFy7owoULCgkJcVjXc+fOqUKFCg5jFSpUUGJioo4ePWofq1y5spKTk3Xy5MlC3toAAAB/bF5FvYCIiAi9/fbb9tclS5aUJO3YsUOxsbGaMmWKfVpqaqqSkpKUmpqqsmXLSpJOnTqlefPmqWvXrgoPD5cklS5d2j7PtWvXlJSUpLCwMJ0/f15paWmSpC5duighIUFXr16VdOPM644dO5SUlKSxY8dqwoQJSklJsdc0bNhQXl7pm+TKlSs6fPiw/b9tNpsMw7C/liQfH59s65tRk8E0TRmGocTERPuYr6+vJDmMAQAA4OaKPLyWLFlStWrVyjaelpammJgYPfjggw7jzz33nFJTU+2vN27cqNjYWL355pv2YFis2I0TxhMmTJAk/eMf/1CdOnUUExOjxYsXq0+fPnr00Ud17tw5RURE6Nq1a/bl+vn5aeTIkRowYIDeeecdffjhh/r3v/+tcuXKyc/Pz/7epUqVkiSVLVtWiYmJunr1qkqUKKEyZcrIMAz9/vvvDr2XKVMm29nU06dPy9/fX+XKlbOPZVyOkHkMAAAAN+e2a16bNm2qAwcOqFatWg6PwMBAh7OX165d09WrVxUZGZntPc6dO6djx45Jklq3bq169epp+/btkqS//OUvatCgQbaA2LRpU129elXBwcGqVauWOnbsqLNnz6pmzZpq2LChQy8ZZ38bN24sSfrhhx8kSSVKlFD9+vXtrzM0bNhQq1evdhhbtWqVgoKC1KRJE/vYvn37VKVKFfv7AwAAwDVuC68TJ07UBx98oOjoaH3//ffav3+/Fi9erF27djnUvfHGG9q/f7/9DgWZlS5dWoGBgZKkn376SevWrbOfDT1y5Ij27Nmjp59+2mGeoUOHKi0tTR9++KG2b9+uGjVqqG7dumratKmWL1+uo0ePasuWLZowYYLi4uIkpZ8hbdq0qb755hv7+0RGRmrdunXavXu3PcTefffdWrlypSZMmKAff/xR06dP15o1a5ScnOzwRa7Nmzfn+scNAAAA4JzbwmtkZKSWLVum1atXq3nz5vrLX/6imTNn2q+JzeDn52cPqFkVK1ZMEydOlCTdddddGjVqlP362nHjxql79+72uw1kCAkJUcWKFZWWlqbIyEg1aNBAKSkpqlatmgYNGqQ6deqod+/eOnr0qMOXr5588kmHe7M+8cQTWrFihZo0aaKuXbtKkiZNmqS0tDTNmTNHDRs21Pz58zV58mQlJSWpZ8+ektLvV7tkyRI98cQTBdyCAAAAfz6GaZqmu5uwgqSkJIWHh2vRokVq2bKlJOnhhx9WkyZNNH78+Bzn69Wrl5o0aaLnn39ekvTmm2/qiy++0KpVq25J3wAAAH8kbjvzajU+Pj764IMPdPbsWfvYjBkz5O/vn+M8ycnJatSokUaNGmUf8/b21htvvFGkvQIAAPxRceYVAAAAlsGZVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUA/oDatGmjkSNHursNACh0hFcAKCInT57U008/rVq1asnHx0cVKlTQ3XffrTlz5igxMdHd7QGAJXm5uwEA+CP66aef1KpVK5UqVUpTp05VgwYNdO3aNR08eFBz585VSEiI7rvvPne3maPU1FQZhqFixTjHAcCzcFQCgCIwZMgQeXl5KS4uTg8//LDq1aunBg0a6KGHHtLXX3+t7t27S5Li4+P15JNPqnz58goMDFTbtm21Z88e+/tER0ercePG+vDDDxUWFqagoCD17t1bFy9etNdcvnxZ/fr1k7+/vypVqqTXX389Wz9Xr17Vc889p8qVK6tkyZK68847tWHDBvv0+fPnq1SpUlq2bJnq168vm82mY8eOFd0GAoB8IrwCQCE7d+6cVq1apaFDh6pkyZJOawzDkGma6tq1q06ePKnly5drx44datq0qdq1a6fz58/baw8fPqylS5dq2bJlWrZsmTZu3KhXXnnFPv3ZZ5/V+vXrtWTJEq1atUobNmzQjh07HJY3YMAAffvtt1q0aJH27t2rXr16qVOnTvrf//5nr0lMTNS0adP03nvv6fvvv1f58uULecsAQMFx2QAAFLJDhw7JNE2Fh4c7jJctW1ZJSUmSpKFDhyoyMlLfffedTp8+LZvNJkl67bXXtHTpUn366ad68sknJUlpaWmaP3++AgICJEl9+/bV2rVrNWXKFF26dEnvv/++PvjgA3Xo0EGStGDBAlWpUsW+3MOHD+vjjz/WL7/8opCQEEnSmDFjtGLFCs2bN09Tp06VJKWkpOitt95So0aNinDrAEDBEF4BoIgYhuHwevv27UpLS9Ojjz6q5ORk7dixQ5cuXVKZMmUc6q5cuaLDhw/bX4eFhdmDqyRVqlRJp0+flpQeTK9evaqWLVvapwcHBzsE5507d8o0TdWpU8dhOcnJyQ7LLlGihBo2bFiANQaAokd4BYBCVqtWLRmGoR9//NFhvEaNGpIkX19fSelnVCtVquRw7WmGUqVK2f/b29vbYZphGEpLS5MkmaZ5037S0tJUvHhx7dixQ8WLF3eY5u/vb/9vX1/fbIEbADwN4RUAClmZMmXUoUMHzZ49W8OHD8/xutemTZvq5MmT8vLyUlhYWL6WVatWLXl7e2vbtm2qWrWqJOn333/XwYMH1bp1a0lSkyZNlJqaqtOnT+uee+7J13IAwFPwhS0AKAJvvfWWrl27pmbNmmnx4sXav3+/Dhw4oI8++kg//vijihcvrvbt26tly5bq0aOHVq5cqaNHj2rLli2aMGGC4uLiXFqOv7+/Hn/8cT377LNau3at9u3bp/79+zvc4qpOnTp69NFH1a9fP33++ec6cuSIYmNjNX36dC1fvryoNgEAFAnOvAJAEahZs6Z27dqlqVOnavz48frll19ks9lUv359jRkzRkOGDJFhGFq+fLleeOEFDRw4UGfOnFHFihV17733qkKFCi4va8aMGbp06ZLuu+8+BQQE6JlnnlF8fLxDzbx58/Tyyy/rmWee0YkTJ1SmTBm1bNlSXbp0KexVB4AiZZiuXDAFAAAAeAAuGwAAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWMb/A3L1TqL7sjbmAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 5))\n", "gender_counts.plot(kind='bar', color=['blue', 'pink'])\n", "plt.xticks([0, 1], ['Female (0)', 'Male (1)'], rotation=0)\n", "plt.title('Number of Males and Females in Heart Disease Dataset')\n", "plt.xlabel('Gender')\n", "plt.ylabel('Count')\n", "plt.grid(axis='y')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 1, "id": "469b73fa-a638-4415-adda-da96739ad1d7", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from matplotlib import pyplot as plt \n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 6, "id": "08021edf-d808-4bab-9bad-4b51cf494088", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of males: 165\n", "Number of females: 168\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\alaam\\AppData\\Local\\Temp\\ipykernel_7584\\1825749726.py:5: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", " print(\"Number of males:\", gender_counts[1])\n", "C:\\Users\\alaam\\AppData\\Local\\Temp\\ipykernel_7584\\1825749726.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", " print(\"Number of females:\", gender_counts[0])\n" ] } ], "source": [ "import seaborn as sns\n", "\n", "df = sns.load_dataset(\"penguins\")\n", "gender_counts = df['sex'].value_counts() \n", "print(\"Number of males:\", gender_counts[1]) \n", "print(\"Number of females:\", gender_counts[0]) " ] }, { "cell_type": "code", "execution_count": 27, "id": "a8c4996e-8cef-4301-b603-4ed8d63a1cd2", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "from matplotlib import pyplot as plt \n", "\n", "plt.figure(figsize=(8, 5))\n", "gender_counts.plot(kind='bar', color=['blue', 'pink'])\n", "plt.xticks([0, 1], ['Female (0)', 'Male (1)'], rotation=0)\n", "plt.title('Number of Males and Females in Diabetes Disease Dataset')\n", "plt.xlabel('Gender')\n", "plt.ylabel('Count')\n", "plt.grid(axis='y')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "id": "cc945492-39cb-40c9-92ca-7d1ac7884a62", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sex Female Male\n", "species \n", "Adelie 73 73\n", "Chinstrap 34 34\n", "Gentoo 58 61\n", "sex Female Male\n", "species \n", "Adelie 73 73\n", "Chinstrap 34 34\n", "Gentoo 58 61\n", "['species', 'island', 'bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g', 'sex']\n" ] } ], "source": [ "\n", "sex_class_counts = df.groupby(['species', 'sex']).size().unstack(fill_value=0)\n", "print(sex_class_counts)\n", "df.columns = df.columns.str.strip() \n", "print(sex_class_counts)\n", "print(df.columns.tolist())" ] }, { "cell_type": "code", "execution_count": 24, "id": "154a6382-fb3c-491c-82da-248352bb2cbc", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "sex_class_counts.plot(kind='bar', color=['lightblue', 'lightcoral'])\n", "plt.title('Number of Males and Females Based on Diabetes Disease Class')\n", "plt.xlabel('Diabetes Disease Class (0 = No, 1 = Yes)')\n", "plt.ylabel('Count')\n", "plt.xticks(rotation=0)\n", "plt.legend(title='Sex', labels=['Female (0)', 'Male (1)'])\n", "plt.grid(axis='y')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 35, "id": "b17e0de6-fad7-4ae6-a925-12c0d57c924a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "df = pd.DataFrame({'cp': [0, 1, 1, 2, 3, 0, 1, 2, 3, 0]})\n", "\n", "cp_counts = df['cp'].value_counts()\n", "\n", "plt.figure(figsize=(8, 5))\n", "plt.pie(cp_counts, labels=['Type 0', 'Type 1', 'Type 2', 'Type 3'], autopct='%1.1f%%')\n", "plt.title('Distribution of Chest Pain Types (cp) ')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 74, "id": "983924da-9a87-4ba4-96ea-6e4531f781cd", "metadata": {}, "outputs": [ { "data": { "image/png": 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QkyZN0oIFC+Tp6amHH37YYXtS7s/DtVq0aKEWLVooPT1du3bt0ltvvaURI0YoICBADz300HXHZhf0Mtuu/sdcmzZtdOedd+rtt99W8eLFtWfPHi1atOima73WlStXtH79elWtWvW6j/1Fixapb9++9vnSmc6cOaMSJUrkeAzXtmUeU+Y5HzNmTI5z+WvUqHHd2jO38dZbb+V4l5TMO4GUKVNG0dHRio6O1rFjx7Rq1So9//zzOn369C3dUWj16tVKT0/Xvffee91+N3qMlClTRqVLl86xFl9fX0l//QXp5MmT2rhxo/0qtqRsb90ZGhqqefPmSZJ+/vlnLVmyRBMmTFBKSormzJlzy4974LbizHkrQGG3detW4+LiYjp06GCSk5OzrE9JSTGrVq2yL+c0h7hVq1amVatW9uWHH37YeHp6mhMnTjj069y58w3naLdr186EhYVlW8+NREdHG0nmxx9/NMYYM3PmTCPJ7N+/36Hf4cOHjaurq3nttdduuM1+/foZHx+fLO3vvvuukWSWL1/u0J45L3zdunX2tvyao71lyxYjySxevPiG2zLGmC+//NJIMqtWrTKBgYHm4Ycfdlh/M+fh2vm32Tl37pyRZJ577rnr9rveHG1fX1/7HO1Mc+fONcWKFTMtW7Y0AQEBuX5sKIc52mlpaWbgwIFGkpk7d669PbvH47WfPzDGmC+++MJIcnjMZ87RbtiwYbZztAcNGmRvq1atmunUqdMN61+1apWRZNasWePQfuHCBVOiRAnz5JNP3nAb2enRo4cpW7bsdftcb4720aNHTUhIiPH39zenT5+2t+flMbJo0SIjyWzfvv264zLPxbZt2xzae/bsecN59cYYU69ePdO4cWNjzM097oHbHVe0gVsQHh6u2bNna+jQoWrYsKGefPJJ1alTR6mpqdq7d6/mzp2rO++8M8un+m9k/Pjx+uKLL9S6dWu9+OKLKlWqlD7++GOtXr1aU6dOlb+/f45j33zzTTVv3lwtWrTQk08+qUqVKunChQs6dOiQ/vOf/2jDhg2SpK5du9rv8Vu2bFkdPXpU0dHRCg0NVbVq1SRJdevWtW+zX79+cnNzU40aNVSpUiW99NJLGjdunH777Td16NBBJUuW1KlTp/Ttt9/Kx8fH4Q4n2enbt6/eeecd9evXT0eOHFHdunW1ZcsWTZ48WZ06dVK7du1u6pxlio+Pz/bbBStVqqRmzZppyJAhGjBggHbt2qWWLVvKx8dHcXFx2rJli+rWrWu/J7QkRUREqEKFCho6dKji4+Mdpo1kbjOv52HOnDnasGGDOnfurIoVKyopKcl+m8fcHHtwcLC6deumCRMmKCgoSIsWLdK6dev02muvZflrxCOPPKIxY8bom2++0QsvvCB3d/cbbj/TqVOntH37dhljdOHCBf3www/68MMP9d1332nkyJF67LHHrju+S5cuWrhwoWrWrKm77rpLu3fv1uuvv57jVfDTp0/rvvvu02OPPabz589r/Pjx8vT01JgxY+x93n33XXXs2FGRkZHq37+/ypcvr7Nnz+rAgQPas2ePPvvsM0myz6WfO3eufH195enpqcqVK6t06dJ666231K9fP509e1Y9e/ZUuXLl9Mcff+i7777TH3/8odmzZ+v8+fNq3bq1evfurZo1a8rX11c7d+5UTExMru+M88MPP9jnMJ8+fVqbN2/WggUL5OLiohUrVqhs2bI5js3NY+Shhx7Sxx9/rE6dOumZZ55RkyZN5Obmpt9//11ff/21unfvrvvuu09NmzZVyZIl9cQTT2j8+PFyc3PTxx9/rO+++85hn99//72eeuopPfDAA6pWrZrc3d21YcMGff/993r++ecl3drjHrjtODvpA0XBvn37TL9+/UzFihWNu7u78fHxMfXr1zcvvviiwxWr3F7RNsaY2NhY07VrV+Pv72/c3d1NWFhYlqtOOd3l4fDhw2bgwIGmfPnyxs3NzZQtW9Y0bdrUTJo0yd5n+vTppmnTpqZMmTLG3d3dVKxY0QwaNMgcOXLEYVtjxowxwcHBplixYlnu4LBy5UrTunVr4+fnZzw8PExoaKjp2bOnWb9+vb1PTle0jTEmISHBPPHEEyYoKMi4urqa0NBQM2bMGJOUlOTQ72auaEvK9ufq8fPnzzd333238fHxMV5eXqZq1aqmb9++ZteuXVm2OXbsWCPJhISE2O9mca3cnIdrr1Zu27bN3HfffSY0NNR4eHiY0qVLm1atWjn8BeR6x9m5c2ezdOlSU6dOHePu7m4qVapk3njjjRzH9O/f37i6uprff//9htvPdPX5K1asmPHz8zN169Y1Q4YMyXJl1JjsH49//vmnGTRokClXrpzx9vY2zZs3N5s3b87ymM+8ov3RRx+Z4cOHm7JlyxoPDw/TokWLbH8v3333nXnwwQdNuXLljJubmwkMDDRt2rQxc+bMcegXHR1tKleubFxcXLLUtmnTJtO5c2dTqlQp4+bmZsqXL286d+5sPvvsM2OMMUlJSeaJJ54wd911l/Hz8zNeXl6mRo0aZvz48Vn+anCtzCvamT/u7u6mXLlyplWrVmby5MkOrwuZ8voYSU1NNdOmTTNhYWHG09PTFC9e3NSsWdM8/vjj5pdffrH327p1qwkPDzfe3t6mbNmyZvDgwWbPnj0O5+XUqVOmf//+pmbNmsbHx8cUL17c3HXXXWbGjBkmLS3NYb+5edwDtzubMbn42DgAoNBKSUlRpUqV1Lx58yz3BgcAWIepIwBQRP3xxx86ePCgFixYoFOnTtn/9A8AKBgEbQAoolavXq0BAwYoKChIs2bNypdb+gEAco+pIwAAAIAF+MIaAAAAwAIEbQAAAMACBG0AAADAArfdhyEzMjJ08uRJ+fr6Zvu10AAAAHAu839fkhUcHKxixQrvdeHbLmifPHlSISEhzi4DAAAAN3D8+PEcv0m2MLjtgravr6+kv35xfn5+Tq4GKDxSU1O1du1aRUREyM3NzdnlAACKsLNnz6py5cr23FZY3XZBO3O6iJ+fH0EbuAmpqany9vaWn58fQRsAYKnU1FRJKvTTfAvvpBcAAADgb4ygDQAAAFiAoA0AAABYgKANAAAAWICgDQAAAFiAoA0AAABYgKANAAAAWICgDQAAAFiAoA0AAABYgKANAAAAWMCpQXv27Nm666677F+HHh4eri+//PK6YzZt2qSGDRvK09NTVapU0Zw5cwqoWgAAACD3nBq0K1SooFdffVW7du3Srl271KZNG3Xv3l0//vhjtv0PHz6sTp06qUWLFtq7d6/Gjh2r4cOHa9myZQVcOQAAAHB9rs7cedeuXR2WX3nlFc2ePVvbt29XnTp1svSfM2eOKlasqOjoaElSrVq1tGvXLk2bNk33339/QZQMAAAA5IpTg/bV0tPT9dlnn+nSpUsKDw/Pts+2bdsUERHh0BYZGal58+YpNTVVbm5uWcYkJycrOTnZvpyYmChJSk1NVWpqaj4eAfD3d/nyZR08eDBPYy9eSdbW2F/lW2K7int55LmGGjVqyNvbO8/jAQBFX1HJaE4P2rGxsQoPD1dSUpKKFy+uFStWqHbt2tn2jY+PV0BAgENbQECA0tLSdObMGQUFBWUZM2XKFE2cODFL+9q1a3mzx23n119/1bPPPntL25h6izVMnz5dVatWvcWtAACKssuXLzu7hHzh9KBdo0YN7du3T+fOndOyZcvUr18/bdq0KcewbbPZHJaNMdm2ZxozZoyioqLsy4mJiQoJCVFERIT8/Pzy6SiAwuHy5ctq3rx5nsb+HHdez63Yr9fvq63qQf55roEr2gCAG0lISHB2CfnC6UHb3d1dd9xxhySpUaNG2rlzp9588029++67WfoGBgYqPj7eoe306dNydXVV6dKls92+h4eHPDyy/pnbzc0t26kmQFHm7++vJk2a5Gms+9EEeWxL0Z31GqheaPbPNwAA8kNRyWh/u/toG2Mc5lRfLTw8XOvWrXNoW7t2rRo1alRkfiEAAAAoGpwatMeOHavNmzfryJEjio2N1bhx47Rx40b16dNH0l/TPvr27Wvv/8QTT+jo0aOKiorSgQMHNH/+fM2bN0+jRo1y1iEAAAAA2XLq1JFTp07p0UcfVVxcnPz9/XXXXXcpJiZG7du3lyTFxcXp2LFj9v6VK1fWmjVrNHLkSL3zzjsKDg7WzJkzubUfAAAA/nacGrTnzZt33fULFy7M0taqVSvt2bPHoooAAACA/PG3m6MNAAAAFAUEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAk4N2lOmTFHjxo3l6+urcuXKqUePHjp48OB1x2zcuFE2my3Lz08//VRAVQMAAAA35tSgvWnTJg0bNkzbt2/XunXrlJaWpoiICF26dOmGYw8ePKi4uDj7T7Vq1QqgYgAAACB3XJ2585iYGIflBQsWqFy5ctq9e7datmx53bHlypVTiRIlLKwOAAAAyDunBu1rnT9/XpJUqlSpG/atX7++kpKSVLt2bb3wwgtq3bp1tv2Sk5OVnJxsX05MTJQkpaamKjU1NR+qBm4PaWlp9v/y3AEAWKmovM/8bYK2MUZRUVFq3ry57rzzzhz7BQUFae7cuWrYsKGSk5P10UcfqW3bttq4cWO2V8GnTJmiiRMnZmlfu3atvL298/UYgKLs+EVJctX27dt14gdnVwMAKMouX77s7BLyhc0YY5xdhCQNGzZMq1ev1pYtW1ShQoWbGtu1a1fZbDatWrUqy7rsrmiHhITozJkz8vPzu+W6gdvFd8fOqud7u7T0sUYKq3jjvzoBAJBXCQkJCgoK0vnz5wt1XvtbXNF++umntWrVKn3zzTc3HbIl6Z577tGiRYuyXefh4SEPD48s7W5ubnJzc7vpfQG3K1dXV/t/ee4AAKxUVN5nnBq0jTF6+umntWLFCm3cuFGVK1fO03b27t2roKCgfK4OAAAAyDunBu1hw4bpk08+0eeffy5fX1/Fx8dLkvz9/eXl5SVJGjNmjE6cOKEPP/xQkhQdHa1KlSqpTp06SklJ0aJFi7Rs2TItW7bMaccBAAAAXMupQXv27NmSpHvvvdehfcGCBerfv78kKS4uTseOHbOvS0lJ0ahRo3TixAl5eXmpTp06Wr16tTp16lRQZQMAAAA35PSpIzeycOFCh+XRo0dr9OjRFlUEAAAA5A+nfjMkAAAAUFQRtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALODVoT5kyRY0bN5avr6/KlSunHj166ODBgzcct2nTJjVs2FCenp6qUqWK5syZUwDVAgAAALnn1KC9adMmDRs2TNu3b9e6deuUlpamiIgIXbp0Kccxhw8fVqdOndSiRQvt3btXY8eO1fDhw7Vs2bICrBwAAAC4Pldn7jwmJsZhecGCBSpXrpx2796tli1bZjtmzpw5qlixoqKjoyVJtWrV0q5duzRt2jTdf//9VpcMAAAA5IpTg/a1zp8/L0kqVapUjn22bdumiIgIh7bIyEjNmzdPqampcnNzc1iXnJys5ORk+3JiYqIkKTU1VampqflVOlBgjiRc0qXk9ALf78/x5x3+W9B8PFxUqbSPU/YNAChYRSWj/W2CtjFGUVFRat68ue68884c+8XHxysgIMChLSAgQGlpaTpz5oyCgoIc1k2ZMkUTJ07Msp21a9fK29s7f4oHCsjpK9Ir+5z7tB294oDT9j2uXprKeTlt9wCAAnL58mVnl5Av/jZB+6mnntL333+vLVu23LCvzWZzWDbGZNsuSWPGjFFUVJR9OTExUSEhIYqIiJCfn98tVg0UrB9PJkr7tmtaz7q6o2zBXt29lJSsmM071aFFY/l4ehTovg/9cUmjlsaqcXhz1QnmeQsARV1CQoKzS8gXf4ug/fTTT2vVqlX65ptvVKFChev2DQwMVHx8vEPb6dOn5erqqtKlS2fp7+HhIQ+PrKHAzc0tyzQT4O/O1fWvp2zNIH/dWd6/QPedmpqqMz9JTaqULfDnTuZxu7q68rwFgNtAUXmtd+pdR4wxeuqpp7R8+XJt2LBBlStXvuGY8PBwrVu3zqFt7dq1atSoUZH5pQAAAKDwc2rQHjZsmBYtWqRPPvlEvr6+io+PV3x8vK5cuWLvM2bMGPXt29e+/MQTT+jo0aOKiorSgQMHNH/+fM2bN0+jRo1yxiEAAAAA2XJq0J49e7bOnz+ve++9V0FBQfafxYsX2/vExcXp2LFj9uXKlStrzZo12rhxo+rVq6eXX35ZM2fO5NZ+AAAA+Ftx6hztzA8xXs/ChQuztLVq1Up79uyxoCIAAAAgfzj1ijYAAABQVBG0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALuOa2Y2JiYq436ufnl6diAAAAgKIi10G7RIkSstls1+1jjJHNZlN6evotFwYAAAAUZrkO2l9//bWVdQAAAABFSq6DdqtWraysAwAAAChSch20r3Xu3DnNmzdPBw4ckM1mU+3atTVw4ED5+/vnZ30AAABAoZSnu47s2rVLVatW1YwZM3T27FmdOXNGb7zxhqpWrao9e/bkd40AAABAoZOnK9ojR45Ut27d9N5778nV9a9NpKWlafDgwRoxYoS++eabfC0SAAAAKGzyFLR37drlELIlydXVVaNHj1ajRo3yrTgAAACgsMrT1BE/Pz8dO3YsS/vx48fl6+t7y0UBAAAAhV2egnavXr00aNAgLV68WMePH9fvv/+uTz/9VIMHD9bDDz+c3zUCAAAAhU6epo5MmzZNNptNffv2VVpamiTJzc1NTz75pF599dV8LRAAAAAojG46aKenp2vbtm0aP368pkyZol9//VXGGN1xxx3y9va2okYAAACg0LnpoO3i4qLIyEgdOHBApUqVUt26da2oCwAAACjU8jRHu27duvrtt9/yuxYAAACgyMhT0H7llVc0atQoffHFF4qLi1NiYqLDDwAAAHC7y9OHITt06CBJ6tatm2w2m73dGCObzab09PT8qQ4AAAAopPIUtL/++uv8rgMAAAAoUvIUtFu1apXfdQAAAABFSp7maEvS5s2b9cgjj6hp06Y6ceKEJOmjjz7Sli1b8q04AAAAoLDKU9BetmyZIiMj5eXlpT179ig5OVmSdOHCBU2ePDlfCwQAAAAKozwF7UmTJmnOnDl677335ObmZm9v2rSp9uzZk2/FAQAAAIVVnoL2wYMH1bJlyyztfn5+Onfu3K3WBAAAABR6eQraQUFBOnToUJb2LVu2qEqVKrdcFAAAAFDY5SloP/7443rmmWe0Y8cO2Ww2nTx5Uh9//LFGjRqloUOH5neNAAAAQKGTp9v7jR49WufPn1fr1q2VlJSkli1bysPDQ6NGjdJTTz2V3zUCAAAAhU6egrb019ewjxs3Tvv371dGRoZq166t4sWL52dtAAAAQKGVp6kjAwcO1IULF+Tt7a1GjRqpSZMmKl68uC5duqSBAwfmd40AAABAoZOnoP3BBx/oypUrWdqvXLmiDz/88JaLAgAAAAq7m5o6kpiYKGOMjDG6cOGCPD097evS09O1Zs0alStXLt+LBAAAAAqbmwraJUqUkM1mk81mU/Xq1bOst9lsmjhxYr4VBwAAABRWNxW0v/76axlj1KZNGy1btkylSpWyr3N3d1doaKiCg4PzvUgAAACgsLmpoN2qVStJ0uHDh1WxYkXZbDZLigIAAAAKuzx9GPLAgQP63//+Z19+5513VK9ePfXu3Vt//vlnrrfzzTffqGvXrgoODpbNZtPKlSuv23/jxo32qStX//z00095OQwAAADAMnkK2s8995wSExMlSbGxsYqKilKnTp3022+/KSoqKtfbuXTpksLCwvT222/f1P4PHjyouLg4+0+1atVuajwAAABgtTx9Yc3hw4dVu3ZtSdKyZcvUtWtXTZ48WXv27FGnTp1yvZ2OHTuqY8eON73/cuXKqUSJEjc9DgAAACgoeQra7u7uunz5siRp/fr16tu3rySpVKlS9ivdVqpfv76SkpJUu3ZtvfDCC2rdunWOfZOTk5WcnGxfzqwvNTVVqampltcK5Ke0tDT7fwv68Zu5P2c8b5x53ACAgldUXuvzFLSbN2+uqKgoNWvWTN9++60WL14sSfr5559VoUKFfC3wakFBQZo7d64aNmyo5ORkffTRR2rbtq02btyoli1bZjtmypQp2d5ycO3atfL29rasVsAKxy9Kkqu2bNmio8WdU8O6desKfJ9/h+MGABSczAu6hZ3NGGNudtCxY8c0dOhQHT9+XMOHD9egQYMkSSNHjlR6erpmzpx584XYbFqxYoV69OhxU+O6du0qm82mVatWZbs+uyvaISEhOnPmjPz8/G66TsCZfjyZqB6zt2vlk/eoTnDBPn5TU1O1bt06tW/fXm5ubgW6b2ceNwCg4CUkJCgoKEjnz58v1HktT1e0K1asqC+++CJL+4wZM265oJt1zz33aNGiRTmu9/DwkIeHR5Z2Nze3Ag8LwK1ydXW1/9dZj19nPHf+DscNACg4ReW1Pk93HZGkX3/9VS+88IIefvhhnT59WpIUExOjH3/8Md+Ky429e/cqKCioQPcJAAAA3EiegvamTZtUt25d7dixQ8uXL9fFixclSd9//73Gjx+f6+1cvHhR+/bt0759+yT9dTeTffv26dixY5KkMWPG2D9oKUnR0dFauXKlfvnlF/34448aM2aMli1bpqeeeiovhwEAAABYJk9TR55//nlNmjRJUVFR8vX1tbe3bt1ab775Zq63s2vXLoc7hmTeg7tfv35auHCh4uLi7KFbklJSUjRq1CidOHFCXl5eqlOnjlavXn1TtxQEAAAACkKegnZsbKw++eSTLO1ly5ZVQkJCrrdz77336nqfxVy4cKHD8ujRozV69Ohcbx8AAABwljxNHSlRooTi4uKytO/du1fly5e/5aIAAACAwi5PQbt379765z//qfj4eNlsNmVkZOh///ufRo0a5TCnGgAAALhd5Slov/LKK6pYsaLKly+vixcvqnbt2mrZsqWaNm2qF154Ib9rBAAAAAqdPM3RdnNz08cff6yXXnpJe/fuVUZGhurXr69q1arld30AAABAoZSnoJ2patWqqlq1an7VAgAAABQZuQ7ambfey4033ngjT8UAAAAARUWug/bevXtz1c9ms+W5GAAAAKCoyHXQ/vrrr62sAwAAAChS8nTXkav9/vvvOnHiRH7UAgAAABQZeQraGRkZeumll+Tv76/Q0FBVrFhRJUqU0Msvv6yMjIz8rhEAAAAodPJ015Fx48Zp3rx5evXVV9WsWTMZY/S///1PEyZMUFJSkl555ZX8rhMAAAAoVPIUtD/44AO9//776tatm70tLCxM5cuX19ChQwnaAAAAuO3laerI2bNnVbNmzSztNWvW1NmzZ2+5KAAAAKCwy1PQDgsL09tvv52l/e2331ZYWNgtFwUAAAAUdnmaOjJ16lR17txZ69evV3h4uGw2m7Zu3arjx49rzZo1+V0jAAAAUOjk6Yp2q1at9PPPP+u+++7TuXPndPbsWf3jH//QwYMH1aJFi/yuEQAAACh08nRFW5KCg4P50CMAAACQgzwH7XPnzunbb7/V6dOns9w7u2/fvrdcGAAAAFCY5Slo/+c//1GfPn106dIl+fr6ymaz2dfZbDaCNgAAAG57eZqj/eyzz2rgwIG6cOGCzp07pz///NP+w+39AAAAgDwG7RMnTmj48OHy9vbO73oAAACAIiFPQTsyMlK7du3K71oAAACAIiPXc7RXrVpl///OnTvrueee0/79+1W3bl25ubk59L36q9kBAACA21Gug3aPHj2ytL300ktZ2mw2m9LT02+pKAAAAKCwy3XQvvYWfgAAAABydlNztDds2KDatWsrMTExy7rz58+rTp062rx5c74VBwAAABRWNxW0o6Oj9dhjj8nPzy/LOn9/fz3++ON644038q04AAAAoLC6qaD93XffqUOHDjmuj4iI0O7du2+5KAAAAKCwu6mgferUqSx3GLmaq6ur/vjjj1suCgAAACjsbipoly9fXrGxsTmu//777xUUFHTLRQEAAACF3U0F7U6dOunFF19UUlJSlnVXrlzR+PHj1aVLl3wrDgAAACiscn17P0l64YUXtHz5clWvXl1PPfWUatSoIZvNpgMHDuidd95Renq6xo0bZ1WtAAAAQKFxU0E7ICBAW7du1ZNPPqkxY8bIGCPpry+piYyM1KxZsxQQEGBJoQAAAEBhclNBW5JCQ0O1Zs0a/fnnnzp06JCMMapWrZpKlixpRX0AAABAoXTTQTtTyZIl1bhx4/ysBQAAACgyburDkAAAAAByh6ANAAAAWICgDQAAAFiAoA0AAABYgKANAAAAWICgDQAAAFiAoA0AAABYgKANAAAAWICgDQAAAFiAoA0AAABYgKANAAAAWICgDQAAAFiAoA0AAABYwKlB+5tvvlHXrl0VHBwsm82mlStX3nDMpk2b1LBhQ3l6eqpKlSqaM2eO9YUCAAAAN8mpQfvSpUsKCwvT22+/nav+hw8fVqdOndSiRQvt3btXY8eO1fDhw7Vs2TKLKwUAAABujqszd96xY0d17Ngx1/3nzJmjihUrKjo6WpJUq1Yt7dq1S9OmTdP9999vUZUAAADAzXNq0L5Z27ZtU0REhENbZGSk5s2bp9TUVLm5uWUZk5ycrOTkZPtyYmKiJCk1NVWpqanWFgzks7S0NNlcE3Xoz/3KcPUp8H2fTDup2NOxcnUt2JeO3/68JJtrotLS0njeAsBtoKi81heqoB0fH6+AgACHtoCAAKWlpenMmTMKCgrKMmbKlCmaOHFilva1a9fK29vbsloBKxy/KLmV2KF/7Z7stBpmrZ/llP26lWirLVu8dbS4U3YPAChAly9fdnYJ+aJQBW1JstlsDsvGmGzbM40ZM0ZRUVH25cTERIWEhCgiIkJ+fn7WFQpY4MeTiZr+3mVN7dBLVcoW/BXtHdt36O577i74K9p/XNLIT4+o+QPNVSeY5y0AFHUJCQnOLiFfFKqgHRgYqPj4eIe206dPy9XVVaVLl852jIeHhzw8PLK0u7m5ZTvVBPg7c3V1lUnz0x0la+vOAP8C3XdqaqqOux5X3XJ1C/y5UyztvEzaWbm6uvK8BYDbQFF5rS9U99EODw/XunXrHNrWrl2rRo0aFZlfCAAAAIoGpwbtixcvat++fdq3b5+kv27ft2/fPh07dkzSX9M++vbta+//xBNP6OjRo4qKitKBAwc0f/58zZs3T6NGjXJG+QAAAECOnDp1ZNeuXWrdurV9OXMudb9+/bRw4ULFxcXZQ7ckVa5cWWvWrNHIkSP1zjvvKDg4WDNnzuTWfgAAAPjbcWrQvvfee+0fZszOwoULs7S1atVKe/bssbAqAAAA4NYVqjnaAAAAQGFB0AYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAs4PSgPWvWLFWuXFmenp5q2LChNm/enGPfjRs3ymazZfn56aefCrBiAAAA4MacGrQXL16sESNGaNy4cdq7d69atGihjh076tixY9cdd/DgQcXFxdl/qlWrVkAVAwAAALnj1KD9xhtvaNCgQRo8eLBq1aql6OhohYSEaPbs2dcdV65cOQUGBtp/XFxcCqhiAAAAIHdcnbXjlJQU7d69W88//7xDe0REhLZu3XrdsfXr11dSUpJq166tF154Qa1bt86xb3JyspKTk+3LiYmJkqTU1FSlpqbewhEABS8tLc3+34J+/GbuzxnPG2ceNwCg4BWV13qnBe0zZ84oPT1dAQEBDu0BAQGKj4/PdkxQUJDmzp2rhg0bKjk5WR999JHatm2rjRs3qmXLltmOmTJliiZOnJilfe3atfL29r71AwEK0PGLkuSqLVu26Ghx59Swbt26At/n3+G4AQAF5/Lly84uIV84LWhnstlsDsvGmCxtmWrUqKEaNWrYl8PDw3X8+HFNmzYtx6A9ZswYRUVF2ZcTExMVEhKiiIgI+fn55cMRAAXnx5OJmha7Xc2bN1ed4IJ9/KampmrdunVq37693NzcCnTfzjxuAEDBS0hIcHYJ+cJpQbtMmTJycXHJcvX69OnTWa5yX88999yjRYsW5bjew8NDHh4eWdrd3NwKPCwAt8rV1dX+X2c9fp3x3Pk7HDcAoOAUldd6p30Y0t3dXQ0bNszyZ+h169apadOmud7O3r17FRQUlN/lAQAAALfEqVNHoqKi9Oijj6pRo0YKDw/X3LlzdezYMT3xxBOS/pr2ceLECX344YeSpOjoaFWqVEl16tRRSkqKFi1apGXLlmnZsmXOPAwAAAAgC6cG7V69eikhIUEvvfSS4uLidOedd2rNmjUKDQ2VJMXFxTncUzslJUWjRo3SiRMn5OXlpTp16mj16tXq1KmTsw4BAAAAyJbTPww5dOhQDR06NNt1CxcudFgePXq0Ro8eXQBVAQAAALfG6V/BDgAAABRFBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAKuzi4AQO5dSU2XJP1w4nyB7/vSlWTt+kMKPPqnfLw8CnTfh05fLND9AQCQHwjaQCHy6/8FzueXxzqpAld9dGink/Yt+XjwkgUAKDx41wIKkYg6gZKkquWKy8vNpUD3fTDuvJ5dGqvpPeuqRpB/ge5b+itkVy7jU+D7BQAgrwjaQCFSysddDzWp6JR9p6WlSZKqlvXRneULPmgDAFDY8GFIAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAIEbQAAAMACBG0AAADAAgRtAAAAwAJOD9qzZs1S5cqV5enpqYYNG2rz5s3X7b9p0yY1bNhQnp6eqlKliubMmVNAlQIAAAC559SgvXjxYo0YMULjxo3T3r171aJFC3Xs2FHHjh3Ltv/hw4fVqVMntWjRQnv37tXYsWM1fPhwLVu2rIArBwAAAK7PqUH7jTfe0KBBgzR48GDVqlVL0dHRCgkJ0ezZs7PtP2fOHFWsWFHR0dGqVauWBg8erIEDB2ratGkFXDkAAABwfa7O2nFKSop2796t559/3qE9IiJCW7duzXbMtm3bFBER4dAWGRmpefPmKTU1VW5ublnGJCcnKzk52b6cmJgoSUpNTVVqauqtHgZQqFy+fFkHDx7M09if484rOf6QftjnrpRT/nmuoUaNGvL29s7zeABA0VdUMprTgvaZM2eUnp6ugIAAh/aAgADFx8dnOyY+Pj7b/mlpaTpz5oyCgoKyjJkyZYomTpyYpX3t2rW82eO28+uvv+rZZ5+9pW08+sGt1TB9+nRVrVr11jYCACjSLl++7OwS8oXTgnYmm83msGyMydJ2o/7ZtWcaM2aMoqKi7MuJiYkKCQlRRESE/Pz88lo2UChdvnxZzZs3z9PYi1eS9dXmnYps0VjFvTzyXANXtAEAN5KQkODsEvKF04J2mTJl5OLikuXq9enTp7Nctc4UGBiYbX9XV1eVLl062zEeHh7y8MgaCtzc3LKdagIUZf7+/mrSpEmexqampurCubNq0fQenjsAAEsVlfcZp30Y0t3dXQ0bNtS6desc2tetW6emTZtmOyY8PDxL/7Vr16pRo0ZF5hcCAACAosGpdx2JiorS+++/r/nz5+vAgQMaOXKkjh07pieeeELSX9M++vbta+//xBNP6OjRo4qKitKBAwc0f/58zZs3T6NGjXLWIQAAAADZcuoc7V69eikhIUEvvfSS4uLidOedd2rNmjUKDQ2VJMXFxTncU7ty5cpas2aNRo4cqXfeeUfBwcGaOXOm7r//fmcdAgAAAJAtm8n8NOFtIjExUf7+/jp//jwfhgRuQmpqqtasWaNOnToxVQsAYKmEhASVKVOm0Oc1p38FOwAAAFAUEbQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALELQBAAAACxC0AQAAAAsQtAEAAAALuDq7gIKW+Y3ziYmJTq4EKFxSU1N1+fJlJSYm8hXsAABLXbhwQdL/z22F1W0XtDN/cSEhIU6uBAAAANeTkJAgf39/Z5eRZzZT2P+pcJMyMjJ08uRJ+fr6ymazObscoNBITExUSEiIjh8/Lj8/P2eXAwAows6fP6+KFSvqzz//VIkSJZxdTp7ddle0ixUrpgoVKji7DKDQ8vPzI2gDAApEsWKF++OEhbt6AAAA4G+KoA0AAABYgKANIFc8PDw0fvx4eXh4OLsUAEARV1Tec267D0MCAAAABYEr2gAAAIAFCNoAAACABQjaAAAAgAUI2gAAAIAFCNrAbeTee+/ViBEjnF3GLUlJSdEdd9yh//3vf7keExsbqwoVKujSpUsWVgYARZ/NZtPKlSudXcYtOXjwoAIDA3XhwoVcj3n77bfVrVu3m94XQRsoAP3795fNZtOrr77q0L5y5UrZbLZb2vbChQtls9lks9nk4uKikiVL6u6779ZLL72k8+fPO/Rdvny5Xn755Vvan7PNnTtXoaGhatasmb3tzz//1KOPPip/f3/5+/vr0Ucf1blz5+zr69atqyZNmmjGjBlOqBgA/t4y36NsNpvc3NwUEBCg9u3ba/78+crIyHDoGxcXp44dOzqp0vwxbtw4DRs2TL6+vpKkpKQk9e/fX3Xr1pWrq6t69OiRZcxjjz2mnTt3asuWLTe1L4I2UEA8PT312muv6c8//8z3bfv5+SkuLk6///67tm7dqiFDhujDDz9UvXr1dPLkSXu/UqVK2V9YCqu33npLgwcPdmjr3bu39u3bp5iYGMXExGjfvn169NFHHfoMGDBAs2fPVnp6ekGWCwCFQocOHRQXF6cjR47oyy+/VOvWrfXMM8+oS5cuSktLs/cLDAws1Pe2/v3337Vq1SoNGDDA3paeni4vLy8NHz5c7dq1y3ach4eHevfurbfeeuum9kfQBgpIu3btFBgYqClTply337Jly1SnTh15eHioUqVKmj59+g23bbPZFBgYqKCgINWqVUuDBg3S1q1bdfHiRY0ePdre79qpI7NmzVK1atXk6empgIAA9ezZ077OGKOpU6eqSpUq8vLyUlhYmJYuXWpfn56erkGDBqly5cry8vJSjRo19OabbzrUtXHjRjVp0kQ+Pj4qUaKEmjVrpqNHj9rX/+c//1HDhg3l6empKlWqaOLEiQ4v6Nfas2ePDh06pM6dO9vbDhw4oJiYGL3//vsKDw9XeHi43nvvPX3xxRc6ePCgvV9kZKQSEhK0adOmG55PALjdeHh4KDAwUOXLl1eDBg00duxYff755/ryyy+1cOFCe7+rp46kpKToqaeeUlBQkDw9PVWpUiWH97jz589ryJAhKleunPz8/NSmTRt999139vW//vqrunfvroCAABUvXlyNGzfW+vXrHeq6lfep7CxZskRhYWGqUKGCvc3Hx0ezZ8/WY489psDAwBzHduvWTStXrtSVK1euu4+rEbSBAuLi4qLJkyfrrbfe0u+//55tn927d+vBBx/UQw89pNjYWE2YMEH/+te/HF7kcqtcuXLq06ePVq1ale1V3F27dmn48OF66aWXdPDgQcXExKhly5b29S+88IIWLFig2bNn68cff9TIkSP1yCOP2INqRkaGKlSooCVLlmj//v168cUXNXbsWC1ZskSSlJaWph49eqhVq1b6/vvvtW3bNg0ZMsQ+Vearr77SI488ouHDh2v//v169913tXDhQr3yyis5HtM333yj6tWry8/Pz962bds2+fv76+6777a33XPPPfL399fWrVvtbe7u7goLC9PmzZtv+lwCwO2oTZs2CgsL0/Lly7NdP3PmTK1atUpLlizRwYMHtWjRIlWqVEnSXyG4c+fOio+P15o1a7R79241aNBAbdu21dmzZyVJFy9eVKdOnbR+/Xrt3btXkZGR6tq1q44dOybp1t+nsvPNN9+oUaNGeTofjRo1Umpqqr799tvcDzIALNevXz/TvXt3Y4wx99xzjxk4cKAxxpgVK1aYq5+GvXv3Nu3bt3cY+9xzz5natWvnuO0FCxYYf3//bNfNnj3bSDKnTp0yxhjTqlUr88wzzxhjjFm2bJnx8/MziYmJWcZdvHjReHp6mq1btzq0Dxo0yDz88MM51jJ06FBz//33G2OMSUhIMJLMxo0bs+3bokULM3nyZIe2jz76yAQFBeW4/Weeeca0adPGoe2VV14x1apVy9K3WrVqWbZ/3333mf79++e4fQC4HV39HnWtXr16mVq1atmXJZkVK1YYY4x5+umnTZs2bUxGRkaWcf/973+Nn5+fSUpKcmivWrWqeffdd3OspXbt2uatt94yxljzPhUWFmZeeumlHNdf71wYY0zJkiXNwoULc1x/Ldc8RXoAefbaa6+pTZs2evbZZ7OsO3DggLp37+7Q1qxZM0VHRys9PV0uLi43tS9jjCRl+4HL9u3bKzQ0VFWqVFGHDh3UoUMH3XffffL29tb+/fuVlJSk9u3bO4xJSUlR/fr17ctz5szR+++/r6NHj+rKlStKSUlRvXr1JP01H7x///6KjIxU+/bt1a5dOz344IMKCgqS9NfV+507dzpcwU5PT1dSUpIuX74sb2/vLDVfuXJFnp6eWdqzOz5jTJZ2Ly8vXb58OafTBQC4RnavpZn69++v9u3bq0aNGurQoYO6dOmiiIgISX+9xl+8eFGlS5d2GHPlyhX9+uuvkqRLly5p4sSJ+uKLL3Ty5EmlpaXpypUr9iva+fE+da2c3kdy62bfRwjaQAFr2bKlIiMjNXbsWPXv399hXXYvaJlhOS8OHDggPz+/LC90kuTr66s9e/Zo48aNWrt2rV588UVNmDBBO3futH/KfPXq1SpfvrzDuMwPwSxZskQjR47U9OnTFR4eLl9fX73++uvasWOHve+CBQs0fPhwxcTEaPHixXrhhRe0bt063XPPPcrIyNDEiRP1j3/8I0ttOb0IlilTRrGxsQ5tgYGBOnXqVJa+f/zxhwICAhzazp49q6pVq2a7bQBAVgcOHFDlypWzXdegQQMdPnxYX375pdavX68HH3xQ7dq109KlS5WRkaGgoCBt3Lgxy7gSJUpIkp577jl99dVXmjZtmu644w55eXmpZ8+eSklJkXTr71PZKVOmzC3dlODs2bMqW7ZsrvsTtAEnePXVV1WvXj1Vr17dob127dpZbh20detWVa9e/aavZp8+fVqffPKJevTooWLFsv84hqurq9q1a6d27dpp/PjxKlGihDZs2KD27dvLw8NDx44dU6tWrbIdu3nzZjVt2lRDhw61t2Vepbha/fr1Vb9+fY0ZM0bh4eH65JNPdM8996hBgwY6ePCg7rjjjlwfU/369TV79myHf5CEh4fr/Pnz+vbbb9WkSRNJ0o4dO3T+/Hk1bdrUYfwPP/zg8EEaAEDONmzYoNjYWI0cOTLHPn5+furVq5d69eqlnj17qkOHDjp79qwaNGig+Ph4ubq62udtX2vz5s3q37+/7rvvPkl/zdk+cuSIQ59beZ/KTv369bV///5c97/ar7/+qqSkpOteMb8WQRtwgrp166pPnz5ZbhP07LPPqnHjxnr55ZfVq1cvbdu2TW+//bZmzZp13e0ZYxQfHy9jjM6dO6dt27Zp8uTJ8vf3z3Lv7kxffPGFfvvtN7Vs2VIlS5bUmjVrlJGRoRo1asjX11ejRo3SyJEjlZGRoebNmysxMVFbt25V8eLF1a9fP91xxx368MMP9dVXX6ly5cr66KOPtHPnTvuVj8OHD2vu3Lnq1q2bgoODdfDgQf3888/q27evJOnFF19Uly5dFBISogceeEDFihXT999/r9jYWE2aNCnbmlu3bq1Lly7pxx9/1J133ilJqlWrljp06KDHHntM7777riRpyJAh6tKli2rUqGEfe+TIEZ04cSLHWzcBwO0sOTlZ8fHxSk9P16lTpxQTE6MpU6aoS5cu9tfta82YMUNBQUGqV6+eihUrps8++0yBgYEqUaKE2rVrp/DwcPXo0UOvvfaaatSooZMnT2rNmjXq0aOHGjVqpDvuuEPLly9X165dZbPZ9K9//cvhvt23+j6VncjISA0ePDjLdMz9+/crJSVFZ8+e1YULF7Rv3z5Jsk+HlP76h0GVKlVu7i+juZ7NDSDPsvtwxZEjR4yHh4e59mm4dOlSU7t2bePm5mYqVqxoXn/99etue8GCBUaSkWRsNpvx9/c3TZo0MS+99JI5f/68Q9+rPwy5efNm06pVK1OyZEnj5eVl7rrrLrN48WJ734yMDPPmm2+aGjVqGDc3N1O2bFkTGRlpNm3aZIwxJikpyfTv39/4+/ubEiVKmCeffNI8//zzJiwszBhjTHx8vOnRo4cJCgoy7u7uJjQ01Lz44osmPT3dvo+YmBjTtGlT4+XlZfz8/EyTJk3M3Llzr3u8Dz30kHn++ecd2hISEkyfPn2Mr6+v8fX1NX369DF//vmnQ5/JkyebyMjI624bAG5H/fr1s7+PuLq6mrJly5p27dqZ+fPnO7xmG+P4Yci5c+eaevXqGR8fH+Pn52fatm1r9uzZY++bmJhonn76aRMcHGzc3NxMSEiI6dOnjzl27JgxxpjDhw+b1q1bGy8vLxMSEmLefvvtfH2fyk5aWpopX768iYmJcWgPDQ21n4Orf64WERFhpkyZclPn1vZ/Jw0ACoXY2Fi1a9dOhw4dyvWX7yQnJ6tatWr697//7fCNkgCA28+sWbP0+eef66uvvsr1mB9++EFt27bVzz//LH9//1yP4z7aAAqVunXraurUqVnm8V3P0aNHNW7cOEI2AEBDhgxRy5YtdeHChVyPOXnypD788MObCtmSxBVtAAAAwAJc0QYAAAAsQNAGAAAALEDQBgAAACxA0AYAAAAsQNAGAAAALEDQBgBJNptNK1euzHX/CRMmOHxjWFFRqVIlRUdHO7sMACgSCNoAiqz+/fvLZrPJZrPJzc1NAQEBat++vebPn+/wNb+SFBcXp44dOxZofUeOHJHNZrN/1a9VJkyYYD8Prq6uKlOmjFq2bKno6GglJyc79N25c6eGDBliaT0AcLsgaAMo0jp06KC4uDgdOXJEX375pVq3bq1nnnlGXbp0UVpamr1fYGCgPDw8nFipterUqaO4uDgdO3ZMX3/9tR544AFNmTJFTZs2dfjShrJly8rb29uJlQJA0UHQBlCkeXh4KDAwUOXLl1eDBg00duxYff755/ryyy+1cOFCe79rp47885//VPXq1eXt7a0qVaroX//6l1JTU7Ns/91331VISIi8vb31wAMP6Ny5cw7rFyxYoFq1asnT01M1a9bUrFmz7OsqV64sSapfv75sNpvuvffeXI1LSUnRU089paCgIHl6eqpSpUqaMmXKdc+Dq6urAgMDFRwcrLp16+rpp5/Wpk2b9MMPP+i1116z97t26siECRNUsWJFeXh4KDg4WMOHD3eoY/To0Spfvrx8fHx09913a+PGjfb1CQkJevjhh1WhQgV5e3urbt26+ve//+1Q19KlS1W3bl15eXmpdOnSateunS5dupSr8wAAf3euzi4AAApamzZtFBYWpuXLl2vw4MHZ9vH19dXChQsVHBys2NhYPfbYY/L19dXo0aPtfQ4dOqQlS5boP//5jxITEzVo0CANGzZMH3/8sSTpvffe0/jx4/X222+rfv362rt3rx577DH5+PioX79++vbbb9WkSROtX79ederUkbu7e67GzZw5U6tWrdKSJUtUsWJFHT9+XMePH7/p81CzZk117NhRy5cv16RJk7KsX7p0qWbMmKFPP/1UderUUXx8vL777jv7+gEDBujIkSP69NNPFRwcrBUrVqhDhw6KjY1VtWrVlJSUpIYNG+qf//yn/Pz8tHr1aj366KOqUqWK7r77bsXFxenhhx/W1KlTdd999+nChQvavHmzMr+w+EbnAQD+9gwAFFH9+vUz3bt3z3Zdr169TK1atezLksyKFSty3NbUqVNNw4YN7cvjx483Li4u5vjx4/a2L7/80hQrVszExcUZY4wJCQkxn3zyicN2Xn75ZRMeHm6MMebw4cNGktm7d69DnxuNe/rpp02bNm1MRkZGjvVebfz48SYsLCzbdf/85z+Nl5eXfTk0NNTMmDHDGGPM9OnTTfXq1U1KSkqWcYcOHTI2m82cOHHCob1t27ZmzJgxOdbSqVMn8+yzzxpjjNm9e7eRZI4cOZJt3xudBwD4u+OKNoDbkjFGNpstx/VLly5VdHS0Dh06pIsXLyotLU1+fn4OfSpWrKgKFSrYl8PDw5WRkaGDBw/KxcVFx48f16BBg/TYY4/Z+6Slpcnf3z/H/f7xxx83HNe/f3+1b99eNWrUUIcOHdSlSxdFRETc9DmQrn8eHnjgAUVHR6tKlSrq0KGDOnXqpK5du8rV1VV79uyRMUbVq1d3GJOcnKzSpUtLktLT0/Xqq69q8eLFOnHihJKTk5WcnCwfHx9JUlhYmNq2bau6desqMjJSERER6tmzp0qWLJmr8wAAf3cEbQC3pQMHDtjnSF9r+/bteuihhzRx4kRFRkbK399fn376qaZPn37dbWYGVpvNZr+ryXvvvae7777boZ+Li0uO28jNuAYNGujw4cP68ssvtX79ej344INq166dli5det36snO98xASEqKDBw9q3bp1Wr9+vYYOHarXX39dmzZtUkZGhlxcXLR79+4sx1O8eHFJ0vTp0zVjxgxFR0erbt268vHx0YgRI5SSkmI/nnXr1mnr1q1au3at3nrrLY0bN047duywfyDzZs8fAPydELQB3HY2bNig2NhYjRw5Mtv1//vf/xQaGqpx48bZ244ePZql37Fjx3Ty5EkFBwdLkrZt26ZixYqpevXqCggIUPny5fXbb7+pT58+2e4nc052enq6vS034yTJz89PvXr1Uq9evdSzZ0916NBBZ8+eValSpW58Av7PTz/9pJiYGI0ZMybHPl5eXurWrZu6deumYcOGqWbNmoqNjVX9+vWVnp6u06dPq0WLFtmO3bx5s7p3765HHnlE0l//iPjll19Uq1Ytex+bzaZmzZqpWbNmevHFFxUaGqoVK1YoKioqV+cBAP7OCNoAirTk5GTFx8crPT1dp06dUkxMjKZMmaIuXbqob9++2Y654447dOzYMX366adq3LixVq9erRUrVmTp5+npqX79+mnatGlKTEzU8OHD9eCDDyowMFDSX3fsGD58uPz8/NSxY0clJydr165d+vPPPxUVFaVy5crJy8tLMTExqlChgjw9PeXv73/DcTNmzFBQUJDq1aunYsWK6bPPPlNgYKBKlCiR43lIS0tTfHy8MjIylJCQoI0bN2rSpEmqV6+ennvuuWzHLFy4UOnp6br77rvl7e2tjz76SF5eXgoNDVXp0qXVp08f9e3bV9OnT1f9+vV15swZbdiwQXXr1lWnTp10xx13aNmyZdq6datKliypN954Q/Hx8fagvWPHDv33v/9VRESEypUrpx07duiPP/6wr7/ReQCAvz0nzxEHAMv069fPSDKSjKurqylbtqxp166dmT9/vklPT3foq2s+DPncc8+Z0qVLm+LFi5tevXqZGTNmGH9/f/v6zA8Yzpo1ywQHBxtPT0/zj3/8w5w9e9Zhux9//LGpV6+ecXd3NyVLljQtW7Y0y5cvt69/7733TEhIiClWrJhp1apVrsbNnTvX1KtXz/j4+Bg/Pz/Ttm1bs2fPnhzPw/jx4+3nwcXFxZQqVco0b97czJgxwyQlJTn0vfrDkCtWrDB333238fPzMz4+Puaee+4x69evt/dNSUkxL774oqlUqZJxc3MzgYGB5r777jPff/+9McaYhIQE0717d1O8eHFTrlw588ILL5i+ffvaP6C6f/9+ExkZacqWLWs8PDxM9erVzVtvvXVT5w8A/s5sxvzffZQAAAAA5Bu+sAYAAACwAEEbAAAAsABBGwAAALAAQRsAAACwAEEbAAAAsABBGwAAALAAQRsAAACwAEEbAAAAsABBGwAAALAAQRsAAACwAEEbAAAAsMD/A7hpduUaf0aRAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "\n", "plt.figure(figsize=(8, 5))\n", "df.boxplot( )\n", "plt.title('Cholesterol Levels by Diabetes Disease')\n", "plt.suptitle('')\n", "plt.xlabel('Diabetes Disease')\n", "plt.ylabel('Cholesterol')\n", "plt.xticks([1, 2], ['No Disease (0)', 'Disease (1)'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "id": "cdfe83cd-f03c-45d2-823b-7d4c89049007", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "ages = [50, 31, 32, 21, 33 ]\n", "\n", "plt.figure(figsize=(8, 6))\n", "#plt.scatter(df['Age'], df['cholesterol'], alpha=0.8)\n", "plt.hist(ages, bins=8, color='skyblue', edgecolor='black')\n", "\n", "#cp_counts = df['cp'].value_counts()\n", "\n", "plt.title('Age vs. cholesterol levels')\n", "plt.xlabel('Age')\n", "plt.ylabel('cholesterol levels')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "cb3e7aed-8332-467a-89ab-f3aa1689e004", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "ages = [50,31,45,32,21,33 ]\n", "\n", "\n", "plt.figure(figsize=(10, 6))\n", "plt.hist(ages, bins=10, color='skyblue', edgecolor='black')\n", "\n", "\n", "plt.title('Age Distribution in Diabetes Disease Dataset')\n", "plt.xlabel('Age')\n", "plt.ylabel('Frequency')\n", "plt.grid(axis='y')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "fbaadb8f-e850-4892-b079-f4c122dadafe", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "import matplotlib.pyplot as plt\n", "\n", "#age_grouped = df.groupby('age')['chol'].mean().reset_index()\n", "Age= [50, 31, 32, 21, 33]\n", "plt.figure(figsize=(10, 6))\n", "\n", "plt.hist(ages, bins=10, color='skyblue', edgecolor='black')\n", "\n", "\n", "#plt.plot(age_grouped['age'], age_grouped['chol'], marker='o', linestyle='-', color='blue')\n", "\n", "plt.title('Average Cholesterol Levels by Age')\n", "plt.xlabel('Age')\n", "plt.ylabel('Average Cholesterol Level')\n", "plt.grid()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "9decfb4e-1b77-4ce8-a710-3b4c4f351539", "metadata": {}, "outputs": [], "source": [ " import pandas as pd\n", " import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "id": "36a6748e-19cd-420a-bc21-c495b6af325b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PregnantGlucoseDiastolic_BPSkin_FoldSerum_InsulinBMIDiabetes_PedigreeAgeClass
06148.072.035.0NaN33.60.627501
1185.066.029.0NaN26.60.351310
28183.064.0NaNNaN23.30.672321
3189.066.023.094.028.10.167210
40137.040.035.0168.043.12.288331
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" ], "text/plain": [ " Pregnant Glucose Diastolic_BP Skin_Fold Serum_Insulin BMI \\\n", "0 6 148.0 72.0 35.0 NaN 33.6 \n", "1 1 85.0 66.0 29.0 NaN 26.6 \n", "2 8 183.0 64.0 NaN NaN 23.3 \n", "3 1 89.0 66.0 23.0 94.0 28.1 \n", "4 0 137.0 40.0 35.0 168.0 43.1 \n", "\n", " Diabetes_Pedigree Age Class \n", "0 0.627 50 1 \n", "1 0.351 31 0 \n", "2 0.672 32 1 \n", "3 0.167 21 0 \n", "4 2.288 33 1 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('Diabetes Missing Data (1).csv')\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "7fddb735-fecb-4a74-b0a2-1c3a999842b5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pregnant int64\n", "Glucose float64\n", "Diastolic_BP float64\n", "Skin_Fold float64\n", "Serum_Insulin float64\n", "BMI float64\n", "Diabetes_Pedigree float64\n", "Age int64\n", "Class int64\n", "dtype: object" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "66ecf2c8-6f4e-4788-8225-9378ecf2fda3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pregnant 0\n", "Glucose 5\n", "Diastolic_BP 35\n", "Skin_Fold 227\n", "Serum_Insulin 374\n", "BMI 11\n", "Diabetes_Pedigree 0\n", "Age 0\n", "Class 0\n", "dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "cc4d3b9a-b55f-4ec8-9185-3248afbaa1ae", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns dropped: ['Serum_Insulin']\n", "Remaining columns: Index(['Pregnant', 'Glucose', 'Diastolic_BP', 'Skin_Fold', 'BMI',\n", " 'Diabetes_Pedigree', 'Age', 'Class'],\n", " dtype='object')\n" ] } ], "source": [ "missing_percentage = df.isnull().mean() * 100\n", "threshold = 30 \n", "\n", "columns_to_drop = missing_percentage[missing_percentage > threshold].index\n", "\n", "df = df.drop(columns=columns_to_drop)\n", "\n", "print(f\"Columns dropped: {list(columns_to_drop)}\")\n", "print(f\"Remaining columns: {df.columns}\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "db1e7e47-ff4c-416e-ace1-59c10e20f33e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PregnantGlucoseDiastolic_BPSkin_FoldBMIDiabetes_PedigreeAgeClass
06148.072.035.033.60.627501
1185.066.029.026.60.351310
28183.064.0NaN23.30.672321
3189.066.023.028.10.167210
40137.040.035.043.12.288331
\n", "
" ], "text/plain": [ " Pregnant Glucose Diastolic_BP Skin_Fold BMI Diabetes_Pedigree Age \\\n", "0 6 148.0 72.0 35.0 33.6 0.627 50 \n", "1 1 85.0 66.0 29.0 26.6 0.351 31 \n", "2 8 183.0 64.0 NaN 23.3 0.672 32 \n", "3 1 89.0 66.0 23.0 28.1 0.167 21 \n", "4 0 137.0 40.0 35.0 43.1 2.288 33 \n", "\n", " Class \n", "0 1 \n", "1 0 \n", "2 1 \n", "3 0 \n", "4 1 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "label_encoder = LabelEncoder()\n", "for column in df.select_dtypes(include=['object']).columns:\n", " \n", " df[column] = label_encoder.fit_transform(df[column])\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 12, "id": "347c5feb-ddc9-409d-9af5-5be3de6ab2c4", "metadata": {}, "outputs": [], "source": [ "for column in df.select_dtypes(include=['float64']).columns:\n", " mean_value = df[column].mean() \n", " df[column] = df[column].fillna(mean_value) " ] }, { "cell_type": "code", "execution_count": 13, "id": "50176661-eb0f-46c6-8693-ad5f8cd9b50f", "metadata": {}, "outputs": [], "source": [ "for column in df.select_dtypes(include=['object']).columns:\n", " mode_value = df[column].mode()[0] \n", " df[column] = df[column].fillna(mode_value) " ] }, { "cell_type": "code", "execution_count": 14, "id": "85a57a12-c359-47e0-a047-6b155e477286", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pregnant 0\n", "Glucose 0\n", "Diastolic_BP 0\n", "Skin_Fold 0\n", "BMI 0\n", "Diabetes_Pedigree 0\n", "Age 0\n", "Class 0\n", "dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "edb3d684-0a74-49ce-a1c4-d6e844aa58c5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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768 rows × 8 columns

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" ], "text/plain": [ " Pregnant Glucose Diastolic_BP Skin_Fold BMI Diabetes_Pedigree Age \\\n", "0 6 148.0 72.0 35.00000 33.6 0.627 50 \n", "1 1 85.0 66.0 29.00000 26.6 0.351 31 \n", "2 8 183.0 64.0 29.15342 23.3 0.672 32 \n", "3 1 89.0 66.0 23.00000 28.1 0.167 21 \n", "4 0 137.0 40.0 35.00000 43.1 2.288 33 \n", ".. ... ... ... ... ... ... ... \n", "763 10 101.0 76.0 48.00000 32.9 0.171 63 \n", "764 2 122.0 70.0 27.00000 36.8 0.340 27 \n", "765 5 121.0 72.0 23.00000 26.2 0.245 30 \n", "766 1 126.0 60.0 29.15342 30.1 0.349 47 \n", "767 1 93.0 70.0 31.00000 30.4 0.315 23 \n", "\n", " Class \n", "0 1 \n", "1 0 \n", "2 1 \n", "3 0 \n", "4 1 \n", ".. ... \n", "763 0 \n", "764 0 \n", "765 0 \n", "766 1 \n", "767 0 \n", "\n", "[768 rows x 8 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 32, "id": "8a1c94c4-b965-4a29-886f-ec8ba9e3461f", "metadata": {}, "outputs": [], "source": [ "def replace_outliers_with_nan(column):\n", " Q1 = column.quantile(0.25)\n", " Q3 = column.quantile(0.75)\n", " IQR = Q3 - Q1\n", " lower_bound = Q1 - 1.5 * IQR\n", " upper_bound = Q3 + 1.5 * IQR\n", "\n", " return column.where((column >= lower_bound) & (column <= upper_bound), np.nan)\n", "\n", "for col in df.columns:\n", " df[col] = replace_outliers_with_nan(df[col])" ] }, { "cell_type": "code", "execution_count": 37, "id": "e4e630df-ef02-406a-8c9a-84b3edbfbd6f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sum of null values in each column:\n", "Pregnant 0\n", "Glucose 5\n", "Diastolic_BP 35\n", "Skin_Fold 227\n", "Serum_Insulin 374\n", "BMI 11\n", "Diabetes_Pedigree 0\n", "Age 0\n", "Class 0\n", "dtype: int64\n" ] } ], "source": [ "import pandas as pd\n", "\n", "data = pd.read_csv(\"Diabetes Missing Data (1).csv\")\n", "\n", "\n", "null_counts = data.isnull().sum()\n", "print(\"Sum of null values in each column:\")\n", "print(null_counts)" ] }, { "cell_type": "code", "execution_count": 40, "id": "19388475-5ffa-40c8-a0c1-53e4c4c4c528", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "plt.figure(figsize=(8, 6))\n", "sns.histplot(df['Age'], bins=15, kde=True, color='blue')\n", "plt.title('Age Distribution')\n", "plt.xlabel('Age')\n", "plt.ylabel('Frequency')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 47, "id": "fb3221e7-1273-4d99-be42-52ccb49876a8", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\alaam\\AppData\\Local\\Temp\\ipykernel_16484\\2073509643.py:2: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.countplot(x='Class', data=df, palette='Set2')\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "plt.figure(figsize=(8, 6))\n", "sns.countplot(x='Class', data=df, palette='Set2')\n", "plt.title('Class Count')\n", "plt.xlabel('Class')\n", "plt.ylabel('Count')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 46, "id": "887e34cf-db5c-436a-ace6-da9768f38d0e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['Pregnant', 'Glucose', 'Diastolic_BP', 'Skin_Fold', 'BMI',\n", " 'Diabetes_Pedigree', 'Age', 'Class'],\n", " dtype='object')\n" ] } ], "source": [ "print(df.columns)\n" ] }, { "cell_type": "code", "execution_count": 51, "id": "319125f5-bb14-41c8-9d93-97a605435e20", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 6))\n", "sns.scatterplot(x='Age', y='Diastolic_BP', data=df, hue='Class', palette='Set1')\n", "plt.title('Age vs Diastolic_BP')\n", "plt.xlabel('Age')\n", "plt.ylabel('Diastolic_BP')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 54, "id": "2ed883f0-2f79-485e-a40f-924cd3042607", "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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+APv27aOqqoo5c+a0bGOz2ZgxYwYrVqwAYO3atUQikTbblJeXM3r06JZt2hMKhfB6vW1emYgM+JBbV6A/9WP0v30X/fHvI1e8gvR70j01hUKhUCgUiowkY5xbKSX3338/06ZNY/To0QBUVVUBUFJS0mbbkpKSFltVVRVWq5W8vLyk27Tnpz/9KW63u+VVWVmZ6o/TaWQshtzxCfKdv4Kv0RiMBJGr30R/7+/IgC+t81MoFAqFQqHIRDLGuf3a177Gxo0befrpp+NsQog2/5dSxo21p6NtHnzwQTweT8vr0KFD5z7xrsLfiPzopcS23eugOTOjzelEhpqRDdXoB7Ygj+1BNtUjpZ7uaSkUCoVCoehGMiJ58+tf/zqvvvoqy5cvp6KiomW8tLQUMKKzZWVlLeM1NTUt0dzS0lLC4TANDQ1torc1NTVMnTo14fFsNhs2m60rPkrqCAUg1JzULOurEAXl3TihzEb6PciPX0FuXA5IJIAzB+2aryOL+yO0jHmOUygUCoVC0YWk9Y4vpeRrX/sa//znP1myZAkDBgxoYx8wYAClpaUsWrSoZSwcDvP++++3OK4TJ07EYrG02ebYsWNs3rw5qXPbIzBbOrY7XN0zjx6A1HXktpXIje8Dreojm73oz/8cmurTNrfW6JlTu6kAFdVX9FgyqA5ccY6o60/XktbI7b333stTTz3FK6+8QnZ2dkuOrNvtxuFwIITgvvvu4yc/+QlDhgxhyJAh/OQnP8HpdPLZz362Zdu7776bBx54gIKCAvLz8/nmN7/JmDFjmD17djo/XudwZEHfkXBwa7zNnoXIKez+OWUqfg9y9VuJbZEg8uhuhDs931coFqU+5Ofj6n3UBpsYlVfGiNwyCuzq4SQdSF2Hpjrk7k+N30VxP8TQSZBToFRIFBmP9DUiaw/B1hVIiw0x5lLILUY4stI9NcUZIKUEbx1y/2bkwa2I/DLEiIshOw9hyfDV5B5GWqXAkuXE/u1vf+Pzn/88YPwYfvSjH/HHP/6RhoYGpkyZwu9+97uWojOAYDDIt771LZ566ikCgQCXX345jz766BkXimWqFJhsrEV/8efgqT01aLWj3fAAlPRHCLXUDiA9x9H/8p2kdnHhVWjTruvGGRmEY1E21B3mLztWtI4nk22x880LZlPqzJzf2vmCrNqH/vz/QiR0atBkRlz3DUSfoSp9RZGxSF8D+quPQtXeNuPigpmIqdcgnNlpmpniTJHHj6I/9xAET2n5IwRiwb2IAWPUA/YZcKb+Wkbp3KaLTHVuAWRTA7LhGFTtN57QS/pDdr66CbdC+hrRn/kJeOsS2sXCe9EGT0j8XinB7wGpg8WOsDtTNq/aoI8frHmdWILlp2HuEr48YjouizVlx1N0jPQ1oj/7UNuHxZM4stBu/wEiOz91xwv4IBoGzYRwuVO2X8X5h5Q6cu27yOXPJ7RrtzyIKB/czbPqHDIagZOqP44sxOlS8Xo4MtCE/s//g+r98UazFe3O/07bCmNP4kz9NfWYkOGI7DxEdp6RoqBIiMjKRVxyHfKtP8cbXW7jgSAB0u9B7lyDXPsONDdBnyFo066H/DJECpzOg776hI4twA5PNf5oSDm33UmgKbFjC8ZN1u+BFDi3MhRA1hxAfvgi1B42Uh4uWoDoO1JF1xTnht+LXL80qVluWAqlA3tM0EN6jiPXvI3cvgoAMeIimDi3dzt3AX9ixxYgGkY2VPXuz9/N9IwzQaE4DaL/aMTMW8FqPzVY0g/txm8ljMbJQBP64ieRS58yIr7RMBzYgv7Uj6HmQErmFIpFO7Qnc3wVXYTe8fctT/P3OhOk1JH7NyGf/184ttf4XdUfQ775J+NmHg50+hiK8xApjd9SMnMoYKw+9QCk9zj6Mz8xHPJQM4SakeuXoD/7U2SS1bdegR7r2N46VUrRaVTkVtErEI4sGDsTMXg8BH1gsoAjO3mkrKkB9nwaPy519CX/QLv+fkQnc2L7Z+XjNFuZXzyAUfZstGiYkMXG0sYqdvkbcZpV1LZbcWSB1QGJHEyTGZGV2/lj+BqRS55KaJJr30FcMMOYg0JxNjhciEHjkJuWJzSLkVNTlq8ppW40Dgr4QGhGykAqzg1OKduQqMumrxG54xOYNLd31pPYnJCVB76GBEaBKOjT7VPqzSjnVtFrECYz5BQYr9MgD21Pbqw9BKEgdNK5dVsd/HDwFFxL/oE4sssYNJm5YdxlRMfOIkc5Od2LKxcx42bkosfiTOKiheBMQV5s0G+kPyRCSmRDNSK3uPPHUZxXCLMVJl2J3Lna0EBvTX45omxQSo4jw0HkoR3GOXKyUVBOAdq8f4HSAZ13oEPNyJ1rkh9/52rE6OnGg2hvIysXcdltyFd/G2cS4y8DV2bV+/R0euHjkUJxBnTkWAoBWscd8M4EZyhA1uu/P+XYAsSi2Na+i2vHJ8jYaZapFClFmEyIIRMQ198PJf3BYoPCCsTCexEXzEhJnjWa6dS/nTlQOqDtw1YvL5pRdCG5RWif/U/EyKnG9cuZg5gyH+36bxh1GamgoRr5ym/adsD01qG/8EjSgt2zQmjGeZcMi63tOdSLEEIg+g5Hu/lBKB9ifNa8UsS8LyKmzEfYUlfMrFCRW8V5iqgYihTCyGVrz6Dx4EhB4Y/3ODRUJ7atfhtGXHxGUWZF6hB2F6LfKGRxPyOH0WTudPpJG07oU4tRl0AsCvVHIbsAkZ2PvuZthLsodcdSnFcIoUFeCVx+O+KS60AAjhyEKTXOoAwH0Ve+SptGOCeJRY3uj9Ou69TxhN2JmHAF8ujuxPYJVyBsqVnRksFmo439ga2gxxD9RhnR0zRGhYXVAX0Go13zdSPHVimpdBnKuVWcn7hyEVd8Hvnu39qOZ+WhXXojonVh2jki644lN4YDqoAgjXTVDU44c9Bm3WpI/jSdinRJqx3t2vuQzhw6vyagOJ8RFlvH0c9zJRIy1D2SIKv2IqJhMHXO+RR9hiAHjoW9G9oaBk9IXXpFwIf8dDFy5WunxsBoenHJtal9oD0HhN0FqpFPl6KcWwUAET1GMBrBrGk4zrDQSQb9RgW6zZmy6EF3Iaw2GDoRUTYAuWUFeOth8FhE+RBEqqKpHcm6mC2gCsp6HTLgQ1/8ZBvHFoBwEP3V36Hd/v2UyI0pFCnHbIHcImPFKQEivzQlaTXC5Ua74vPIhirY/KERgR41DZFXikhR3qmsP9bGsW0Z37QcBlxgFB4rejXKuT3Pieoxjgf9LD6ynd3eWnKtDq6sHEmlKw9XkuiA9HuQR3Yj170L4QBi0HgYPa3HLbkKqwMK+iAuvREpZdKOeee8/7wSpCsX/I3xtjEzQC1H9T4CPjiyM4mtCemtT2mjCIUiVQibEzFlAfLgtkRWxPjLU6bIIFw5hiNbMTQl+2uNjIaR6xYlt69+C1kx1IieKnotyrk9zznkb+SRDYuIntBIPNbsYVtjFQv6juHyPsNxtHtSl81e9CX/gF1rT40dP4LcsAzt1u8h8npmJXiqHVsAkZ2PdsMD6C//CjytoiFDJiEmz+v1HXnOS2KRju0hf8d2hSKNiKJKuOw25PvPGjnjYHRuvPIuyOkhwYtYNLHU2EkCvlOfTdFrUc7teUxTOMjfd61qcWxb8/rBTVxY3D/OuaWxto1j20LQh1z5Ksy+w8gJUwAgCsqN6li/Bxn0G1E7Z7aKGvRWbM7kWrqgZMAUGY2wO2HUJYgBY5DeetA045rlcqcsatvlWOyIARckL1rrO8I4TxW9GiUFdh7jj4Y5nGDJHIzk+31N8blXJ9slJnzPztWGzqeiDSIrF1HSD63fSER+qXJsezOuXMTUqxPbhk/ptHayQtHVCIsN4S5CqxyG1seoQegxji0gNA0x/MLEBVtmK2LiFWrV7DxAObeKpIhEdd0d6b92wdK+QtGTECYTYsTFiDlfMLoRAdgciIsWoM24ST3YKBTdQU4h2i0PwoAL4OR9rGIo2q3fhR5WG6I4N3rO45gi5bjMVvpm5XEwQTtAgaB/drxqgBg2BbluccL9iWFTwN4LO8v0QoLRCE2RIFUBLyahUezIxm1xYOlhqheZiHBkGUu7/UdBNAImMzjdPU5RRKHoqQghIL8M7TP/cmo10eZUD5fnEcq5PY/Jttq5Y8gU/nfDIsJ6225Z1/UfR44lgdaru8hoPrDt47bjLjdiylWp6fKk6FJ8kRDLju7k9YObkScE2y2aiS8MvZjR+eXYetASZKYihDgVuVUoFGlB2Jwqv/Y8RUiZqEXT+YXX68XtduPxeMjJOb9y4mJSpy7o54Nju9nhqSbP5mROxQjKHDk4k0qBeZE1B5BrF0G4GTFkImLoZERHuq6KjGFT/RF+u+X9uHEB/OeEz9DHldvtc1IoFApFz0NGI0anNa17slzP1F9TIZrznJNL0tf0v4BgLIpZM502cidcOUY1bZ8hoMeMnEKh0rd7Av5IiNcPbk5ok8DyY7u5adAETOrvqVAoFIokSG8dcv9m5J71iKw8GDsTcgoNxY0MQDm3CgBMmgmXdnY5galoUavoXiJ6jPoOFC2qA15iuo7JpJxbhUKhUMQjG6rRn3u4RU9YAnLT+4iZt8LoS4wGSWlG3cEU5zVS15G+BmRTPTLQ+2XMbCYzfbOSd8ga4i7GcpYPOanGEw5QH/TjTaIV21OQzV7jd5VEbk+hUHQtMho2zsGmemQklO7p9ApkKIC+/LmEjTLksmc6bqDRjajIreK8RfoakVtXGK0aA01QNhhtxo1QWNFrG1E4zFYW9hvDloajtE+2t2lmLizq1yXd2s4EXyTItoZqXj24kdpAE8WOHK7pdwFDc0vI6kF/DxlsRh7bg/zgBTh+BHLyEVPmIwaNQyidW4WiW5CeWuQnbyK3rQSpw5CJaBdfDbnFabvG9QqCPti7IYlRIg9uR+SVduuUEqEit4rzEtnsRX/3b8gPX4RmL0gJR3ehP/1TZNW+dE+vSyl1url31AzyrKdyo8qdbh64YDYFaZLKCceiLD+2m/+34yNqAk1IjBSJP27/kI+r9xKJxU67j0xA6jpy73rkS/8Hxw8DErx1yEWPI1e9jgz17Gi0QtETkN469GcfRm5aDtGw0W53+yr0p/8HPLXpnl7PRkrjlYwMiZCryG2GI8NBw/kK+sFqB0e2oaOp6BxNDbA/UWGVRC55CnnjN3ttlM1mMjM6r5x/HzcHfzSMhsBlsZGTxhxqbyTIG0kK3V45sJHxhZUUmnrA797fiFz2bEKT/HQJYtzlYEtNPlqba4PFDs4shCM7JftWKHoqUkrk7k8hgX47QT9y4/twybU9qutaRmFzQkl/qN6f0Cz6jezW6SRD/XUzGOn3ID96CbnlI2NZBaB8CNq8LyrZrU4iD29Pbqw7AqFAr26VKoQg1+YkN0M0IL3hINGTv/F2RPQYvkiIwp7QICToN5btEiKRDdWIvJJOH0b6PciVrxmRqZMa1WUD0eZ9CZGrOjApzmPCAeSuNUnNcveniElze/X1vSsRjiy0y29Df+ahU9eekwy7MGP0vVVaQoYio2FjGXPzB6ccWzCWzl/5DTJDkrZ7LLYOlt+FBmkuqjrfOF0Rm7mnSJNpp4kXWDufOyyjUeTaRcgNS9veXI7tRX/p/5C+xk4fQ6HosWgm6Kha32Y3rvGKc6ewEu3278OQieDIgvwyxNy70GbekjEryypym6n4PUZUJhHHDxtLLi53986pFyEqhiKF1vbB4SQnT9hejGxuAu9x5N4NYLYgBo0DV15KNQplwGfkm+7dAEIzjpGVh3DEP1hkWWzkWh00JlBIKLC5yErULS8TcWRBcV+oORhvszkQOSlYcfE3Ite/l9jWUAXeOsjK7fxxFBmHbPaC5zhy30YwW0+ct7kZoy2aCQiLDTFhtvEdJbJPnJMxDlhPRZgtUFiBNvcuY5VTMyFcmRUJV85tphIOGUnwSZDeOkRJ/+6bT2/D5UbM+yLyzT9Da90AdyHa9Bt6tYav9HvQ3/s77F53auzDfyIuWgjjZyd0Ps/+GF5DLqZVm2a54iXEhCtgylVxuaG5VgdfGTGdX25aQkg/9bu3m8x8ecR0clOUp9rVCGc22ty70J9/pG16gsmMuOorSKebTtdpR0JGkUwSZGMNonxQZ4+iyDCk34P+7uOw71SluvzwRcQl18LYWYg0FYNmIqKoEkZPN1Y+WzNwLKJyRHom1QsRVrtRC5SBKOc2U7HajOWV9jktJxDZybVKFadHWGzGhe7zP0buXA3eehg4BlHSv9d/t3L/5jaObcv4ylcRA8aAY2Dnj3F0dxvHtmV83SIYNA5RObzNuBCCvtn5fH/iZ9hSf4QDvgb6ZxcwMq+M/I5SSDIMGfChr3oTbc7nkY01UHsIcgoQ5YPQNyxFKyiHzv6+LFYwmZM+/Kp8/N6J3LO+jWPbMv7RS4j+o0E5ty0IZw5Mvx5xwQzk9pUQiyGGT4HckoyLMCq6BuXcZirOHMTIqfFPngB5JRmTtN2TEVY75JciLlqQ7ql0G9LvRa55J7l9/RIo6YfoRM6xDPqRa95Obl+3CFk6IE5LWAsHKaivYtr2NUyLRsBiRRt+EeSXpUxhoMsJNMHOT9B3fmLMO7cYDlQhV70OgJx0ZecfnlxuxJhLjb9Ve3IKQTm3vQ7p9yDXdnDeblgGsz+H0FQu6UmEI9tQFyodkO6pKNKAcm4zFGGxwdSrkUF/2yhbYQXawnsRKqdOcS5IHULJO7HJZi8iFutcQV0s1oFiANDcZGxjaXXcWBS5Zz3y7b+02VT/9D3EZ74MQyYiTD2gyK91NLX+mPFqTai504cQZitceJXRUW/HJ7Sk1RSUoy38mtHnXdG7kLqhxJHM7PcgdB2Uc6tQAMq5zWhEVh7anM/DtOuRgSajX7MzRy2rKM4dmwNROQKZIGUAQAyegLBYO3+MfqORDdWJjzFonFGx3Bq/B/nePxJuL997wsghzSno3Ly6A5vTiDInadYgcjsvAwYgsnLRLr8dpi5ENp+8NmQjVJFp78TqgMoRsHN1QrMYMhFhVrdzheIk6jEvwxF2FyK/FK3PEERRhXJsFZ1CWGyIKVeBOYEDm5Vr5Nx29hhmC2L87MSFBo4sxLALEe2leJq9EAkm3mEoYCz39wRcuYhLrk9oEqMuSam2prA7EXmtrw3Kse2tCKsd7eKFRq51e7LyEH1VkZRC0Rrl3CoU5xvuIrRbvwt9T3SS0UwwfAraTd9BpCo66i5Eu/V7MGAMIAxdySET0W75bpKCp9NpCPSMXvDCZDKc96u+DPmlkF1gFJRNvwEx7QYl2aQ4d3KLjXPqZDGmZkKMuBjt5hSetwpFL0FI2VGT4PMDr9eL2+3G4/GQk6Mio4rUIaWEpnqkpxb8HkR+mREhzYDuODLoN6KiQhgRVUvnGwzEHSPUDMFm4xh2V1KJNemtQ//7jxLnFTpz0G77zx6lYiH9HkPjt6EasvMQucU9av69DRnwQ7MHWXvI+B3mlRrnYQ9swdod561Ckamcqb/W885shaKHIKUONYfQ//kLCBgFVhKgYhjavH9BZKe38EfYXV0uHyRsTiMP9XRk5Rq6wy//pm1jDc2EmPfFHtWUQHrr0F/5LdSeauQgs3LRrnsAUViexpmdn0i/B/39Z2H7qlNjFhti4degYmiPc3C747xVKHo6Ki1BoegqmhrQX/x5i2PbwuEdyBUvIyOhDt8uIyFkNNKFE+weZCR82s8hNBOiYjja536EGDsL+gxBjLsM7Y4fIvoMjc/RzVBksNlokFHbrkOZr/FEa9yG9EysE0gpCcUiRPUE3fwyHCl1o3iylWMLQCSEfOn/oKk+LfNSKBRdS896ZFUoehCyviqpfI/c9jHiovngLoq3NdUjD243xMctVsT42YiC8oxIZTgbpK8BeWyv0UZaaIixM40mGUkKn4TFCgXlMPNWRDRstBftCfJfrQk0wb5NiW1NddDU0GM0qqWU1IX8rKs9xJbGo+RZncwqH0aRPQtnZxU1ugu/B7k6ieayHkPu24wYf1n3zkmhUHQ5yrlVKLqKjqJCegwSRDNlU73RurXxlIyW3P0pjLwELr0R4cyOe08mIn0N6K/+Dqr2nRrbtxHZfzTanC90qNMsTCYw9ZCmDe2JhmnTzrkdMtDUQ0rjoCrg5WcbFtHcqtXvxzX7uHngBKaWDMJutnTw7gxB1ztW2mhMLFenUCh6Nj1jrU+h6CJkLGZESr11yObUyk2Jwj7JjfYsaFcIImMxo+tUghuu3PpRj7oRy70b2zi2LezfjEw03luwOhLLrJ1A5PSM7mH+SJind69p49ie5Lm96/Amk23LNMwWKOjgPKwY2n1zUSgU3YZybhXnLdLXiFz1GvoT30f/f99G/+cv0Q/vRIZTdOPOKYDivglN4uKF8cvTzV7klo+Sz3dTglbMGYhsbkJuWJrcvv691H3HmYbLjZhwRWJb35HQQ7Rom6MhdngSP0xJYLe3tnsndI4IZw7i0hsTG7PyVGtWhaKXopxbxXmJbPaiv/X/kCtfO9VNquYA8rmHkcf2puQYwuVGW/g1GDLRkO0BsDkRM29BDL8wQR942bZ9a3uiYXqGct9pPkcs2lYRoRchzBbEhNmIi68GywnZM82EGHkJ2pV3IRxZ6Z3gGaKf5ncW1WPdNJPOI8oHGbrDrR8sKoej3fgtJc+mUPRSVM5thiNDzdDcZBQmWWxG+90eknfZGhkOGl2oAj5j2TbdrUK9dXBoW0KTXPoU8sZvp6QbnMgpQJt7F0y/wcixtdqNLlaJCqUcWYghE5Gbk0RoR12CED0gY9OehRhxEfKjlxKaxahphkRYCpB6DHyNxjkC4Mw2oqdplHcSzhy48DNGR7JwECxW47ztQXqkTrOVClcuh/2NCe1D3MXdO6FOIGxOGDoZUT4EQs1Gly9HliGplUJkNAJ+j5Hjq5lO/BZze8Y5qwBO3m+9hja31Q6O7B55v1Uo5zajkb5G9OXPwfZPaClSKemHNv+riARV9pmK9HuRn7xh5JOejNjllaItvBdRkB7dT3l4Z3Jj/TEIByBFrY6F1Z64FW377cxWuPAzyN3r4lUW+gxBFFakZD5djdA0GHExcuP78UV1+WWIfiNTchwZCSEPbEW++7dT35fNgbji89B/dNKGEd2BMJmNtJQeSrbVzu2DL+R/Ny4m1i7KfmnpYHIsPavgTwgB2XnGqwuQAR9yy0fIFS+dKhTNykOb/1VkaX+E1sNUP85DpK8Rfck/YPe6U4Nlg9E+8y9JuioqMhmVlpChyEgIufLVE/qMrZYIqw+gv/QrowNSD0DqMeSWD5GfLm67FN1Qhf78/yLTpTPZ0fKwZjJe6cBdhPbZ/zDyNrMLDGfwstvRrvpKhwoDmYbIKTDagl60ANyFkFuMmHY92vX3p24puLEG+erv2j4IhALI1//Qo4rvMpXKrDz+Y/yVTC7qR57VSb+sfL40fBoL+l2Aq6dIgXUT8sgu5PLn2iqg+BrQX3gEvEpLN9OR4SD6+8+1dWwBju1Gf+1RZLM3PRNTnDNpdW6XL1/OggULKC8vRwjByy+/3Mbu8/n42te+RkVFBQ6HgxEjRvD73/++zTahUIivf/3rFBYW4nK5WLhwIYcPH+7GT9FFNHuRmz9MbKs/llbxcd3vQdYcQv/on+jv/R1930ajvWwifI3INUl0Jpu9yONHum6iHSD6DE3qwIphk40lxTQghDBatU6/Ae3W76Ld9G20cbPS7tjKWBTZWIu+8X30JU+hb1uJ9NZ1+B6RU4iYsgDtlu+i3fzviMlXpsyxldEwcs27JJbdkuifvHXaJhldifTWoe/4BH3JP9A3LEU21iA7ykPOQMyaiXJXLncMnsKD4+byr6NnMrGoLzlpjIhnIrLZmzQFh2gYuWd9t85HcQ74PbDzk8S2mgOGXdGjSGtagt/vZ+zYsXzhC1/g+uuvj7N/4xvfYOnSpfz973+nf//+vPvuu9xzzz2Ul5dz9dVXA3Dffffx2muv8cwzz1BQUMADDzzA/PnzWbt2LaaeJgDfmnDI0EJNgmyqT0ulr+73wNaP0T94/tTghqXIkn5o8++JX76JRZM2MgCQdUdgwJgumm0HuNyIq76MfP330Lp4JrcEccl1ac+PFCZzxrSclVKH6v3oL/z8hI4rsP49pD3LiM52kFoiTKauUQiIhJF1HTzE1h2FSChObq07kPXH0J/7mZG7d3LMZEZc/wCUD+pxS9Q2sxmbWWWwJSUWhYaq5Paq1BSoKrqQcLDtfaAd0u9BFFV244QUnSWtV6x58+Yxb968pPaPP/6YO++8k5kzZwLwpS99iT/+8Y+sWbOGq6++Go/Hw1/+8heefPJJZs+eDcDf//53KisrWbx4MXPnzu2Oj9E1WG1GZNFiQ4y4CHJLINCE3LYSvMc7jIBJKY0bq5RGcU8Kb0zC72nr2J6k+gByw1LkxdegWU6Ju+uayeiDnqxTV356cm6FxWrkZX7+f5B7NxiR8H6jEIUViC7Ky+ux+BrRX/nNKcf2JEEf+ht/RLvhgaTd02QsZhTYCGEUZ8QpRMTTHA0TjkWxmsw4k2nGWmyIwgpkzcHE9sLy9Di2zU3ob/2/No4tALEo8pXfIO74YY/OxVUkwGSG/FJItgpVNqh756M4e6wO4xplcxEcPY1wfimWpgYcmz+ApnqEKzfdM1ScJRn9OD5t2jReffVV7rrrLsrLy1m2bBk7d+7kV7/6FQBr164lEokwZ86clveUl5czevRoVqxYkdS5DYVChEKnliy93gzMp3G6YeYtaNn56BuXwa51kJ1ntGyNRiCJcyt9Dchda43irUgYMXgCTLgC3IUpqdqV7Xu0t7ZtWo52wQzIPVVJ7bM5MI+/HPvHr8a/weWm2V1IuprKCosN8koQE+ecfuPzGV+DoXKRiOOHDaWCBM6t9B5HblxutBEWAjF6OoycmvTBrDkS5rC/kdcObqSq2UuZ083CfmPo48rF0c7JFWYLTJyL3PZxgoiLQJv8mfRE34M+qN6f2BZqRnrrEMq57VUIZw5i6rXIV38bbzRbEYPGdv+kFGeHK4fwZbdRVVzJa8cPcNh/nAKHi/lX3k3fhiqyUlRcrOg+Mrqg7Ne//jUjR46koqICq9XKlVdeyaOPPsq0adMAqKqqwmq1kpfXNtJWUlJCVVXyZaKf/vSnuN3ulldlZeYtNwiLFeEuRH/lt0aven8jVO1DvvuYERVKUNAhfY3or/0BufRpaKgGXwNy/XvoT/03JMuJPVuSOTlgKAy08zNCMsbOPoMJj53VNsc1v4yGBfewKdycmnkpuozT5q4myCWV3jr0Zx9GfvKGIbvmOY786CX0F36esIgwEoux9vhBfr5pMTs9NXgjQXZ4qvnfjYtZX3eYWKIUndwixMKvGSsDJ7G7EAu+2uYBq1s5XV5tONA981B0K6LPUMSMW9p2p8vOR7vxW0kDEYrMQVpsbC8fxEO7P2FLYzWecIC9Tcf59f51fJxXRCRF0oWK7iOjI7e//vWvWblyJa+++ir9+vVj+fLl3HPPPZSVlbWkISRCStlhlPLBBx/k/vvvb/m/1+vNOAdX+hqQi58kUcGMXPU6YuRUYyml9XjtITi2O35nQT9y9Vsw61ZDbqozDBoHW5IUulUMR1pttP7m7SYLr9TsY3v5AC4dcRH2cBDdbGZfNMI/D23mKyOnd24+ii5HZBcihUick2Z1xClPSD2G3Ppx4qLHhirk/i2IMW3/7p5IgOf2rk14/Gf2rGWYu4T8drqkwmKDAWOMpf7mJmN+zmzIyk1fXqvNaTjblcPRhl1ojGkm5L6NyK0fI/JK0jMvRZciHC4YOxMxeDwEvKCZDW3jDMmbV3RMY7iZf+xZk9D28oFNjC/sR1EPacCiMMhY5zYQCPDd736Xl156iauuugqACy64gPXr1/PII48we/ZsSktLCYfDNDQ0tIne1tTUMHXq1KT7ttls2GwZLqgebE6uiCAlsu4oolV0SkodtnbQunXnGsRFCyG7c86tKKpAFvaJzy/TTGjTr49rzJBjdXDzoEn83+YlLDt+oI1tRG4pBbbUCqkrugBXNmLc5YacWzvEtOuhfT5a0G+kIiRBbv0IOXQSwnbq4awpEiScpIAyGIvgjQTjnFvAcGKz8zMnOpaVi7j2PtjxCfo7fzWK2jQTYvAEtBu+mTB9Q9E7EGaLIXunNFF7HP5ImKZI4pbgMalTF/Ip57aHkbFpCZFIhEgkgtauAMVkMqHrhl7qxIkTsVgsLFq0qMV+7NgxNm/e3KFz2yM4XX5sXAcm0XZJLNH2Kci5Fe4itIVfQ4y97FTBTsUwtJv/HZlfmvA9A7IL+eYFs+mXZTggTrOVBX3H8PmhF5OtZIUyHmF1IKZchbj8Dsg68RCZV4JYcI/RRjhOlUSAyRK3nxYS/Ba101yKtJ7S5UnXYc+nyHWLDMcWQI8hd65G//gVw65QKDKK011fTCJjXSVFEtIaufX5fOzefWoZfd++faxfv578/Hz69u3LjBkz+Na3voXD4aBfv368//77PPHEE/ziF78AwO12c/fdd/PAAw9QUFBAfn4+3/zmNxkzZkyHaQvpQoZD0OxBVu2HWARROhBcOYnbQDqyIFGEFMBsQeS1dSSFEDDmUuSWxNFbMeZScKRGu1XkFiMvvQFt4hVGFNliQ2TlkuzyYDebGeIu5l9HzyQci6EJQbbVri4YPQjhzIELZiAGjTMk6kyWpO2JhTMbMW4WctHjie3jL4/rHpZttZFlseFLkN/rtjrItvSQhyC/x3BsE3Fwq6GXqSJACkVGkWW2UWTPojYYX1NiM5nJVyuMPY60Ordr1qxh1qxZLf8/mQd755138thjj/HMM8/w4IMPctttt1FfX0+/fv34n//5H77yla+0vOeXv/wlZrOZm266iUAgwOWXX85jjz2WcRq3MtSM3LkaufjvLZ26JCDGz4Yp8+P6VwtnDtrcu9Gfe/hUBMiwIObclVg7NLcEMWoasn1ObF4p4oIZCSJs545msbUU7ZxpTC3LYocOAnqKzEYIccbau2LAGGTZIDi2p62h/2hESbw+c67VwReHTeXXW5aht8rtNQmNu4dNJdfaQ9q9hgNtu1S1Q3qOIwr7dOOEFArF6XDbHNw9bCo/3/QekVbpUQLBXcOm4lYrjD0OIWUHysXnCV6vF7fbjcfjISena3LiZM1B9L//KKFNLLgHbcjE+PfoOniPI7d/gjy8A5FXghg7E3IK4yJfLe9pbkLWHT0hBRZEjLgYUTE0dS1PFYozRPoakVX7kJuWg9AQ42Yhiirj8rJPEonFqAv5+bBqD4f9DfTNyueS0oEU2FyYe0jjA9lQjf6375G4cxpot34PUTaweyelyGhkNAKxiKHd3EN+5+lCxqIQCYPFajS6SSExXacu5GdV9T72NB2nzJnD9NIhFNhd2FJ8LMW5c6b+mnJu6XrnVsZiyPeeRG7+IPEGJf3QrvsGIknagJS6EQ0ymc/44idjUdB1o1mBQpFG5IlIpjCfWdhelzoRXceiaWg9LHVFhpqNJg57N8QbcwrQbnkQkZUXb1Ocd8hQABqqkeveNfSPK4YhRk0Dd4FyctshoxHw1CLXL0XWHkQUlBurnu7ClOtZ61IS0WOYNU2lzmUgZ+qvqceR7kCPJtT2bMHn6VAfUwjtrLstCZMZ1PVRkQGcqVN7Ek1o2Ew986YibE60y25D93mgZv8pQ1Ye2rX3KcdWARja0XL7KuR7T54aO7obuW4R2s3/DiX90ji7zEJKHXlkF/Kl/2tpSS+P7kZu/gBx9deNVKcUPgxoQqhIbS9A/QW7A7MV0XcE8sCWxPbyQXGata3xhANEYjFMmkaOKsRSKNogQ4FT7Z3trjYSY+lA5BSgXfdv0NSAbKyGrDxETqFq66w4RbMXufSp+PFoGP3dv6Fd/0BcHcZ5i68R+dafWxzbFqREvv0XxO0/hByVdqdoi3JuuwEhBAydhPzkTQi168ilmdAuWpgwh9YfCbGtsYqX9q/neNCP02zlij7DmVY6iJyeUmCjUHQRUkpoqEJf/jzs22ikuQ4YjXbpTZBfaqx4pAnhzDFE/FUETpEAWXs43lk7Se0ho42zcm4NmpuMrpyJCPqh2aOcW0UcKgTYXeQUGstN5UNOjeWXIW74JiToWhSTOuuOH+LP2z/i+ImoVHM0zCsHNvLcnnX4T9cSVaHo7XiPoz/zUyO/VUpAwr5N6E//BDx16Z6dQpGcZI7tSVQpTCtO812or0qRABW57SaEEFDYB+3qrxlPm1KCzZG0ctwTCvDS/vUJbauPH+CqfqNxpSiRXgabIeBF+r3Gkq4zJ+m8FIozRUYjhq6zr9H4/btywZWbEkk6qccMTeeT6QitCQeQG9+HS65JeUW1QpEKRFFl8pbWeSVGC2eFgSPbaGvdftUTwGKHJHrbivMbdeXvZoQj64xE3JtjYfzRcFJ7VbOXMmfnHVDpa0Rf/hxsX2X8HyCvBO3qryPyyzq9f8X5iQwFkLvWIpf8A6Jh43dldSDm3gX9R3W+wjkcQO7bmPz4+zciJs1VS7uKzMSZg7j4auSKl9uOaybEFZ9XwYXWZOUirrgT+frv40zi8tsSa74rznuUc5uhWE5T/ensqNXuGSKjEeSat1sc2xYaqtFf+LmhyamKYBTnQv1R5Lt/azsWDiBfexTxuR9CYUXn9q+ZwX7iIdHmgPLBgICju40Ijz0rQYtqhSIzEDYHjJ0FZYOQq94AXz2ibBBi8jzILUr39DIKoZkMRYTP/if69pWGLKbZgjZsMuSXq9UZRULUryJDyTLbGJJTzC5vTZzNabZQZE9BC0+/x1i+TYSvAdlYk9C5lcFmaKpHblsJgSYYOglRVKFkjhIgIyHju9yxBhprjKhl+RBECgsgZDRsVObvWgv1VVA5HFE5DJFTkLJjnNV8wkH0la8nsyLXLYbLbkeYz/3yI6x2xKQroe8IyCmEA1uMJd7L7wBfg5HPnmbVBIWiI4QjC9FvJLKkv9HEwWpPuWZrb0FarNTnFrJl6AT2NdXRNyuPMbkl5FttSvFSkRDl3GYoLouNzw2ayC+2vk9Dq1wji2bia8Om4U5B5JZo2Hglw1MDlcPaDMlQM3LT+8gPXjg1uOVDZGEl2rX/qjqhtUJGw8gDW5CvPXoqt27rR8isXLQbv41IUEh41seIRZGHdyJf/vWpIpWtHyEd2Wg3fyc9qSXRMDRUJzXLuqOIaBg64dwCiLwS9I3vQ7vfIgPHoQ2d1Kl9KxTdhbA70z2FjOewv5FHNi4mdEIP/uOafby0fwPfGHM5A7ILjJx+haIVSi0hQ5EBHwWLn+Tbhf35Wr+xfKZkEJ+vGMkP+o2l73v/QEsmjXI2WGxGQn4y8krjx5oa2jq2Jzl+CLl+CTJ2mirgLkaGg0hfA9LvSes8ACMy/sYf44tGfI3oS/5hRMBTcYzXHo2vvg40ob/zN2TA1/ljnC1mKxQlTzsQJX0hBZ3z5PHDsGtNvGHvemT1gU7vX6FQpJ/GUIA/bP2gxbE9SViP8cdtH+AJB9I0M0UmoyK3mUrAB3s34N67AXdWHiPdhYbeX0MVALKxuvPLzi43YuIVyJWvxdtyixHu+Nwv2T4/t7Vt4zLEuMshDXm6MhoFTzX6ilfg0HZDzH/CHMSQ8QhXbrfPB0DWHEzeee7AVkPLspNRG9lQDclk4Y7tMX5HZ1DAmEqE1Y524VXou9YRp9OjmRDjLu90npwMBZBr301uX/sOsnK4ioopFD2cpkiQulACVRSgMRygKRIk16bOc0VbVOQ2U9FbOUW+Bjiyq8WxBSDceZ1bYTIjxs4yenS3LmArHYB23TcQWbnxb+ooEhgOpk+fsf4o+t//C3atNeShGmuQS/6O/u7jyFREuc+FUEcRBXl6rcszIRzs2K4nb+vcpeSVIBbe29axduUirr3PyJHtLHqs488eDqbm+1UoFGklJvUO7RG9Y7vi/ERFbjMVmxOcOUk7s6Qql1K43MipV6ONnWk4hRYbOLKSF4cNmQibkhSh9RsNtsRpDjIWBb/HqGQ3WcCZjUiRlqMM+tGXPZ04SrpvI3iOG99lNyNKByTXF3cXdthy+YyPUVCe/BjOHON3lAaE1Q4DxyJu/4FRdIgw9CqzclOTH2d3IoZMRFbvT3z8wRM6HRVXKHoDMhwEvxeiIbDajRW7VNRsdBNZFhtWzUQ4wcOqWRgt6RWK9ijnNlPJykXMutXI2WyHuGBmypw12dwEG5ehf/LmqeKyfqPRrvhcwrQHUdgHWdIP2uc0msxo069HJHCmZHMTcusK5MpXT0XbKoahzf1CwtSHsyYUgMM7k5rlvo2IsoGdP87Z4nLD8CnxUmuAuOy2xJHxs8WZg7hgJnLjsvhjzPospOIY54jQNMjON16p3rfQYPiFyE8XGw9NrXFkI0ZdYkgIKRTnMbKpHn3ZM7B7nbGqdmK1jsnzeoyWrtvi4Jr+Y3lu77o421V9R5PTUd2I4rxFSKn6/Hm9XtxuNx6Ph5yczOl2IkMBZPU+5PIXoPYgZBcgplyFGDTO6F3f2f3HYsh17yYuECvog3bDAwkvgLKpwVBMWL/EcFb7jUKbfj3klsbJO0mpIzd9gFz8RPwx3EVGRX8nJcSktw79rw8mXYYW029Em3xlp45xrki/B7lzDXL1W4YTVjoA7dIboajSiG6m4hjNXuSe9YZeZlMdFPdFTL8RUdK/18thSU8t8pM3DVk6QAybjJgyH5FbnOaZKRTpRQaa0F/7PRzeEWcTE65ATLuux0Rw/ZEQuzw1vHxgI1XNXood2SzsN4bhuSVkKef2vOJM/TXl3JK5zu1JZHOToYMotNRE+07u11uH/uQPkuaGard+L2nEU8ZiEPAa0QCrI6kTJZsa0P/xX8nTK278Flrl8HP7ACePEQkhFz+J3PZxQrt2538jCso7dYyWYzU3Gbq1R3YZS+Nlg4xc0g6q/6WUhmMrdTBbjS51XYD0NZ44hgXhOH86c8lo+FQuuD2rw79Fb0L6G5GNtcYqSk4BorgvZOUZEXPFeY88fgT9ie8nNprMaJ//cWpWzroRbzhIVOqYhSAnBWldip7HmfprKi2hByC6qoVoJNRh0ZOsO5LUuRUmE5xJxDUaTurYAsaNuZPOrbDYYOo1yMM7oKm+rW3a9Wc2zzNA+hvR330C9m04NaaZEPP+BQZekFSAXQjRLekBqXzw6UkIs7VLUh8yGemtQ3/pV1B35NSY1Y52/QPIkv7KwVUgvXXJjbHoaQpeMxOVX6s4U9QV8HzGbO24RWkqOlyZzB1r6aZo+Vi4C9Fu/nfE3Lth0DjEmBlot30fccGMlCzNS11HblnRxrEFQI8h3/wjNDV0/P5ICBnwG4V1CkUnkKGAkUfZyrEFIBxE/+cvDXUVxXlPx6lrwigeVih6KSpyez7jykGMmpawGAlnDiJRE4ezPoYbMW6WkXPaHpvDWEpNESKnADFqKnL4haBpRtFRqmj2JNdVlRK5ey3iwqviTQE/sv4ocs3b0NSAqBgGY2eCu1AVPCnOjUAT7Pk0sS3UjGxIgQa2oueTlWsEDxrjW7gzaGxaFGQUiu5CRW7PY4TZirhoPgwY09aQlYt2/QMpaaUrTGZDR3dIu3aozhy0G77VJQ0fhMmcWscWjNzijjR+PfFLgDIcMArvnn0I9qyHmgPIde+iP/lDqD2c2vkpzh+ikY71pNOl66zIKERWLto1/xa/OlY+GG3Wbb2+2FRxfqMit+c5IisP7covGpHJxlojvzcrLyWO7alj5KJd8TkjL7axBuFwGTmSWXk9pye42QqlA6Bqb2L7gNHxY34v8sN/xo9Hw+iLHjcaZXRVPvUZIAM+Q3dYCKMQS93skiJDzYYOtJRgc562KFBKCb5GwxE1mYyiQ1OKIvU2B9izjA53CRCFfVJzHEWX06L/HYuBxZryvHmRX4p203eMIli/B5FTaKympfG6o1B0B8q5VRg3akcWoqDrborC7jJa4hakpvlEdyMcWWgzbkZ/9iHiWsrmFCJK+se9R1bti9/2JDUHIOSHNNxkZCwKx4+gv/f3E866gIEXoM24GZFX0u3zyXRkfRX6+8/Avs2AhLJBaJffDgXlCdsIy4APuftT5IqXwd8IVruxejHustRoi7pyEZdci3zvyXhb3xGQpnbTirND+hqRaxchNy41inuz8hDTb0D0H2MEAFKEyMo1dNNTtkeFIvNRaQkKxZlSVIG4/n7IKzWKMUxmGDIR7cZvnlukO10ifI016M/8pFUUWsLeDejP/LTjCuvzEOk5jv7MT2HfJlr+YMf2GN+f53j89rGo0bBk0WOGYwsQDiJXvY6+9Glk0N/pOQlNQwydhJjzBaNRCBjyb+MuQ7vybhWV6wHIQBP6oseQa982HFswoqtv/Rm5ey1StZRVKDqFitwqFGeIsNqhbADi6q8jPbVgsRlObRLH1mi/K0joxRb1hRS1H4YTGrd+DzLQZMzJmZNw6VxGQuirXk/cqjjQhNyzHjH+8pTNqycjpY7ctSbx8n80glz9Jlx2e1tdXb8H+fEriXe4czVMvTolf3fhyEIOHo9WPhgiQTBbkDbXeSsH1+PweU48MMUjP3wR0X/0eSdvp1CkEuXcKhRniGz2Ij95C/npopaCHml3IRZ+DcoGxi9RO3MQFy+Md3bMFqO9cYoibLK+Cv3lX7VURUswWijP+TyifcFeKACH4jsWtexr7wbk6OnnTSOEDomEkXs3JjXLQ9sR4QC0/q5CzadaTCd6T2MNIr/zqTnS14hc/ARybytpOnch8pp/S1nDEkXXIdvLuLUm4DPOUxWAVyjOGZWWoMh4MqWJntyzAbnu3baV6kE/8sWfJ9S5FTYHYtxliBu/Bf1GQWEfxNhZaLf/EIoqUzMnX4Ohbdpe7ufAZuSH/0S2d7Q0E3RQDCVcuUYBlAI086ll/0Q4so3vszUmS8f7TEHUVkZCxgPT3naay57j6C/+AnkazWVF13JG16sOOwgKMJ/md6RQZAh6htyf26Mit4qMRIaawVuH3Pwh0teIGDoRUT44pSoOZzUffyNy1WuJjbEoct/GhMv5wpGFqByOLOlnVM5bHYhU3riaGsAbn/sJIHesQly8EFp19RHObMTkeci3/pzwPWLcZUp/9wTCbEabMBt9xyeJ7ZPnxad+OLKhYigc3hn/BmdOan6/zV7k1hWJbb4GpKc2PmKv6HKkrwFZtR92rELanIjRlxp61gkeJkVeCdLmSNwlbOAFp3F+FYr0Ux/0s8tbw4a6w+TZnEwtGUS+zYUjQx7MlHOryDhkqNlwat9/9tTYrjVIdyHaDd9CuAu7f1K6Dh0VWx3vWLdWWB3QBb3QZUfdqPTYqWKV1nPpOxI54mLY9nHb8UtvhNye1Wu+y8krNZQJPnqpzbAYPQ1RMTRuc+Fwoc35AvoLj7T9vVgdaNf8a2paQUfCiXOmT6KKArsd2VRvtENudR2QG99HTJ4Hk69E2Ns5uFl5aNfeh/7iL9qeo7klaLM+q2T5FBlNbcDHzzcupiHc3DK2+MgObh88mclF/bFngIOrnNteSEzXaYoEkUicJiu2DPihATRHw4RiUTQhyLHYk2vc+j1tHNsWPMeRK1+Dy27r/pxQzQwF5VB3NLG9fEj3zucEIqcwueiC2domatvyHlcO2sybYdJc5IGtYLYi+o4w9C/VTbUNwu6CcZcjhkxCHtwKsSii70hDWilJeofILUa7+UFk/TGo3g95JYjifpCTnxpdZ4vNeCV4cAEQ+UrOLRGBUDOBaARNCLJtLkwpSr+RsRhyw7KED7hy9VuIoZMNXeJWCE1Dlg5A+9x/Iav3QeNxo1g1rwSRigcghaKLCEYj/HPfp20c25P8Y/dqhuWWKudWkXoaQs0sP7aLD6r2ENajXJDfh/l9x1DsyEJLddeuMyQci3Ks2ctL+9ez21tLjsXOFRUjmFBYiTtBNFPuWZ90X3L7SmOp3dK97UWFKwcx/Qbky7+ON9pdiMr4KF63kJ0Hxf2hZn+cSYy7LGnOqHBkgyMbkaLc396MsDmMVtH5Z96OWmTnGakB/UamfkJZuYiJc4wHvfYUVkC2ar3bmkg0TE2giVcObGSbpwan2cqs4gFcVDqI3FQs/we8yI3vJzXLrR8hSvrFjQvNZKQtpGMlSqE4R3zREOvrEq9USmBHYzXFGZBWowrKehGNoWZ+s3kpbx7aQlMkSCgWZXXtAX6y/m1qO2od28Uc9NXz0/XvsK2xiogeoy7k55k9a3hq92p8kQSV5R1UmxOLdtx6tAsR5YMRV9wJNuepwcIKowNQmhwK4cxBW3gP9GvVIc1kRky4AjFxbmrzexUZgTCZEWMvQ0yaZ2gtn6TvCLSrv56aRhG9iKpmLz/ZsIgNDccI6zEawwFeOryVP+9YgScVrYqlhGg4uT0YH+FSKHoqupToHYi0B2ORbpxNclTkthexv6meI82euPFQLMrrBzdz+5ALsSXoqNSVeMNBntq9BpngZFhfd5j5fUeTZWm7dC4GXIBc9XriHVYM7ZLc1TNB2F0w8hJEv1FGK1bNDM4shDMnpceReszQwQz6WtQNOnJYRE4B2vwvQ7MXwiGwO8HpVnJevRjhyoGpCxFjZxjOk8UGzmzjN5pCZDRitIcN+g3JM0f2aVsPZxL+gI8X968nKuObIuxuqqM66MOd5Pz1RUL4IiHCehSn2Yrb6sCSqNjS5oQBY2DX2sSTGH5RZz6CQpFROEwWKl15HPInrvcYlpsZaVHKue0l6FLySe2+pPZN9Udojoa73bkNxiIcaW5Mat/pqaEyq10FeW6RsZx7YGvbcc2ENuOWlLamPFuEyQQ5BcarC5ChZkNybNnThkMBkFeK9pkvQVElQku82CJszrYRZUWvR5it4C6CLgrUymYvcv0S5Jq3DaUPgLLBaPPuRuQWd81BU0xIj7HNU5PUvqHuMEPz43WBawNNPLbzY3afUCKxaCau6DOCy8qHkt0uj11Y7WhTr0Xftyk+glvSD1FU0fkPolBkCNlWO7cOnsQjGxbHRXDHFlSQZ82M+5BKS+glCMBhTh6ps5nMaektriEQHRzZnkAXVDhz0ObejZhxs9Glx2KHgePQbv8+FPbpyummHVl9EPn2/zvl2AI0VKE/9zNoUlXwiu5B6jHk1o+NvN5oq2XGY7vRX/x5xyodGUbCaOsJHAmuP42hZn61eWmLYwsQ0WO8eWgzH9fsI5YgCkxesXF9GnahUcTpykVMvRZt4ddV1zhFr6OvK58Hx89lVF4ZNpOZApuLWwdN4rbBk+Me/tKFitz2EoQQTC8dzIdVexLaZ5QNIScNPzqXxcbYgj4JE9AFgiHuxBEgkZULE2Yjhl0IUjf0YU9TyS/9HvDUIg/vNJZoK4YZN5kesjwvAz7khy8kNkaCyD0bEBNmx79Pj0FTA7JqLzTWQukARH6Z0jpVnDs+D/KTNxLbPMeRDdUpq+qXfg801hjnrcttSKxl5RqR6U6SbXVwSWFfltUkXtWaUNQ3bqwm6KM2Uctl4K1DW5hU2Jf8dukfQjNBfhnaFXcaXeqEMHSNlWa0ohdiMZnom5XPvwy/hGAsioYgx9qBAlIaUM5tL6LQnsXs8uEsPrq9zXhfVz4XlwxMi1qCw2zhhgHj2d9UR2O4rWD5HUMuTKiWcBIhNDjDqIdsakB//fdw7JRzL4WGmP9V6D+6Zzi40QjUHUtqlkd2IsdfZnwvJ8d0Har3o7/wC2hVnGdoAn8T4Va6tYpzIBpuu3rQnuNHoHJ4pw8jmxrQX/stVO0/NaaZEAvugX6jOl0QabFYmVs5im1Nx6kONLWxXVsxilxz/AP/UX9j0v01R8OE9VhSu7DaE8rvKRS9EYfZ2uGKcTpRzm0vIsti4zN9R3FhcT8+qt5LczTCRcX9qXDlknuafEwZjUA4ACbLGWudyqDfaG5gcxq5qEkocmTznbFzqGv2Eg540UwW8nIKyLU6U5IDLGNR5LpFbRxbw6AjX/894gv/Az0hR9BkAneB4TgkQBRWtnFsAfA3GuLx7VUnPMfRFz2BNv8rKS8yUpwHmC0daumS1/miERmNIFe/2caxBUCPIV97FPH5H6fkvM135vCNkZeyv6mONfVHyTFbmVoygHyrE2eCc+OkjJHb6uCSvHLcJjN7g37WNBzFJESHaQ7nggwHjYcJqz0l0epEhGIRwrEYNpMZazfXXSgU6UD9ynsZLosNl8VGv+wCpJSnXSaQsaixlL9uMfLITmM5cPJnEEWVSauipd+DPLIbue5dCAcQg8bD6GlJo4RSj5EX8JG7finy0DZjuW7SPERpv9T0UG/2IjcuS/IBdeT+LYhxme/cCmcO4uKrka89Gm/UTIjhF8YNy8ba5BG2g1sh0ATKuVWcLS43YuxlyDVvxdsc2YiC+CKss6bZi9z8YWKbHkMe2pGywrU8p5s8p5uxRf3RkhRlnqTUkcPnKkcxIhwhZ8MyRLOHKeWDWTB6GltiEXIsqYnMyqAfWXvYSP/wNSDKBsHEuZBbhEiRA9ocDXPM7+GtQ1s4HvLTLyuPORUjKbZnY0lREwuFIhNRzm0v5ozyX2oPoj/78Kl2nnVHja5VFy2EiXPioriy2Yu+5B9tZG/k8SPIDcvQbv0eIi/BzajuKPrTPzlVSVx3FHloO0y4Ai5aiLB3srpS15NHmAB6UPGLqBgGFy9ErnrDaJ8LRpOIq75iFNe1J0luYAvRDtq0KhRJECYzTJyN9NXD9lWnDNkFaNf8KyLRb/Fs0WMd68N2kB5wrpzOsQXIE4Iph3YhVp9y7K11R8nf9jHTb/73lHQ2k+EgcuP7yA9fPDVWdxS5dQXaTd+G8sGdPkb4hM75U7tXt4wda/bwSc0B/nX0LEbknXlTEoWip6Gc2/MY2exFf/fxhH3q5crXECMugvYpCo21ifUcgz7kyldh9h0Ii+3UfgI+9Pf+kfAmJtctQoy51NBl7QwWq9GZKUH7SwD6jujc/rsR4ciCiVciRk5FeusQJovRhczlTlicIvJLk8tp213xfz+F4gwRrly0y2+HixYiffWG3JzLnbr2sBYb5JdBcSVi3OVGyo3JhKw5iFz+PFQMS81xzha/p41j20I0AoufRF53n9HhrzM0e5Ef/TN+XI+hv/sY2o3fNrSMO4E3EuS5PfHXah3JE7tW8p2xc06brqY4fwhEI/iiIWK6jsNs6bAepiegnNvzmWBzcocQiazah2iXWydbR3Hav2PnasS064yb1klCzXB0V/L3HNre6SVO4cxBzLwV+cL/xhsL+iDyyzq1/+5GWG1gLTqzYjCnGwZPgN3r4vcz9dozLshTKBJxUj/5bFoPn/G+XW6j4PP4YeSbf0J664ymJYMnoN3872BLT7MIeXR3cmP1fuO62UnnVtYeSt5psf6YsSLTSee2NtCUsHkFQH2oGX80rJxbBQDHAz6e37eODXVHkEiK7dncMmgSg3IKsffQLpdK51aRnERpDVoHqQ7nJAOSGukQUdofcf0DRiQIjBa0o6ejXXdfr9aZFI4stMtvQ0yZf6pKOzsfceUXEcMmKykiRWZTX4V880/gPaHhrMdg52r01x6FWAepRl1KN8gZne5amQJJpY70xRWKk9SHmvnFpvdYX3e4pZNoTbCJX29ZmrQLWU9ARW7PZ+wuKO4HNQfibUIgSgfEDw+bgly3OOHuxLApYG8XbbG7jOXFwzsSv6dv5+WEwJDgEf1GIm/8lpF/q5kMrdsMlSlJJcKVa+Quj7nUcA5MFqVx2wHS7wV/o5H2kZVrPAx00N5Y0TVIz/E2OadtqD+GrK9Ki5SdKB+MRECihJ+yQSkp0BSFFUihGRre7Snsk5JjFDqysGgmIgmkywrtLlznwbVRcXoONtVRF0pclPz83nX86+hZZLVeje0hpDVyu3z5chYsWEB5eTlCCF5++eW4bbZt28bChQtxu91kZ2dz0UUXcfDgwRZ7KBTi61//OoWFhbhcLhYuXMjhw8mW2hWtEc5sQ3Q8wUVOTL8BEvVcdxfBiIvjx11uxJSr4vRkhd2FdtltCbUfxZT5kGKnQrjciNxiRE7BeeHYnkSYTMZnzi1Wjm0HSO9x9H/+Ev3vP0K++lv0p36M/tzDyMbkLVoVXUQ0DI3Vye1HkqczdSkuN2LqNfHjFhva7DuSqsicFU43YuYt8eNmC9qcLyASXXvPErfFwWcHT44bNwmNzw+9SKUkKADY2lCV1HbAV09Y75lFyWmN3Pr9fsaOHcsXvvAFrr/++jj7nj17mDZtGnfffTc/+tGPcLvdbNu2Dbv9lKN033338dprr/HMM89QUFDAAw88wPz581m7dm1Kqlp7PUUVaHf8ELlpuaFgkJ2PmDgXUVBmCJK3Qziz0S69CTl8CnLtIgg3I4ZMRAydjHAXJj5GQRna7T9AbvkIeWCLcWGfOAdR2MfI6VP0amQsaqwEpDlFQgb86O/8DWoPtjU0VKO/+lu067/ZYRGPruvogPkMKu4VZ4Bm6lhLNxWKDOeAsDlg3EyoHIZc87ZRYFY53FgZyUlyjTvbY1htMOJiRGl/9DXvQFM9onwwYuwsyClIyTEsJhPjCyrpM87NosPbqQk2MSC7gFnlQylsv8KmOG8p6GCVIMtiQ+uh6S1CymRZ7d2LEIKXXnqJa665pmXslltuwWKx8OSTTyZ8j8fjoaioiCeffJKbb74ZgKNHj1JZWcmbb77J3LlzE74vFAoRCp26oHq9XiorK/F4POTkdP6JuSciYzGjiYPZ0kbtoMP3hIPGMrjNEd9cIOkxgmA2n/ExFD0X6Ws0Kt83fwCahrhgBqKgT9pSAGT9MfTH/iOpXbvjR4iiirhxbzjIsWYPy4/tIip1ppYMpF9Wvop8dRI9HIQPX0SuXxJvNJmNv0cXFLKdDTISMuT0rPYOG9V0/hiRE8fomnhTJBYlrBtNHMwqD1/RiupmLz9Y+0ZLvm1rruk/lrkVI9LS3TQZXq8Xt9t9Wn8tc2bcDl3XeeONNxg6dChz586luLiYKVOmtEldWLt2LZFIhDlz5rSMlZeXM3r0aFasWJF03z/96U9xu90tr8rKyq78KD0CYTIhHFln5XQKqx1hd52RY3vqGC7l2J4HSF8j+ht/RL78K0PJYeca5As/R1/0BNLvSc+kOtJCBmSCvDNvOMDTe1bzi03vseb4QdbXHebRrcv5w9YPaAw1d9VMzws0qx0xcW68pqvZglhwDzJVkmOdQFhsxjWrC1cBjWNkdZljC2AxmXFZbMqxVcSRZ3PypRHTMLW7j1+Q34epJQMzyrE9GzK2oKympgafz8dDDz3Ej3/8Yx5++GHefvttrrvuOpYuXcqMGTOoqqrCarWSl9f2IlhSUkJVVfI8kgcffJD777+/5f8nI7fnM75IiHAsiiYEOVYHWgqqddOBDPoNJ0YIoxPaaS7mUteh2WPI8piNG1nK5yR18HuN4hGzNTU5e2lC+hqNz2Eyd5gXKPdvhiM74w171yOrL0UMHNt1k0yGzQnJingA4YyPKB/2NbLu+KG48X2+OtbXHWZm+dCUT7MrkbEoNDcBEqyOM2613VUIdyHaZ74Evgbk0T1GvmtJf2RWHppVPQQrFABRPUZTJIQEHCYzjhTWk1hNZsbklfOjSfPZ31RHcyTEwJwicq0OshOkJrZGBvwQPXm/dSMyKGUrY51bXTduQFdffTXf+MY3ABg3bhwrVqzgD3/4AzNmzEj63tO1nbXZbNhs6sIJEIyGOeRv4IW96zngq8NtdTCnYiSTivr2KBFnGY1A3RH0958znCqbCzH+chgzPanovPQ1IreuQK5bZLSpLRuMNuNGKKxIWXRZ+j3IHZ8gVxu5e5T2R1x6E6K4EtGTvt/mJuS+jUajDk8dFJQjpl9v5Am2y9mSgSbk+veS72vdYmTFsIQ53V2KMwcxehpy0/J426Bx4GyrXRqJRVl6LLHKB8CyY7uYUNiXnO7+HOeIbKpHfroEuWkZhEPQfxTa9BsgtxRhTt+tQOQUQE4BolUEt2c+WisUqac+6Oe9ozv4sGoPYT3KqLxyrus/lhJHDqYUOZMWk4kiUxZFZ5iLLSMhOH7ifntsN9izEBPnwMipGSO9mTludjsKCwsxm82MHDmyzfiIESNa1BJKS0sJh8M0NLTVYqupqaGkpG3zAUVidnpqeWTje+z31SGBxnCA5/au5fk96/CdZhk3o6g9hP7U/xiSY1IaHdM+fsVYGk+wDG50Z/ubIUXU7DXec3QX+tM/RVbtS8mUZMCHvuwZ5LJnTrQSlVC1D/ncw8jDaaoEPwdkOIhc8zbynb+C5zggoe4I8uVfI3etMXKpW6NLI4cwGdFwcgH7LkRY7Yip1yDGXQYnl4A1E2LkVLTLb49z0mNIwglklE4S0aMJ89QyEelrQH/pV8g1b0EoYESv921C/8d/Q2PyVS6FQpE+GkPN/GbLUhYf2U4wFkGXkk31R/jJ+neoCTSlbV6y+gD6Mz8xGjRJCYEm5IcvGvfUZm/a5tWajHVurVYrkydPZseOtpGTnTt30q9fPwAmTpyIxWJh0aJFLfZjx46xefNmpk6d2q3z7Yk0hpp5Zs+ahLbVxw/gDQe6eUbnhgz40Jc+nXi5+cguZGNt/HhTA+zfnGhvyCVPJT1BI7EYdUEf+5vqOOxvwNPRd+T3wI5PEs95yT+MJf6eQLMXufadhCa5/PkTjnsrHC7E0HgJopOIkVPTthwuXG7E9BvR7vwx2h0/RPv8jxGX3ZYwum83WbioOF7r+SSTCvuR1UPk5mTNwcTdCGNR9A9fQoZ6xrmuUJxP7G+q42iCe1FEj/HqgY0EYx0EEboI2exFLvl74gDF/s3gre/2OSUirWkJPp+P3btPtTrct28f69evJz8/n759+/Ktb32Lm2++mUsvvZRZs2bx9ttv89prr7Fs2TIA3G43d999Nw888AAFBQXk5+fzzW9+kzFjxjB79uw0faqeQyAWSSreDLDfV0+5K7f7JnSuhINQtTe5ff8m6NO2aEUe3p58+7ojRnSrXU6pPxLi4+p9vHJgQ0tEr9iezb+MuIQKV15cnnKHEWDvcUOdgtzk22QI0luXPNIaCkDQ30a+SGgmGH2Jsfzf3vHNLUYMGNN1kz0DhMUKuWfWHGB4bikljhyqA21vMNkWO9PLBmPqKQU6OxM/xAJwYLPxW0xz/q1CoTiFlJLV7WULW7Gl4RiBaAS7qZvb44aCcPxIUrM8tB1R2r/75pOEtDq3a9asYdasWS3/P1nkdeedd/LYY49x7bXX8oc//IGf/vSn/Ou//ivDhg3jxRdfZNq0aS3v+eUvf4nZbOamm24iEAhw+eWX89hjjymN2zPAdJqiMXsXVu+mFKEZy8yxJGLTifKIbB0UjgnN0OBsxw5PNc/vW9dmrCbYxM83vsd/TpgXrx3ZobMgEh4jExFma8eL7wk+h8gpRLvlQeT6Jcjtqwyd29HTEKOnI9KkX3ou5Nmc3DdmFiuq9vJh9R5ius6kon5cXj6sZ2mFdjRXqz0l7V4VCkXqEEKQZUm+MmQ3W9KTm66duD8mS9lKQXe9VJBW72XmzJmcTmb3rrvu4q677kpqt9vt/OY3v+E3v/lNqqfX6zEJE8NzS9ieoEuQWWiUOnqI5q8zCzFyauJCIUgYKRQVQ5O3vxwyEdopGnjDQV7evyHh/oOxCDsaqyksbfseUdwXmczp7j8qqcMh/V7w1CL3bzK0LweONarI06Wrmp1nzDXoi7cV9IkrxDqJcBfCtOsQE68ABDiyu1RSqavIt7mY13cU00oHAeAy27Ck+HPIYLPREnjvBoiEEAMuAHcRIsl3e7aIUVOR695NbBs7C3rKuX6eI5u94DmO3LfRUF4ZNA5cuQi70lzujVxSOohlxxLXZ8wsG5KeYlZHFgyZBDtWxduEQFQO6/45JSBjc24VXU9EjzKnzwhy21XtawhuHTyJ48HkKQuZhDBbjVa+efFFhGL25yBR9abLjZj3ReLqst2FaNNviKvkj8oY1R0k8O9tOp74GFd9xYgEtyYrD23WZxPekKSvEf3tP6M/8xPkyteQy59Hf+w/kJs+RKZLV9WVi1h476kirJPYXWhXfalDSTBhMiOy8hBZuT3SsT2JSWjk2pzk2pxd4Nj6kRuXoT/+n8gPXkCufA396f9Bf+evyPZpHedKdj7ikmvjx0v6I8bM6NF/m/MF6fegv/MY+tP/Y1wbPnzR+M1sWGJIICp6HQU2F/P7jo4bH5BVkDYNWmG1o027DuI6kgrE3LshTU162pMxHcrSyZl2vOht1AV9/N/mpVzTfyyeUIADvnrcVgdD3MV8cGwXsytGMNRdnPT9MtAEug52V5cJkMuA70QXNCfC3HFukWxqQNYehN3rIcuNGHYhZOUlLV6S4SD4GpE7VxtJ8APHIEr6J1w294QC/GzjIo4nil4Ctw2azKXlQ+KPEQlBUwNy9zpoqIb+oxFlgxA58ceQUkeuew/5/jMJj6Hd8UNEUer0mGO6jj8aQiBOr2cYi0JTPXLvRqg9BH2GIPoOh+yCDmX3FKdHVh9A/8d/JbSJy+9AGzszNccJNht/w+0rjTzpoZOMjnFdIN1z6rx1pVVmrLuRsajx3WoawpGaqPtJ9I3vIxc/kdCm3fafiJL+KT2eIjPwR0M0BJv5pPYAzdEwkwr7UuZyp12qUzbVI6v3w95NxsPzsEnG/baLo8ln6q8p55bz17mN6jFe2r+BxUe2U2zPptSZgz8aYq/3OLk2J98ZO4e8BEvh0u9BHtyKXPsuhIOIgWMNeSV3UcocHen3IA/vNHq7h5oR/UYhJlxhHCNNQtGravbx1x0fx41bNRPfn3jVGWsEJkP6GtGf/gk01SW0i/GXo836bKeOcZK6oI8V1Xv5pPYAZqExo2wIYwsqEv69FV2HlDpy8ZNJU2rILUG7+Ttpa1l8tki/B3lou6GuEQog+o9GjJ+d1vO2O5BSgvc4csNS5O5PwWJDTLjCuG6l4OFB+j3ozz1sPCAnQIyejpj9uV79HSsUcOb+2vnzSK2Iw6yZmNNnBL5wkFW1+6kJGsvuJY4c7hl5aVLHVn/3cdh3Kv9UrluE3PIR2me/B3md7wUvm5vQ338Wtp/K6ZGNNcitH6Pd+l0o7NPpY5wLo3LLuKpyNG8f3krsRK6u2+rgKyOmk58Kp1Dq0IF6hfR5kFI/43bHyTge9PG/GxbR2ErG7Ok9a/i4eh9fHTmdXOXgdh+63nE74qDPWB3pAcjmJvQl/4Bda0+NrV+C3LoC7dbvQUF5GmfXxTTWoj/9YyNqewL5zl+R/UahXXl35x9OpN5m33Fmvweh60axj0KhUM5tb8QfCeGPholJHafJiruDqn23zcEtgyfxmb6jaYqEsJvMZFvtSZc8ZGNNG8e2hVAz+opX0K64s/PLEk31sHcjwUlX4u8/migSR8BH9tp30Zc/b+R5psEBy7LamVs5kotLB+INB7AIE9lWO7lWR2oi1lYHVAyDvYkL18SQiZ12bGN6jOXHdrVxbE+y31fH/qY6xqXRuY3qMTzhAKFYDJvJhNvqwJxiVQnjGEHCehSrZiLH6sCSJuUKYTIjhkw0CskS0XdkWiW6YnoMvanBkAozW8GRhSVZNbS3to1j20I4iP7Bi2jzvpj2dr9dgYyEkateT+x8HtiCrD/WeefW6oDKEbBzdUKzGDLxvEr/UChOhzobehlVzV7+vusTdnlrACiyZ/HZwZMZlFOILYkensNsxWG2ckY93ZI0JQCMG9ulNxrSQp1AHtxG/fX38dzxg2w68CkSyLU6uHHatQzfuwlXqBnS5IDZTOazalN4NgibA+2S69D3b46XWckpRLTT6j0XfJEwq2sPJLV/ULWHUfnlaXH2POEAiw9vZ9mxnYT1GDbNzGXlw5jVZ2jK8su84QDvH9vFoiPbCcWiWDQTl5YOZm7lCNzW9PymROUIZHa+8VDXGpMZ7eIF3d+m+ARhvwf2fIrpo5eN9tRCQx8ykej0GzDHFZOA3LUuficn2bcBQs29U0s36EPu6kBHeOsKqBzeqUMIqx3t4oXoez6NV1/JykP0HdGp/SsUvQ21htGLqAv6eWTjInZ5a8i22Mi1Ojge9PPrzUs5lqqWeB05PSlyiDwDRvF/h7eysbGqRV+1MRzgzwc2sG/wWNB68TNZfqmxhFsx1Pi/yYwYdQnajd9KjT6sIK7ZRGvMQqRFOzEQDfPy/g28e2RbS4OMkB7lrcNbeP3AppR04gnForx9aCuvH9yMLiX5NicagveO7uD5vZ/SHA13+hjngsjJR7vpO4gRF586hyqGGb8Dd3raiOu6Dns3YFr8pOHYAkgdbedqeOU3hNs74hCvptEaoRGnTNJrEB2nA6TqepVbbPwmTjrKmgkx4mIjJ7tVExWFQqEit72KrQ3HqHTlM6N8CE3hIGE9RrEjm20Nx3h53wa+NHIazk62CxXDL0KuW5TYNnJqnD7suXAEkqoSPF+1h76FlfSM8pqzR5jMUNIPbcHXjKVgcUIftgMx77Mh22JjWungpJq9M8qHpjwN4ExoOtH9LREfVu1hTsUI7I7OdeLxhgOsqT3IZwdPJstioyHYTK7NQSgW5fUDm2iKBDt9fpwrwl0Is+8w5LqkBJsDkUYx9GhTPaYVLye0ieOHEZ7j0O5hSwyZiFz5WuL3DLsQHJkh7p5yHFmIkZcgP12c0CxGT0s4frYIkxmK+6ItuMfoDCiEcWyLLSX7Vyh6E8q57SXoUscfCTG+sII/b/uQaKvmBFNLBjKusIJQLNr5m7e7EDFmBnLT+23Hs/IQk+chUuAc7EqiFgBQHfDS/d20ux/hcHWJM6AJjSnF/VlZvY+qdi1lL8jvQ0Wa2i37IiFkkj5oOhJfNMyZNcxNTjAa5c6hU3h+37o2KxkFNhe3D7kQfyQMaVw1FxYbZIijIqIh6KDQLVa9H8vJ1YWTZOUhxs+Od/JcbsTFV/daJ0yYLTDhCuSeT8Hb9tolRk4Fd2d/ue2OZ3dlTBcohSJTUc5tL0ETGkPcxfzvxsVxTsKK6r1UuvKwpKCSVjiy4JJrYfiFRgQ31IwYOtmQA0vR0tjJtqYOk4UL8vtgM5k56Gtgv68Op9mCKQ3C1T0BGYtBLAIWa4eFZ/k2F/eNmcX2xmpWVO/FopmYVTaUvtn55HSBdqKMhI3ONR3oFNtOo5NsS8HSrs1s5o29m+NSdOpCfp7bu46vjpze6WN0N1LqEAmDyZLaRgwmc4ctrbUE57pwZMGU+TB4gnFtCPqNQqfB43v9srlwF6Ld9B3kvk3IHavA6kCMn40oqkhZlzmFQnHmKOe2F7G+7nDS6Nf7x3YxqahfSo4jnNkI53Bk2UCj8MliT6mQ//DcUq6qHEW5K5d1xw/hDfoYU1DOZ/qOojbYRI4lPQU2mYoMB42WnOuXIBtrEBVDYcRFRhFakgeaPJuLi0sGMr6gEk0IrF3QhEP6GpDH9hoarkJDjJ1pNMlIUDmebbFT7nRztDk+WljpyiM7BVG/qB5jjzdBJzmgKuBtyfXtCUipG3/zHZ8gD21H5BTCSa3pFBRtCaeb2IiLMW3+IN5otSdtJmJcG4YhywZALGZse540+RA5BYixM5EjpoAwpSyVSKFQnD3Kue0l6FKnNpi8PWxjuLlFmzVVpCIFIREOk5mYlPx5+0ctY9saq3BbHTww5nJMSsuxBRmNIPesR771/+DEg408tA255m20m74DJR0/0NhP0/XtnOfla0B/9XdQdSqPVu7biOw/Gm3OF+KE7XOsdr4yYjq/2ryUulZav4X2LL48YtppO6idCZHT6MWGkkQpM5LjR9CffQjCQcD4y8vNHyDmfAGGTe50CoDZaidy0QL0hiq0I61629sc6Nf8G9ppihuF2Xre3l1EmjtHKRSK8/bykz5kJAwBHyCNlrJnEGXxhoNE9BgmIcixOhJWu2tCY0RuGZ/WHU64j35ZBSlZ2u0OvJEgbx/eGjfuCQd47eAm7hh8IbYkTpkvEiIUi6IJIxqYjuKobsXfiHz3b9A+Yh8Job/zV7QbHkA4u7/rnty7EXyNxD7zJcRJ8f7qA2jLn0dW7UMMHh/3nhJnDt8eewW1QR81gSZKHNkU2rNO21RChoMQbAak0Qo6iSPsNFvQhEBP0pQxu4esCMjmJvR3/tbi2LaxLX4CUTksJXmelpwCwld9Gd3XiF57EOFyI/L7oGXnYeqidtsKhUKRCtQVqhuRnlrkyteQ21cZS3YDxqBdegPklSISOGH+aIg9nlpe3LeeqoCXbIudKytGcGFx/4S5kaPzy3EdsOJvJ2kkgGv6j8XZQ5bJNiRx0AHWHj/Itf3HxTm3oViUI/5Gntu7ln1NddhNZmaUDmFWn2G9uqWsrK9KmhfJ8cPGg1Q3O7eyuYlYfRXi2n9DrHgZbe+GFo1UefO3YfMHyL4jEjqhuTYnuTYnQ9zFZ3ashmr0j16C3esMlYHB49GmXQe5JXHL4TkWB5eWDmbZsV1x+5lQWNlz0l2CfqhJolWsx5C1hxEpKmKyZuVBVh6UDkjJ/hQKhaI7UOu73YT01qE/+zByy0cnnBEJ+zaiP/U/4KmN216XOhvrjvC7rctbqtqbIkGe3/cp/9y3nuZIvCZnvs3JN8dewcDsgjZj946aSbmz54hnRTpInzCibvGRt8O+Bn62YRH7TigtBGNR3jmyjT9u/QBPgm5cvYbTLaUniVJ2LRIxZjriuYfR9qw35qDH0HZ8gnj+EeSYGUY70c4exXMc/ZmfGF2b9Jixz11r0Z/+CSTIrbWZzVzVdzRzK0a2NKkwC40ZZYO5eeDEHvPwd9rvLgWawAqFQtGTUZHbbkLu3wy+hnhDJIRc/TZc9tk2OayecIAX9n2acF8f1+zjyspRcTdjIQTlTjf3jpqJPxIiJiVOs+W0y7qZxgX5fXjj4OaEtpG5pTja5fr6IkGe3bs2YTHdPl8dNYGmlHW4yjREYR+kEImd2JyCtEgGxSw29JVvYAoleKho9hLbvQ4x6UoSJYz4IiF8kSDN0QhOs5Vsiw1XgvxRqevIbR+fSPFpR9CP3PQBXHx1nIJAjtXBwn5jmFE2mGAsis1kJsdi75KCurNBlxJPOEBTJEhM6uRY7ORYHFgSKSDYnZBbDI01CfYkEMWpKRxV9A5kc5PRiCMcNH47jhyEvWfdExSKs0U5t92AjISQiXqun7Tv34QINkPWKafNHw3ji4SSvudYs4fSJMvNWRYbWT1YU7LA5mJCQSXr6g61GbdoJm4YOCFOqzcYi3LAl6Bj0gm2NBw742XuHoczGzFlAXLlq23HhUDMvjOucKs7iAX9mA9sSmo379lAbMylmBxtJZLqg34e37mS7Z7qlrGReWV8bsiU+NSSULOhK5oEuXc9YsIVkECGyayZKOiC9snnSlSPsa+pjj9t+xBvxMijtWombhgwnslF/XC2O5eFKxdxxZ3IF34eF8UVk6/s9jQUReYiPcfR3/gjVO09MSJgyAS0WbcisvLSOjeFoitRzm13oJkQjqwkIl2AzRXXvtEiOi6EcnRRlXsmkG21c8vgSYwtqGDRkW34I2FG5pUyt2IkhQk6oGkILJqJSBIppx6TS3kOCKsDxl8OZQON7lBN9VDaH+2ihZCXntatQjNBB6sF0u5CtCtu9EdCPLZzJTtaObZgdN17ctcqvjjskrYrFSazcd4kw+ZKWTvorqY+1Mz/bVoCwFB3MSahccBXz1N71lDsyGFEXmnce0TpQMTt30df+Roc2wtZuYgp8xHlg1IiBabo+Ui/F/2V3xi596dGYddapMUGl92WtPhSoejpKOe2GxAmM4y73CgkS2SfOCeuoj3LYmNwTiG7E+QO2k0Wiu29WxjcbXVwUckARuWXoesSh9mSdOk422rnkpKBCQuFBDAqv6yLZ5tehCMLMWCMoTscjRjaomm8aVldbsITrsD05p8S2vUJs7G2c8C8kWCcY3uSLQ3HjNa4rZxbYbUjJs5FHoxX1QAQk+b2mKXXVdX7mFc8gIucuTj2bsQUDePvP4p9mok3Dm6ib1ZeXGqGsFihqBJt7l3GcrPJYnS1UyhO4m9s59ieQm5bibhoASjnVtFLUQVl3UVeCWLyvPjxAWMR/cfEDbssNj435KK4XFGz0Lh35KW4z5PoTLbFjtvm6DAn0qKZmFsxkj7tiuYEcOfQi3BbeoaT01mE3YXIys2IaIzoM4TYkIlx49ExM6CgT9x4INpxEVRzgiIpUdIPMSZBV7ERFyNKB575ZNNIVI9RbjIzq+ogec/9DPuat7GsX0Luy79hzCdvcU1R/w6bSwir3fibK8dW0Q6ZqMajxagnlJJTKHoLKnLbTQhHFkyehxh+EXLXGohEEEMmQG5RUh3SEmcOD46dQ0O4GX8kjNVkotCejdtqVy1o25Fvd/Gvoy/jaHMjm+uP4rY6GFtQQa7Vid3c8c9cNjeBHjV0h3twrnImYcnOJzzrVvSJc5G71iI1DW3oJERWHtYEHcpOl2bjMMXbhTMbpt2AGHsZcucakBIxdBJk5/eYlqdmzcQIzYxtzdvxtsM7KDu6B62obxpmpgCQAZ+hPmF1nNFDo4xGDKk2IcCZk9bubCIrL3kqnGZSUVtFr0Y5t92IsLsMkfmiijPaPhSL4I2EeOvQVg746smzOZnfdzQ2UyFZPSSfsDvJtTnItTkYmXdmaQjS70Ee2o5c/RY0N0HlMLQpVxkaqUqkvtO0aKSWDzrtttkWO8PdJW2KyU4yOq88ad60cGSBIwtR3DMdQCl1bJs/Smq3b1iGNuJiUA9d3Yps9iIP70B+8ib4vdBnCNpF841rQ4IHsZZ2yGvfRe5ZDxYbYvxsGDw+LUWdALhyoagv1B6MM4nhF6nCQ0WvRkiZFiHMjMLr9eJ2u/F4POTkZM4Jv6X+KL/Zsizu6Xt+3zFc0Wd4l7VOPR+QAR/y/eeQW9s5FiYz2s3/jlCi9d1OfcjPkztXsbWxqmVsVF4ZdyRSS+glyFgU/bVHYe+GxBvYXWh3/AiRrSrbuwsZ9CM//Cdy47K2Bs2EdtN3EAke1mRDNfpT/w3t5e8qhqFd9WVEgtWK7kB6jqO/+Sc4tufEiIChk9Bm3pI+p1uh6ARn6q+p8FSG0hhq5sndnyRcVnrj4GamFPdPmXMb02M0hoMEYxEsmolsiz0j1Bhk0G/omMaiYHMYFeEdpGPoUtIYbiYQNT5HltmWXJjf1xjv2ALEouhL/oF27b8hHPFL21JKGsMBmqNhzJpGljmxDqvi7Mm3ufji8EtoioQIRMM4zFZyLPae01zhHBAmM2LYhchkzm3/MYY2aQJkLGYUDYUDYLYay+Bqqbnz+D1GwdX42YjK4UbXNz2G3PIR+uIn4lpay0jIkOJLpOt8eAey7kjanFvhLkS75uvGylQ4aOheO7MRvfRhUXHu+CMhfNEQUV3HabbgtjrR0phW01mUc5uh+KNhGkLNCW0SybFmD8UJnK+zpSkcZGXNPl4/uJlgLIJAMK6gDzcOnEhBGhoAnEQ21qAvehwObTcGHNmIGTfBwLFGekc7miMhNtQf4cV9n9J0Qh94uLuE24ZcmPB7koe2JT941T4IBqDd+wLRMFsajvHsnrUteqSDc4r43JAplKglvpTgStK0oTcjKoYi3YXgaaeMYraiXTQ/YR64bG5Cbv4A+ckbhtMiBAyegDbjZkROQdz2ijNHVu1Hm/8V9PVLkJ++B0gjH3/CFYZkY6i57ZJ+0I/ctS75Drd+DH1Hdvm8kyEc2XHXMoWiNdUBL0/u/IRdXqMxTI7Fzs2DJjIqryyuaVJPQVUlZSine2Iyp6CgLCZ1Pqndzwv7PiV4ohpdIvm07jC/3/o+3jS1rZVN9ejP/+8pxxYg0IR8+y/IwzsSvmeHp4bHdq5scWwBtnuq+eWm9xI/JLSOTBf0MW4+LdEMAVr897+/qY4/b/+oxbEF2O2t5ZGNi6kL+s/qMyoUJxHZ+Wg3fhtxwcwTv0sBA8agffZ74C6K217GYsgtHyI/fPFUxbs09Ev113+PbPZ26/x7HQVl6Mufg30baWn1HWpGfvwKCIGMW9USba8n7TnPHtYUPYv6oJ9HNixucWzBkGb88/aPWtrZ90RU5DZDcZltlDvdHG32xNksmiklkUJPKMDrSdrcHvI3UhdqJicNbWtl7SGjGUEi2/IXkGWD2izzecIB/rlvfcLt60PNHPY1xOVsisrh6KOmIyfMRjZWI/0etKlXI3WJacMSaNfBqikcTNoO2RsJstdbm9ZIt6JnI3IKYOYtiClXGY6qzZF86djfiFz1RmJb1T7j3ElwfYjEYnjCAQ766wlEI/TPLsBtdfToboZdQiQMdUcTmuTaRWjDprQddGYjRk1Drn0n4XvEqEtSPUNFhiAjISONpXo/RCOIsoHgdPcYjW2AvU3H2wRsWvPivk+pdOWR3QPTnZRzm6HkWO18YdjFPLJhMSE92jIugDuHTMFt6bzTGdKjNEfDSe1H/Y0MyO7+JU55JL4ZQwuN1dBuzhE9Rk2wKelbdntrGdNOWzVmz0aOmIL2zE+h1YmtVwxFn3s3pnY6whEZ47C/MekxdniqmVzcP/m8FYrTIMwWyM4//YbhoJFnmwRZfwxR0r/NWCgWZUvDUf6yfQXRVi17Lyzqx40DJ6TlITZjqTuS3BZoMqTBWiFMZhh/udEOurGmre2CmQmj74qejwwFkDvXIBc/0dIGWwJi3GVw0YKkEp+Zxs52v9nWHPY3Ju38meko5zaDqXDl8p8T5vFJ7QF2eWoocWRzadkQCmxZWEydlwKzCBMmoRFr15/+JPlpKjqQucUkTcqwu5CaqY1dkwKn2ZrUUS9O0KZVBn1oL/86zlHWDu8ktuZtItNvwNLqaVVDI9fqoDGJU1Hi6PhCJqMR46ZosRntaRU9DhkOAtJoeZxOzFZDpzTZTScrXlmhIdTMn7Z9hGxXovpJ7QEG5RQxo2xIWjVZM4qOcpZNZkikuZxTgHbjtwxpwW0rjcj7uMsR+WWGXF2KkaEACNHjCwiD0QhCgC3Bd5rxeI8jFz0WNyzXL4GKoYihk7t/TudASQea4G6ro8cWlSnnNoPRhEaRI5t5laOY3Wc4Zk1LafOGHKudi4r781H13jiby2w7rcPWVYTKBhlKEAm6VgXHzUK32ml9OrqjIWYX9ePVBO13LZqJoQmKKfSqfWhJnGHTlo+ITZzTRuTcbbVzZcVIntm7Nm57TQjGFiTWLpZBPzTWoK99F5rqEX1HwMipkFOI0FTKe09A+huRx/YZNy09hhg1DdF3RPrkuZzZiGGTDScqzpaDyC2OG15TeyDOsT3JO4e3Mr6wMq4b4vmKKKxA2pxG4Vh72+jp4Ep8XRTZ+YiRU5FDJoGmdYlWtmyqR+7fjNz6MZgtiPGXI0r6p02N4VxpDDWzy1PLB1W7Abi0bDCDc4rI7SEqDlKPITcsTW5f9SaycnhCxZ1MY0x+H17ctz5hkOvKipE99rqgnNsegCYEti64UFpNZhb0u4DaoI+dnlNLE9kWG/82elbatEXXhXyMmf9Vst/8U5sWkdEhEzjYbyRFUm/j3GqREJdg4khuGWsbj7WM200W7u0/jrz9WyCvtM0xpLeDRPlo2JAfa4UQgolFfTnga+DjmlMPAzbNzJdHTCfPGv9dyXAQufVj5LKnT40d3W3k7d3y71BUebqvQpFmpM+D/s5f4cCWU2OHdyAL+qBddx/iTNIIUoyw2mHaDcZvuHUKjzMH7fr7E86po7QdTziIridevTkvyc5Du+EB9Bd/CUHfqfG+IxFTrkKcpnpcdJF0nfTWo7/wiJGadXLswBbk4Alol9/eYxzcxlAzv9/6Aft9p67BOzzVDMwu5MsjpvUMB1ePdXwP8TfG3UMylTyrk6+NmsEftn7QJgXy4uIBTCrq12NXdJRze56TZ3PypeHTaAwHqA54ybE6KLS5yLM5O/xRy2jY0E6UutGaMoVLb3mObP5Us48brv03cpu9iGAz0fxS1geaWFG1m28U9Wv7BpOF7I9e5rNjZ3L1mNkEhcAkBK6An6ylT6FNujLuGFrZwOQTyM5PUBENOVYHNw2cwJWVIznsb8RpslDizMFttWNOlGrQ7EW+/0z8eCSIvuiJE1q6qV+y7MnE9BgxXwMiFkOaTJiz8tHSGOGW1fvbOLYt1B0xWv5OmN2h9nJXIbLz0BbcY+g1N1SBKxfhLkzqbI/KK2dVzf6Etv5Z+VhT+PAsoxFo9p64Nth7RPSqNUJoyOK+aHf8ANlYA81NiPwyyHKn7bPIWAy5eXkbx7aF3euQ4y7rMc7t1saqNo7tSfY2HWdHYzVTShI30JFSGk5jNApmM7jcaTn3AITZiug3Crk/cUE25YOgh0Q8LSYTQ93F/GDiVVQHvDRHI/RxucmxOHD1YI1x5dwqyLbaybbaqUyQq5cI6a1DrnoduXWF8XRa0h9t1mehqDIlUYsKVy6eaJiH967BabZg1cx4G4+gI/n6qJnktMszE+5CIlfcyREZ47m9azjsb8SimbikoIK5l95InjM3/jPkFiML+iASFI/Epl2HLUkRiNNixWmxUnoGxQKyap9R+Z6Iqr1GVEg5ty2Emxpg1xpMn7xpOEeuXKJTroLBE7CmoZuSjISQG5Ykt298HzEifW1MhTPHSEM4g9bDQ3KKcFsdeNrljAvg+oHjU6YtLJvqkaveMBqkRCNQ3Ne4NhT3TajXm6kIoUF2floi8wkJNCE3f5jULDcuQ1YMzfh8fn8kxPsJ0sdOsuzYLsYUlOM0t/2tyOYm5O51RrMMX6Pxu58yH4ZNTlvhlhg8AbnqdWgvAyk0tIuv7lH50GbNRIHd1asUf1TSn+KskE0N6C/+HLlp+alll+r96M8+1HGV8VmQa3PyjTGXMTa/gkA0SmM4gNvq4EvDpzEopzDhew46XfzyhGMLhoLCstoD/K5qJ15n/AlryylAXv119CET4eTTvyuX2JwvEKsckZLPkbTg5yTnfePrU0SCzbD2HUzLnjEcWwB/I6Yl/0CuX0I0nFiqpsvpaLle6skfXjKMfLuLb14wm5G5p9JzCu1ZfG3UTPokePg7F6SvEf2f/2e0rT2ZL19zEP3Zh6H2UEqOcV6TpPAXgFisx/wW9Q4+hy5l3MeQ0TBy/RJDlcDXaAw2e5FLn0KufvtEoWcayClAu+nfoc+QU2P5ZYgbvxWXBqfoflTkthuJSR1PKIAnEkDXJW6bA7fF0aHygT8SpikSpCkSxGm2km2xdSjbo0udxlAAbyRIVNfJtTrIsdpTtuwoaw5CQ4KlMamjL38ebeG9CTuInS2F9izuGnYRTZEwURnDYbIkzcVqCgd5du+6hL7iYX8j1c3ehEnxltwigrM/h5x2HcSiSIsNc1Y+1hQoUQCI0oHJ/dfCiqRtVc9HRMCLtj5xlNS89h3kqEvaFPh1C2YrkZFTsRzcmtAcGXYhJruDRHXeMug3bsDNTYZerTMHkaQQqbsodmTzLyOm4YuE0KWOw2TFbUvd0qmsO5LkAVeiL3s2aRpOIBqhKRLEEw5gM5nJsdh7Rt5lO2QkZPzNfY1GMZnLfdqW4WeMI8to07xuUUKzGHNplxSwpRqn2crUkoEc9MUX5gJcUjowfhXB70GufjPh9vLTxYixM7v/2oBRh0FhOdrCrxurcPJEJ7s0n+cKg8w/G3oJkViMHZ5q/rLjI5pPRDUsmokbBoznwuJ+ccswYCTeP717DevrD7eMlTvd3DPyUooS5H5F9Rh7vMf50/YP8Z3o1GUWGgv6XcD00kGpWXrcuz657fBOiISM/uUpwG62Yj+D1n8hPdqhBu3WxiqG5pYkPoYjq+tSA5w5iMmfib8wm8xoV3yux+ggdgeyuQmRLNIdiyIDTZCX+G/YVYT1KDX5JZQV90WrOdjWmF1A4+DxOGIx3O2ehaSvAX3xk7B3g/F/gMIK48EvgZJBd+I0W3F2VTvNZPmHYKThREJx55o3HOSNg5t4/9juFjWHfJuTe0bOoMKV22OKWWTAh9ywFLnyNaPYCMCRjbbwHmTpwE47noaW7mzkjtVG3mlr+gw5o9SUTEAIwbiCSpYe3Ul1oG2RY6kjhzH5feLfFPAlL87SY8ZKTxrPK+FwgaP3LOf3FlRaQjdxPOTjd1veb3FswVg6f3rPGg76GuK2D0WjvHpgYxvHFuBos4dfb1mWUG+1PuTn15uXtji2AFGp89L+9ezxHo/b/pxw5Sa32V1Gj/tuRhMCawe5Zu405T4JuxMxaS7ium9An6GQW4wYeQna7T+A4n6n38H5xOkcrjT0NzcJjfc8VRy47Fb8l95oRNvzywhOmc+x+V/mudq9mNopMstwELn8hRbHtoXjh9Ff/jWyg4ewHk9HBU1We9y1QZc6q2r2sezYrjYyZfWhZn6xaTH1idpmZyjy0HbkipfbpiIFmtBf+HnSbotni3AXot3yIOKiBcayd1El4oo70a76CiINOennSp7NyX1jLuOGAeMpc7opd7q5ceAE7htzWWKFntNp4PagXG5F96Eit91ATOp8WLUHPcki9esHNtHXlY+zVTGWNxLk45p9CbevCTTREGomt91S++rag226D7Xm1QMbGZBd0Ok2emLYZCOpP5Ft/Oy0FNdkm+1MKx3MkqM74ueEYFReWbfPqeX4jixE/9HI0oFGEwero8ukgnoy0pEF7kLwJHgIyysx7N2MWTNxadkQfrZhEf2zCrj4koVoCNb5G9i2exVfHHYJWe3Pp2YvcscniXdYfwx8no4fEM8C6a0zuvkd2Ar5JYghk4wiqARKH92BGDjOcOwTXOfE2Flx1wZPOMBbhxKnfDRHIxzw1fWIAhfp9xqObSJiUeTuTxGT5qbkWMJdaHS/GnuZ0cShAwH+TCbf5uLyPsOZUjwAAWRZbMmj9M5sw5lvqIq35RRAGtU4fJEQdSE/q6r3EdKjXFjUn1JnTkq1Yf2REPWhZlbV7CMQjTCxqC/lzlxyU5hS1BtRzm03ENN1jnQQsakJ+gjrUZyccnpCegS9gwKBhpC/TWtcXeoc8iWPENQEm5I6vmdFdh7iis8jFz1Om5tYn6GIMdPTUq1rMZmYUzGC3Z5aDvpPfQcCwReHT8WdQIO2u+lJvcbTgTWngOiCexAv/Lxt9bE9Czn/q1jTVLVe6shhXuUo3jq0pY180eSifgxLtBQaCXVY+CN9jYiSzkftZUOVUah1svgOkB+9jLj6a9BvVHryL7PyEFfejXz7L7S5NpQONLp1tZtTVNfxR0Mk46jfw4TE9aOZhR4FT21ye/X+lB5OaKakjSR6EpoQcco3iRAuN9qCe9Cf/5mRonASmxPt6q+nLWrti4R44+DmNkGVD6v2MNRdwt3DpqbE+fRHQiw+sp03D52SI/yweg/9swr46sjpPTI3vbtQzm03YNZM9M3KZ1tjgidPoMyZE9ekwS40zELDYbZwcfFAih3ZNEWCrKzZR3WgiQJL2xNHExoDs4v4tK5tGkPLMRxuLB3ohQaiYYKxKCahdXjBEVaHIb9SMQS5bzMEfYj+o8FdlHKdRSllK71MR4fSKnk2J18bPYPqQBPbG6vIsdgZmVeG2+o4bQMM6fec0uTsIdqEvRGtsJLYZ/8DWbUXWXsYUdQXUToAS25iWbbuwGWxMadiBJeUDGxZJndbHWRZrGRZEvweLfYOW+OmQlpKBv3oix5v49gaBh35+u8Rd/7YiIJ3M8JqgyETEGUDDf3PQJPhaOcWJ7w2mDUTORY73kjiavczlSZMOyYL5JclV4QoH9y98+mFiMI+aLd939CdrjkIhRWIsoGQRqm26oA34WrhTk816+sOpaSldV3I38axPcl+Xx0rqvdyZeVItDRp/WY6yrntBnQpGZNfzntHtieMns4qH0q03c0wJxrj1v7jcNldLD26kw+r95Bvc3JZ+TDMQiM3wX7GF1by2sGNhBPcWK/pPzbhzTgUi1LV7OWVAxvY11SH2+pgXsVIRuSVJXVyhdUO1lJEF8qdSH8j/5+9946P467z/5+f2V7Ve7UsybIt915iO7ETp5NGElq4EDochFAOuO8Vvt/fwV2AA45ylONoCQkBUoB0J07iOI7j3m2527KsXlbby3x+f4wlazWzsmyta/b5eIQHntmdmdXOzrznXV4vuX8LcvNLWiavchLKglsgqwhhNj5ts6wOsqwO6rNGN1wgA33IQ9uRG1/QbsTl9SgL3gM5RWd0IcqQfhRFQcku1IZDGi720ZwmHI+xrv0wa1sPkpCSWfkVrCibiNuo+u/yIhqv0uSwhlNYDe40PACG/NrwphHxGLKrRStfXwSExab9fkYx/JdtdXBL1RQePbBBt85rGb3u9sVGOD2IxXcgn/qBfqXFjqiZeuEP6gpEePMQ3jyom3WxD4WEqvJaS4rfIPBqyz5m5leMqGw0Gta2Hky57rWT+1lYVJPJ3qYgE/JfAFSpsrWzmY9MWJjUMO80W7l3/GyaettJDGtBMCeiuK12frpnDfv62gjGozQHenns4EZaQ31YIvpyXq7dyUNTl5M/pE/NbrLwobp5VKV4wj3c38m3tr7Irp6TBONRTgb7+N+mdfz16HaCsdQlw/OJDPpQX/o18tVHoLddC26bNqA+8n+huyU9+wj5UV97HPnyr7VernAADmxBffT/ZTQ5MwzSEwny/Z2v8uyxnfRGQ/THwrx2cj//se1FOodas55CWGyI+bcgJi8+rZ8MUNGAcuun0qOQoZ7B1tNg2PRSRAjBjPwKbqmcgmVIO1OZM5uHpi4n13bp99sOIErGI5Z/KFmSKrsQ5e6vwJD2sQxXBipq0nD4cMKJ+IhthaNBSok/Fh1hH7GMVPoIZDK3FwCryUyZO4u/HdvBTRWNuC02JJK4qvJG6wFKHV6dTFefzcFjTesMt7fqxD6WzKhh+POaSSiM8+Tz0JTlBONR4lLiMlvJtTowG5Tm+6IhHt2/gVm5JVybXYojESdhMrM12MeLHUdYXjYB58WYRO3rhMM79MsTcdTXHk+Plq6/B4wGf9QE6iuPoNz50GVnG5oh/ezpbdVJFgH0xyK8cXI/76maimlYn7lwZ5NYdg/MuQEZCSKsdqTDjSldw5ZWJ7hztHPYgHTKQslEXJOeioTAbAGHJy061gN4LHZWVkxiftE4ArEoFsWE22IbVS/mpYSwu6BxMWLcFK0KpJjB6blsLHEvB2QsAgEfxMKaQoLTe9FcwCyKmTkFVezsMU62TMktTRoQPxeEEMwpqGJj51HD9Y05pTgu0vDo5cBFDW7feOMNvv3tb7Np0yZOnjzJU089xW233Wb42k984hP8/Oc/53vf+x4PPvjg4PJIJMKXvvQlHnvsMUKhEMuXL+cnP/kJ5eXlF+ZDjJKGrGKePbaTRw4kB1R2k4X76uYlZS4AglIayn2BNqrREu6ncFjZTpWSlkAv/7tvHSeCvYBW3ntf7WwmZZdgH/ZDCMWj3FxYTePx/dhf+QPEtafEa6smM2PRbRz391DsvPAXZzlcRmkozfu0G+0Yb7Dy2N7UK9uPQTh0UadwM1x8wokY61MolgBs6jzG8rIG3WS0LxripRN7Wd3SRFyqCAQz8sq5e/wsY6mjs8WdjbjmA8i//Ei3SkxalDbFEhnsR+5cg1z/rBZQAFRORLn2w4gU9tTngkUxkW93k395xbM6hMmsTe97M5nadCP9vch1zyB3rdX62YWiWV8vvvOiDZTVZxeSb3frKjh2k5nryidiVcYeXlV5cil1ZtES7EtablFM3FI1BfuZZNLexVzUtoRAIMC0adP40Y/0F+mhPP3006xfv57S0lLdugcffJCnnnqKxx9/nDfffBO/38/NN99MInEG69MLTK7dxUNTVnBN6QSm55YzM6+CBQXj+Pr0leTb9TJHpjM0olsNTuruSIDvbF81GNiCJin2sz1v0hzQZ3nswkRjy2Hs7zw7GNgCKEd3kf/ir6k1MJa4IIyULVZM6dHSHempWghQLg/x+AznDxMj6ydbFTNimM5tVI3zUvMeXh7SXy+RbO46zi/3rqU/DVahQghERQPi7q9A8Tit/cGTh1j+QcRVd6YlsypVFbnvHeSbfz4d2AIc24P65+8hB2xQM2Q4z8hoCLnmT5rl+8A8iVSRu99CffVR5EXSQ861uXhoynKWl07AbjJjFgqz8yv52vTrKTC4p58LOTYnf9+4jJXlE3GaLZiEwvTccr4+/XoK7Znky0hc1MztDTfcwA033DDia06cOMFnP/tZXnzxRW666aakdX19ffzyl7/kd7/7HStWrADgkUceoaKiglWrVrFyZXq0BdOFVTFxVXEtTX1txNQEE7KLsJnMKAbBmstso9yVbei8ZVFMFBlkFbd3nSCUMO4DeurINj49aUlS+4M7GkZsfsnw9UrXCbxDJZmGIGNRCPQiTx6EcBBRVqfJAI2gueiLhugKBzjU30W21UGVJ5dsqwOzQfAgxk/XbqoGiIZ5aXEUExUNSARGmpzUTIM0XZwyXL5YTGauLp3A9hR93stK6nXlc180zOoUgyb7fR34YuExa00DCJsDUT4BefuD2oOpUMCVlT5Hr0BvSj1retuQve2XlXFAhkuLYCyKLxamqa8NgaAuq5Asqx2H0SBvsB+5923jDR3YDFfdCRdpqCrP7uL26ulcWz4RAIfZkvZsaq7Nxa1VU7m6dAJIid1szbQjjIJLuudWVVU+9KEP8eUvf5nJkyfr1m/atIlYLMZ11103uKy0tJTGxkbeeuutlMFtJBIhMmQgy+fzGb4unfRFQrzdfpinjmxNCqfmF47jPVVTyR2WbfFY7Xy4bh7f3fEq4SEBq0Bwf/18PMOym6pUaeprS7n/44EeomqCoXsxxaOokdTDJ6L7pE7GRsYimsLA878YfIqWAOOmaZayBje8nkiQn+1ew+EhOqEWxcRnJy+l1lugD3Bd2YjFd+oDXE8eYsF7tInsMaI6PKjXvA/l1d8P23cWXPVeREYgOwNQ7sphdn4lGzuT7XfHe/KZlqe3Cg3FYyPqSXeFA5SlycQBNJOQ80IsmqwpOpyO41Bef372neGKxh+L8HLzHl5oTjbwuLFiMsvLJuhVfcIBGGk4K+SHiyisYTGZyDGd3+DarJjS09L0LuKSDm7/4z/+A7PZzOc+9znD9a2trVitVnJyks/soqIiWluNNWUBvvWtb/GNb3wjrcd6JroiAZ48slW3/O32wzRkF7HAXpO0PBgO8EbLfj7asIhDvk6aAz3k2pw05payueMY45zZ5A4ZVlCEQokzO6XObZ7NhWm4Hp7ZCiZzat9uo96x/h7kcz/TMrW1M8FsQbYehsPbkHvWweyViCH7iapxnj22IymwBc16+Ee7XudfZ95E/rAbtLA7YepSRNVk5PbXkEEfom4WonyCJgUzAgmpEo7HMCsKthGeoHtQWeNwctV7v4xn7zuYg30EKhroLR7HFl87N2Tnp6Vn6kojmogTUxPYTGbDrPuVhtdq597xs1laWs8bLfuJywSLisZT4c41FGm3mcyp6gGD27ssMFu060M8xbT2RdQevpDIWATip5wFTVf++X4haA706AJbgOeO76Ihu5gJ2cN+I0Z60kPJaJNnMOCSvXtv2rSJH/zgB2zevPmsS21SyhHf87WvfY2HHnpo8N8+n4+KiopzPtYzEUvEeb1lf8r1r5zYR0N2cdKTWX88ypr2Q6xpP0SNJ59Ch4eOsJ+f7H4DVUoWFlQlBbcA8wqreeH4LkOb35sqG/U3VpcXMXmxsSan02uoYyv3b0IsvRcsVuTedyAaQlRORMxeibruL4iJC2BI9rY/GmFdm/FQTkxNcMTfpQtu4dT0sd0FKz6EUNUzOi5JKemKBFjXdogd3S24zFauLZ9IhSvHsAx8uL+LF9oP84pyjEmlVbhMZo4HfRw/+A4WxcTi4hryMq0Jg4TiMdpDPl5q3kN7uJ8qdy7XlDVQYHNjucJv+h6rHY/VTq03H6Smx5vytRYb0/PKDR8y8+1unWX2JYvTi5i6FLn5Zf06uwthkLW+kpChALKrGbnxRfD3IiobYMpSzaxmhO8/w8iE4jFeTGG5DPBS8x6qPLnJpX2nB0rrwOgeWliprc+QYRiXbHC7Zs0a2tvbqaw8LWuTSCT44he/yPe//32OHDlCcXEx0WiUnp6epOxte3s7CxcuTLltm82GzXbhhqWiaoK+WOryf38srDNxiA3Jph7q7+RQf2fSer+BkkKezcWnJy/hF3vXEjn1foHguvKJTMjSi6oLsxXm34zs70qW3nJno9z+BWM3pZxi5L71sH/T4CLZdgS5ay3K9R/VWY/GZWLEMm3vGYYBhFDAdOabSXuon3/f9hLBIZmm3b2tLC2p49aqqbiHtTL0RLR+4piaYFv3iaR1MTWh0x1+NxNLJNjW1cyvhkjTHfP3sLbtEJ9vvJqG7PNn5nEpoQgFzvCc7TBbuWf8LPqjEQ70n7Zkzbe7+fvJyy4bwXVhtsDs65F9nXBwy+kVriyU2x+8qM5Q5xsZCSK3voJc98zpZe1HkdteQ7nnq1pAleGciKsJ+kYYquyLhkioKgx5XhYON8oNH0X9y4+hY0h7UF4pys2fTo92dIYrjks2uP3Qhz40OCQ2wMqVK/nQhz7E/fffD8CsWbOwWCy8/PLL3H333QCcPHmSnTt38vDDD1/wY06Fw2RhQlZRSvvdcZ48HMNK6E6zFbvJQonTy6Ki8TjNVhJSZWPnMbZ3naDIma3bjsVkYmJ2Mf8y8yY6w36iaoIihwevxa6TARtAuHO0oDTYh+zt0IbC3DkpbUKFw4UcEtgOEvQhd6+FZe9Luv/bFAt5NhddEePhtHGesTspheNRnjy8NSmwHeD1k/tZUlKrC27He1KXVfNsrhGn5PujYUKJGCYhcJvt2FI4pl1IND3SPq3FxGLV+pbTNFzki4V0EnagSc/9et/bfHX6dWkL2qSa0D5HPKaVxl3Zac+UqVLSFw0RUxOYFYUsq0PfsjMGcmwuPjnpKvpi2hBlltVBttVx2QS2Awh3Nsp1fwfBO7Rrg8MNnhzt+pCuwbVLkYAvKbAdJBbRNLBv+9z563W+RPDHIoROmRS4LVbjQa9zwGG20JBdlKToM5SJ2cXYDap0Iisf5Y4vaIOO/d0Id44miXcGHeFgPErglBGC02zR6clnuHK5qHdlv9/PgQMHBv99+PBhtm7dSm5uLpWVleTlJfdXWiwWiouLmTBhAgBZWVk88MADfPGLXyQvL4/c3Fy+9KUvMWXKFF1gfDERaoKZuaW8fGIvgXiy65dJKNxUMRlXLJLkbuO1OflkwyKag3387dgOeqMhbCYzC4tq+HzjMrwpfqRmxUSe3UXeWcgBCYcbHO5RlRrlgc2p1+3fhHLVXUnLsm0O7qqZwc/2vKl7faUr17Al4WwJxGO67OtQtnedoNyV3Jedb3dT6crlWKBb9/r31sw0DEQiiRhH/d08dmAjLcE+FCGYnV/JbdXTLmoLg/T3Ire8gtz6qibb5M5BLLodaqal5SbcHQkSM7B0BuiJBvHHImkJ3GRAe0CSG57XhkgcbsTcm2Di/LRlZ/qjYTZ2HuXZYzvpj0Vwmi1cWzaRxcXjx2yVOZSBVobh593lxulrg16G8UpFthxIvfLkwcFz80okoaq0BPt47MAGDvZ3IoBJOSXcXTOLIodnzA81ZsXEstJ63mw9SGSY057NZOaqklqdKcoAwuXVWulGkTmXUtIa8vHEoc3s6TmJBGq9+dw7fg6lzixMmdaSK56L+g1v3LiRGTNmMGPGDAAeeughZsyYwT//8z+Pehvf+973uO2227j77rtZtGgRTqeTv/71r5gusT7A/G2v88XGZTRkn24PqHDl8FDjMvL3rNdNgwrFRGuonz8d3kLfqcA2lkiwuqWJ10/u18qGF4Wzv7g1ZBfz8YbF5J4KgMxCYVFRDZ+afJVOAP/ckKQe4dEudMPJsjn41KSrWFRUM5i1y7U5+cTExUzI1rdwAJwI9PGf218ZFNRWpeSdjqP8545X6blIWosy5Ed95RHkhudO65H6e5Av/q+mU5oiKD2rfQz5/wKB/dTQVDqR0TDynWeRa/6kBQ8AIT/y9T8gN76oDfaMkaga57WWJh4/uIn+U9sLxmM8c3Q7fz26g1Cq4akMGd4ldIT9/Me2lzh4qg1OArt6TvLwtpdSVt/Oljy7i69Mu5bxQ6p2td4CvjL12iTr+LHQFQnw8LaX2X0qsAU44OvkP7a9RGdkBBWQDFcMFzVzu2zZMsPAIxVHjhzRLbPb7fzwhz/khz/8YRqPLM2YzMQKyil+4tt85OoPEKycigo4gj6yXvgVsfJ6YnYnQws/fdEQzzfv4t6yBhptbkyBPlS7i2Ykj5/cT180nLYSSyAWoT8WpisSxG22amXUFJk4MWEucssq43X1cwydw5xmK7MKKhnvzSeSiGNWFDwWO9YzDImNFqfZSmNuGTtSZG+n5hm71eXaXdw7fjY3VjaSUFVsJnPKz+2PRfjToc2GIXRn2M8xf/fFkWoJ9iX3RA5Brn0KUTNtzI5JuTYnuTYXdxXVUKWYUUJ+Ek4ve2MhXuo8rmv5OCeCPi3zbIDc/DJi2jIYoyuWLxo2nNIGWNN6kGvLJ6at/Hq2DLSVyP5uSMQ0BzCHF2FN/beVIb/2d+vv1jKsruyM9mwaEKW1qR+Vi6pTuiOG4zH6Y2E6wwGsp6SbsmzpbXk5n0QTcV5q3m1YpQnEo2zqOMa15RMNddnPBpNQKHfn8OnJSwdbyVxma9ruZ6pUeaf9iGGbWkxN8HLzXu4ZP0vnCprhyuLiNwu+C4iqCU56cih3Z+N++vskFbS8+fTWzcSeiGMdcmMNJqJ8unIqpW89g+no6RtyTnYRZTc8QFskQGkafMt7IyEeO7CBrd2np7vzbC4+O3kppUZ6nNkFUD8HmjYkL3e4EQtuHVGD9nz1HDrMVu4aN50Dfe06E4vFxTUjBp1Wk5l805lLjNFEXDfUN5Qd3S1MSxFEn09kd2rJOyJBSGHhfDZkWRz8c81srM/+FLpOGxrML61j9vUfwZ6O7HuwXzeMOIia0LQsxxjcBmLRlMONEq0Pt/AiWC7LeAx5fC/y2Z8Nfl9SMSEW3ApTlxm2lkh/L+qq38Ipq2oJkJWPctvn31UtBOcFpxc59ybEO88mLzdbYcV9ht9HfyzMqua9vNS8Z1Ctxmm28omJi421vC9BQokYe3tTa6Xv6GlhSUld2gwE3BZbeh6MhxFJxNnZY2y8ArCvr41QPIrlclEuyXBOXB6PlJc5ZqGwtr+TA4tuw7/8g5plZkEloYW30XLTx3ms/ZCuB8iLQunmV5ICWwB628h97udUp+FiGVXjPH98J1u7myl3ZTMnv5KJ2cX0RIN8b8erdBuU2oXTi3L1+xC3fEYzeMgvR8y/BeX9/4TILjzjPtXzpEJQ6PDyjzOuZ2X5RMqc2dRnFfKZSUu5rWp6Wi6gihAjZhZyL9aw0Jl6fdPglmMO+3WBLYDSsh/ra4/DCEYgo2YkO2TQhsvGuosz/GZso6gknE2ladT4upBP/1fyg4iaQK59SnMBHH4MsYg28HQqsB2krxP1z/+J7NdbbWcYPT1I3i6roe+WTyMrJ0F+GbGpS+l+75d4IxYkZmDtvqenlReadyfJMAbjUX648zXD6+iliFloFbVUZFnsmC+DXlWzUPCO8DncZhsmcfEfNs7XvTCDRiZzewEwKQpXldTyza0vUurMYt6clViEYEugh/0H1vPAhIW6i4orGoamjcYb7OvEEeyHFL2ho8UXDbOvr51/HD+XrNbDuA9sJ+rJxV83i+d6T9Ie8hkGbcKVhaibiaxo0LJqNucZBc67wwGa+trZ3t1Mrs3FgqIa8myulCoOZ4siBAUOD++pmsq1ZRMxK0paS8xeq53lpRN45uh23ToBzMw/fzrJIyGyCpB21+k+1aFUTkqPBmTQpwtsBzm0HUL9YB9jcO/0QnYh9Lbr1+WWgGPsA2UeS2pL6zybK+UNUQZ90NOG3PkmJOLISQsRBeVnnNQeDVJKTWUkVUb5rWeQJeOTs4VBH3L3W8Yb9Pcg+zoQnst7kO1i0hr08bvm3XgtdhZNXYRXsXAo4mfTwXcwC4Wp+RXkDan2+KIh/nZsp+G24lJlc+cxrq/QO2xeargsNlZWTOJne9YYrl9eNuGyKOVbTGZWlE1MaWh0fcUkXGd6mD5PSCnpjgTY1XOSPb2tFDu8zCscR67NmbY2vQwamb/mBaLA4eGO6uk8eWQrT50aSAKYX1idNGQ2gBKPoY6gDysCfSnXjZa4qvKZkgnkPvMjLUABrEDu5lXcft2HORGPjfh+McqApiPUz3e2r6J3SGbq5RN7+VDdPOYUVI0qYzZaTIoJjzX9F2BFKCwsqqGprz1J0m3ADjnHlp5BiLPGk41y+4Oof/ru6YEygKwCzQ45HSoGwf6R1ibv9xwRriyUWz+L+sdvD56LADi9KLd8RpuUHiMeq52PNSzmezteSToXXWYrn560xLBtRgZ9qK8/AXtOa/yy921k+QSUGz8+9h5XNYHsTK30ga9Tk0UbSiya2lUQwNeVel2GM9Ie1s4/XyzM822HktZFZYKYmnxdTkhJVzj1kJLRw9SlSp23gEVFNawd9rlvrZxCURoeMC8UJU4vN1c26h46lhSPpyYN8pPnyslgH9/eviqpH/j547v55KSraMwpuSzaVy4XMsHtBcJptrKkpI5Z+ZX0RUNIwGO14TJb9V7aoMmCmS36G9spxAj9hzKR0AIEqWpZ1RSWn16pYn3zz8nBhLYFXKt+R819/3eUny41oXiMPx3akhRMDPDI/neozyq8KH2OA0gpNV1VqYLFPmLAnm1z8sCEhXRFAuzra8NlslGfXUiW1ZHWAP1sEEJBFlWj3PcNZNsRLfNZXI3IKUlb9k64s1IP2CimtNlfivwylA/8E7KjGbpOaC0vBeUpNZfPhWKnl69Ov44TgT6aAz0UO7KodOek7MuWHc3Jge0AzfuQh7cjpiwZ0/EIk1kbYBreYjBAfpm+ZcNi0/5LoSAhcsdW0RkrUlW1QUcpNdtaA5viSxnDWYNTOM1WnQa2RVEodWVxzG/cDlLnPXO71qWCx2rnznEzWF7WwJ6ek5gUTTs9y2q/aMOW54LLYmNFWQNzCqrZ09uKqqo05BSTbXWccXBNhvya7bRiTstD9QD+WJhfN72tG3STSP5n71q+MeumjCtmGskEtxeQUDzKG637ebP1EAmZYEZeBTdWNuIy2/T6ga4sxLRrkJte1G+oqBpSXIBlfzdyxxvI7a9rGZ6aaSgLboXsQp0YviMaQW1uMj5YNYG5sxlyx+Y+FYhFUmrQSiT7+9ovWnArA33Ipo3a3zjYD2V1KIvvhNwSRIqy1YB+abVnbAoE6UQoCmTlI7LOU0bCmQXlDdC8V7/vxsWQhvL84Pa8eQhvHoyflrZtDifH5iLH5qIxd+TBKxmPIbe+knr9llXI8dPHrMEr6ucg1//NMFhVFt2pWVEPxZ2NmHUd8u2/6jeWXw4X8dyU/l7k7re0v1s4CJWTUBbfDjnFZ7TQvlQotLsptHsGM7hDubFiMlnDgnW3xc4d1dP5/s7Vutc7TBYm55act2M9H7gsNlwWG2UjBPmXAw6zZj5RPMrfp4wEkW1HkW/+GTpPgDdPG5KumKiZG40RfyzKUb9eVx00FYfWoC8T3KaRS787/AqhJ6INab3YvIdAPEI4EWdd+2G+tfUFOg1KWsJsQcxeiZh5LQy9KVRPQbnl04ZPlNLfg/rUD7SbXtCnlYv3rUd99P8Z9zKO0PYAIGJj1/1UkcgRNGjDiZFbH84XMtSPuup3yNW/18q48Sgc3YX6+/8P2o9elGO6VNHsLx+A8TMY1DlWTIipy86okHFZoyYgOoK+biwCahqGQrx5KHd/BXKGZFwdbsRNn9Qyt8MQJjNi2jWI2TckXxsqJ6K85+/T0gt8LshAH+qzP9OCA3+v9ps6tFW7/qTq2b4EybY5+Vzj1dR5T1fHLIqJmysbmV84zlDaq8qdx9/Vz8c5JLtZ4sziS1NXkHexWpYyjBqpJpAHtyL/9B1oPaydu90nkc/+DLn5ZeQIlsGjJXGG++1wU4sMY+PyeJS+Atjb22qYCQjGY7zSspe7xs3U9dsIVxYsugMxY7mWBbHawOlN2UcpW49Ap0ETfSyMXP9XTcZmaCBidWjySn0dhtsTxdWj/XgpcZgsKYd4AOqzLlIJtb/HWB9WqqivPopy50MZz/IhCE8uyvUPaA9N0TDYnJpb0AiB7YDNrT8WQaBJ/5xJDq4/GqY/FiGUiOI0WXFbbHhStNWcKzIa1j5HJKS1/zi9hqVzYbUjGuYhj+8x3I6onQWOsQcuQlGgqBrl7n/QJM/UhOaANYL1sHB5YeGtiGlLtWuDxQZOjz7LO0ZkPKa17YQDWnuEw5PS9U72tMEJg0pQIo76+h+0h/I0H9/5osDh5lOTltAfixBV4zjNVrKtjpQ9kU6LlbmF1UzILsIfi2AWJtwWG940n7sZzhP+XuRrjxuukhue1ypUY/wuB84hoxY9AZQ5s8e0/QzJZILbC0A0EWdDR+ps4NbOE1xfPtnwxi8sVi0APUMyRkoVueft1OsPbUOEA9pNcGDb7mzE8g8hn/wewx2+xOT0lJs9VjvvGz+H725flSSTAzAjr5zsi9SPJ4/rS+yDdByHSFib4M8wiLA5YJTfVyQRo6mvnd82rcd3auAs1+bk/gkLqPHkGwYJnWE/fzq0ma1dJ5BIBIJZ+RXcMW562sp10t+LfPNJ5J51pyoXAmpnaPJ2Br29onoy0kjFwe5GTL86raV24co6q9+cMI/u2nCuyFPGGnLjC6d7/0tqUW54wFj27+DW1Bs7vld7KLpMgls4XZ4fLSahkGtzkZvJ1F5+hAPGijMAUkX6OkcldTkS2VYH76udw3/vfkO37urS+rQ/xL/bybQlXAAUIbArqW+CVpMZZcwuNmJIRldovXdF1ZrwOGhBrcE+RGktyvu+DhUN2muyCxHX/R1isUGv3zlS6c7hazNWMim7BJtiJt/u4v3jZ/O+2jkj6iqeV0YaghIClHQbzL67aA/5+fGuNwYDW4DuSJDv71hNp8FNpDcS4vEDG9nS1TzYxiKRbOw8xh8PbcaXBjMKGQ0h1/xpmPSWhAObUV/6jTZIMgzhyUW560uI2ddr2VSbAzFlCcr7/xG8F2/q+nwj1QRy9zqtxWnoUOvJA6h//i7SaHhqpKy82aL9rjJkuBQ5k0qBaezDdEIIGrIK+fLUa6n1FmBTzBQ5vNxfv4AbKxqTWloyjJ1M5vYCYFZMLCubwKau44brrymtxzPGvkUhBExdoll3NszVJudjUcTcG7VyoZSGmUhhtUFJDcotn9Z6CBVT2nv2rCYzle5cPjZxEZFEHAWB12rXD9FdQER5PVII7e8ynPEz4CIqOFzuROKaOYhRr3VCqrx+sok7x81Iyt4G4hF2pHAV2trVzK1VU/GOVZUh2I/cm6K6cXSnphpiUHIX3jxYdDti5gqtwOFwaVnTywwZDWsWv8f2QDSIqJioDc0YVSj8fcjhDl0D9HUie9oQ7mQ1DlE3A/nWU4ZvEZMXZX5TGS5dHB5NT7v7pH6dzZk2xRa72UptVgGfnrSEqBrHJJQzXtfC8Rh9sRB7e9sIx2M0ZBeTa3NmMr1nIBPcXiAKbG4WFI5jXfvhpOXjPflMyS1NS6An3TlQUIH61H/BYPYLbQht+QdT9u8BWpb2PJcMnWbrpfN06spGXPt3yJd+lbzcnYOy5L0p5dMynJmIGuP4CNqeR/q7iaqJ5OA2hawVaOewkU/82R9Y0PhhZmA/QR8ixWS7MJnBffkaI8hoCNm0CfnSr0m+NjSirLwfMXwyPh5NXaYFbZq8oiF5mTsXcdVdyDV/Sl6eXYSYcyMiTYYtGTKkG+Hyotz4cdQnHk52ClRMiJs/Be70JnxcFhsuzpzQCsVjbOg4wu8PbEhKFUzNLeODtXN1yh0ZTpMJbi8QfbEQBQ4PH29YzM6eFmJqgkk5JcTVBId9XSP2FMp4VOtXM1tHDLpEyI/6xhP6FUd2II/s0HzqL2K2VMZj2oXDZE6LuYARcTVBKB7DpCgjBtLCaoP6WYiScchdb4GvG2qnIUrrtEzdRUaqCS24UEyXzRDOABbFTIHNTbtOP1mjyOHRaYWeSUMzLU52Z2iBEVeyDE9/j/5BDuDITuSe9TDrWsTQtiWzZUQt3SRlh1MImwOmLEVUNyJ3rtW0buvnIIrHZRzTMlz6FJSjfOhfkYe3Q/M+KKhE1M/WqhsXyVyhOxLg0QMbdMu3d59gc+cxlpXWX9R7+qVMJri9ACRUldUtTbzdfhi7ycKErEJMisJTh7fii4Upd2UzIadI138q4zHo7UBuehHZegg8eYi5NyLyyw3NBuROY9tEALnxBcT4mWl/Ah0NUk1opcwtr2iT504vYvYNiOIqRJpKleopl6DXTx5gV08LLrOVa8snMs6Tn3JiWVgdkFeGWPJepJSXzEVC+jq1fsemjWCxImasQFQ0XDSJp7PFYbZwU1Uju3r1JT4BrChr0A2UucxWajz5HOrv1L2nLqsQtzkNcmNOD1Q1ai0IwymsTI9V8SXKiMOmm15CNMyDoW5rAzrbG5/Xv8HhQeQZawQLuxPsTsTV915Sv6kMGc6EEKf0wqdfg5x29SVx7q4b5hQ3lJdO7GFGfsUZFWjerWSC2wtAXKr0nSp1hBMxnamBPxYhoeo18GTrIeSfvqtJAwF0tWgZ2GX3QuMSLfs48FpVRfYbC0QDmsTQGXT2zhtdLaiPfVMrdZ76tzy+F2ZeC/NvHbWN70i0hXz8+9aXknRz9+/uYGFhDXfWTDd2gRvCpXAhA5B9HaiPf0uTXxpYdvLnyOpGlJUfuWwC3BJHFh+oncMThzYTO3X+2hQz99XPI98gQ5prd3H/hAX8z961SULn1Z487qubl5YLuLC7UK69D/X5X8CJ/adXFFRoFr9XsjrGSNeGsP7aIExmmLUC6e+GvetPr/Dkodz2uVH1IF4qv6kMGc6WS+HclVLSHUndGhSIRUfUkH+3kwluLwA2k5nJOSXs6W01XF+XVYDNlFx2lf5e5Iu/QpbW4ptxDVGbA7Oq4t29DtObTyJqpoH1tDSJUBRE7Qzkgc3GB1E+Ycw6feeCDPlRX3n0dGA7dN3mlzX70jEGt6F4lD8f2mJoCPFW+yGuKZtwxuD2UkDGY5pb2pDAdpAjO5HdJ9Ma3MqgT+tDFQrYXWltf3BarMwvHMeknBK6wwGEEOTaXGRZ7Sm1QgsdHj7VsJhAPIIvGsJrdeCy2MlJ43EJbx7KLZ+BoA8Z6NOch5xZabXZvCQZP93YRhigtDZJInAA4dKkAsX8WzVDGLMFaXPqBskyZMiQfoQQTMurYFOn8SB6bVYBdlOmjz0VmeD2AhBT49R6C/BY7PQPkUYCMAuFa0obiKpxHAw5UcMBAjOWsyWngL+2HaQ/FsGimFhc08j1kxbg7WnV6e6J8gnaUNlwmR7FhLLw9ovj8R4JQsv+lKvl8b0pS5yjJRiPsjPFpD3Atq5mKi6HG3LIj9z7Tur1O9YgyyeMOasg4zFoP4b6yu80TV+Aiokoyz+g2aSmKWthNZnJN7kNM7WGxxXoI2vba3g3v0yJmtB6s2etRE65Kq1BvRbQehAG7l9XKqKkBunNB9+wtg8htAFKgwcIGQ7A4R2oa/6kXVNMZkTDfOTC96RtejxDhgypqfMWkGNz0hMJJi1XENxePe2MswrvZjI6txcEgdNs44EJCzRlhFMWpjWefD4x8SpsBhq4CYuV9Vm5/L55N/2nhjpiaoLVHUd4xNdGwMA/XnjzNJej+tmnNW2LqlHu+SrkXiQnsKEIoUmuJP0g0xFIiRG3o1wCJaZRIQb/R8PuTs62p+tz9LahPvEfpwNbgON7UB//d82K+CIgo2Hkur8g3/6LNnQYj0IkiHzrKeQ7zyFHUFPIcGaEJxflvV+GhvmnNT0LK1Hu/qomgTQMKSXy8A7k8784/bCciCN3vYn61x8jjaoLGTJkSCu5dhdfmrKC2fmVKKfuDZXuHL487VqKHZdHi9rF4pwzt/F4nNdee42DBw/y/ve/H4/HQ0tLC16vF7f7Cp46PgdMCDZ1HuPF5t0sKBrHxyYuQgAnAn08cuAdajz5fLB2TtJ7es1W/paimXxHXzu+cTMwGn9JeHOJXP0BlIW3IVUVbA4Uhxt7Gp/wEqrWQ9wR9hNOxCh2ePFY7cbqBHYXVE5ElNYiimuQvq5TkmMqcv1ziMoG/XsAVar0RkJ0RgKE4lGKHF68FjtOi34fTrOVabllbO02sB4GpuYZZ+hUKemNBOmKBAjEIhQ5tX2cjSvRaOiJBOmOBOiPhil0ePBa7cZtEnY3YtICiMcQ46dpPdRmK8JiQ92yChqvGnvWNhpGffuvp/u4hxL2Iw9sgZkrLnzPWdCH3PG64Sq5bTVixgrILtCvi0W0957Scha5xdrA4gjfoS8aoi8aoiscINvmJMfqTLukTn8sTG8kRFfYT5bVQY7NmfbBDxnq13Rrezu0fmFP7siqBO5sxFV3IubcoH3/Noc2HGb0t/L36iW9Bmg9oj0EXSb93xkyjBZfNHzq2uAny+Ykx+q46ANb+Q43H6qfxx3jpqNKicNsuSza7C425xTcHj16lOuvv55jx44RiUS49tpr8Xg8PPzww4TDYX7605+m+zgva0KJGE19bYQTMVa3NLG6Jdl//XB/J6FEDDenT9iAGjfsIR2gJdRP2bDsbTQRZ29vG/+zdy0RNQ5oecBryydyXfnEtLiBxdUEB/o6+O89bxBOxAeXLy2p5ZbKqTphaWF3oay4D3X1Y5rb0QAOD8pNn0S6snQ514SqcsTfxY92vZ6kbzq/sJo7xs0ga5jotcNs4Y7qqez3tRMY1tu7rLiWbIs+cFGl5Li/m//a9Rr+IVnBGXkVvG/87LQEO1JKTgR7+a+drw0OFAJMzi7hvnr9kJQwW2DGcuTGF5K1ii02xIr7IKd4zMdENAwnDqQ+5iM7EFOXgsFDxHkl2J9ag1ZNaENPJAe3MhxE7t+AfOXRwWBdKibE0ntg4nzDUntX2M9Pd6/hWOB0606Rw8NnJy+jME3KHd3hAL/YuzZJ+SHP5uJzjcsodqYnIJT9Pagv/hKO7dH+DZpG8x0PIvLL9a+PR5HNTci//jdysDVKIGZcA/Nu1g/TxSL69qah22s7giipSctnyZDhUqA7HOBne97kiP909arA7ubvJy+j6CIPm9pNlkx/7VlyTm0Jn//855k9ezY9PT04HKeDgNtvv51XXnklbQd3pWBVTGQZBFgDeCz6IRuLYhqxYO82yLZ0R4L89+43BgNb0G56LzXvYV9v2xmPMxqLEE8YZPSG7eOHu15LCmwBXj95gC1d+sypTMSR29+Aw9uTV4T6Uf/6I8SwXiKAnmiQ7+14lYSqMjOvnMUFVZQ5s3m7/Qjr2g6jDpvslqpK/oFtfK1qGrcW11LtzmNydjEPVs/gJn8/LgO91Z5IkP/c8WpSYAuwpes4r7bsI26U2TxLeqJBvr/j1aTAFmBX70mePbaT6LC/IYA8eQi57TUYOgUbiyBf+CXiDBa0UqpaP+1ImEwjSl4JT+6ZrSjPBwPnsxBQOUlztKqYyGCbhlFVoLcN+fJvk7PQagK5+veGTkOBWITfNK1PCmwB2kL9/PfuN/BFw7r3nC2heJQ/HNqkkzTrigT44a7X6TU4388WGYsg1z41GNgO4u9B/fN/Gqum9Pcgn/4vbTDs9JY0eb7DO/SvN5lPnwdFVSQaFyPHT9eWQ7JsWIYrkriqGqr4XIkE41EePbAhKbAF6Aj7+fHu1+mLjN3+O8OF5Zwyt2+++SZr167Fak2+4VRVVXHixIkU73r3YjGZWVpax9sdhw3XryhrIGdYFs9lsTExu5jdBgoLHovN0PRhffth1BTSIH87tpP6rCKd5msikaAn3M/2rmZ293eSb7GzuLiWHIsDl0Of+dredYJ4Ckmx547vZGpuGdlDs56BPuS21YavJxJCth9DePOTFjf1tXFDwTjm2Vx4967HFPLjr55Mz/g5/PbkfuYVVif/vQK9sPZJcsMBrqucxNKScZiiIaxr/6qVrK12RFZy1u+YvztlZnz1ySaWltSRO8Yp/bagb7BfejhvtR1iZcUk8k2nv0cZ8CVnt4ciVeTetxELb9OvikXA14XcsQbZ06pp4tbN1MTHRfLzq3B4NEvmZ39muBsx7WqE6SIEt04PTFqEMmEO8uguzd61sAIxeyXqvg26gFzGIqgbX0i5OfWd51Bu/HiS6Ul/LMy+PuOHvJZgH/2xcEpN5NHSH4uwzeAhD6Az7Kc3Ghp7mTPgS20jPNCmMGzgS+7fZNyKAsi3/4qsbkwe2nN6ELNW0j2ukZ2RADvDPnJNVq6acwN5+zbgKKgc22fIcMnSGwlyzN/N2rZDmITCkpJaSp3ZY/5tXMr0R8Mph5LbQv30xUIZN7DLjHMKblVVJWGQ4WtubsbjuXKF0MdCnt3FneNm8OThrUnadAuLaqjPKtS9Psvq4J7xs/jv3WtoDfkGlzvNVj45cQnZw1oMVKlyMph6yKM7EiBhEJS2Bfv4zs5Xk8r5q9sO8Xc1s5ieX4Fj2I14pH30RoK6rCqJ+LBs0fA3degWeSVM6ziBff3fBpe5D23D7cnl47d8Sv85VPVU2RqUY7twHNuVvL5HH9B0hI3dswAiiTgxOfbMbfcI9qVxqQ7qvw6iJkbUI5VdLUipJgWsMh7TBn/+9lMG2xgOb0e+/VeUe/4BCip02xEVDTBlaXKPqxCIq98PWfq+1guC04OYOB/1qe+fbjE4vB259VWUO76gDSIO5ZTBSUr6OrWhtCE35MgZqhLDW1rOhUgiPqLyZDqyw8SjKQNVwPgc6kqtJkJ/t257wmKjfeZyvr3jlaQHtNc7jvCB+pnMdXq4ckOddy+9kSA/2/NmUuVhU+cxZuZV8L7a2XitV2aAN7TaaUSqJEWGS5dzCm6vvfZavv/97/Pzn/8c0PTY/H4///Iv/8KNN96Y1gO8UsiyOlhQOI4puaXs72snqiaYkFWIx2I3zOT0R8M8emADN1dNIc/m0gJiCVE1zh8ObuSTk64ib4glqSIUJmQXsa3rBNPyypiWV4FZUTjo6+DttsOUubJ1lqe+YD+PHtxoeFP/7eHNjM8u0gW3dZ483mw7aPgZy1zZmIcPIpmt2uBJqunqoirdojrFgnlIYHv6j9JN7uZVxK9+/7B9mLWgLBomNG0Z0ZJxiGgE9861KEd3Qcl43aYq3amljLKsDqwGChZnS8kIPZxOswXr8MYTsxUKKlNKp4mKBl0mlkAf8oX/geEhVTSE+sIvUe58SNdPKZxeWHwnYuYKZHOTNrRWVqsNYl2s7Iy/F/ncz/VBWyKO+vwvUN7/f2ConJvVjiitQbYfNdycKB6n03V2mi0oiJTVDW8aetIdZgsWxaR/cDlFXjo0e6127b8UgbIwUD+goiG1zm1Bua7tIxiP8tihzYY39d8f2kJDbhn2NA9eXgmE4lFCiTgCrcKWStP5UmVb1wlDl8DNXcdZXDyeyblXZnDrMFkxCcUwAQToKqsZLn3O6Q7+ve99j6uvvppJkyYRDod5//vfz/79+8nPz+exxx5L9zFeMXisdjxWOyWjGCoJxCMUO70kVJXfH9hAc6CHbJuTpcV1XFVSS1uoX9eaMDW3jEK7hy1dzTx+cCNRNc6k7BI+1rAIt4EKQFCNccDgQganBq58XRS6spOW11kdeCw2w5veHSX1eGJRGHohcGchFt6GfPk3+p1kFyIMPOrNRwzsUU9hatqIZfGd4Dj92YUrm/h199NqNvOn9kPsb96N02zlmqmLWDTnerIN9lHs8FJgd9NxKuM7lFurppKdhgxFXkKlzJHFiZA+sL+xcDzZw9oihMOFWHwH8on/0G/M5kSMm6pbLHtatSymER3HIRwAg2EI4XCBwzVmjeG0EfSBQW80AP5ebeBsSHArTGaYvhy5Y41WHRiKYkLMXokYFrB5rHYWFtfwZqv+4WxabhmeNARrXqudq0vqeenEHt26em9hWgJoXFmI2Tcg33pKv66gEgw0aEVlA9Lu0s6H4euuei/CkXwtCcQiKU1nJJKDvs60DeBdCcTVBG2hfp48vJXdPSexmcxcVVzLNWX15NjSZ0JyPumPhnntZFPK9atbmqjLKsRquvLk8bOsdpYU17La4PNPzC5Oz+82wwXlnAbKSktL2bp1K1/+8pf5xCc+wYwZM/j3f/93tmzZQmGhvsSe4exREJQ6s/lV0zqOB3qQaENQTx/dxkFfp+HkpCIETxzazJrWA4QTMVQp2dnTwn/vWYPFIIMgU02nnyKi6oMmSzzORxsWUTUk8+mx2LinZha5drdu4l0IzTlNLLsXhgaMlRO1rKKBuYIYqQSkJgyn6luycvn3A+/Q5OtAIgnEI/y19QD/091Mv0GvVLbNyYON19CQdVqBwG6ycHfNTKbnlaVFCsvTcYxPl9bTmFU0mKO1mcy8p6SeeX4fSkyfMRcFFYhbPp0ss1RQqekXe/XaxikD2wHSMBh3QTjT4IrR58gqQLnrSzDUzCSrAHHnFyErX/dyu8nCrZVTWVZSj/lUBlwRgvmF43h/7Zy0SMBZFTPXljewsnzS4G9OIJiVX8FHGhbq1ETOBWEyI6YuQSy4dUjGVUDNdJT3fNbYbc2Tp+ldFw6plDi9iBs/gSiq1r1cPcO1wWgY8t1Me6ifb255gZ09LahIQokYL53Yww93vpaWIcILgYpMWXEArXR/pdq9Wk1mbqiczIqyhtPXBgRz8qv4cP18wwHuDJc25/wI5nA4uP/++7n//vvTeTxXNKqq0hUJ4I9FUKXEY7GTY3NiMRjgEULw7DHjDObb7YdZWT5Rt/xwf1dSf+4AMTXBc8d38qG6uUk2v3bFTKHDQ3uKjNk4r/5BpVlR+NXet7i6tJ4bKyaTQBJXE7xx8gA7FYWP1i9geAFHODww7RpE7UzNscxs0fQ1U5RoRc00pFFbAmjl1WElIn8swh8ObTYsNx/o76Aj7DfsFct3uPn4xMX442FiiQROs5Usqx1TmkqJoqCSnMf+Pz4y/Rr842YSBRzxGN7tb2DqaoHaGfr32BxQOwNRXKNl2RQTONyaq5bRPvJKkQh0bQmgZTrTaF17XnF6tUDNqO/VYjfOPpvMUFan9RaHTmUk7S7ECJP8WTYHt1Q2srSkjmA8isNswWOxjdhLKKWkNxoa8ru1kWVzYBreInIKr9XBLVVTWFJSSzgew2oy47HYcZhHlvKR/p5Tkmiq1mPsyk453CecXphzI2LSIs30wmzVgtUUQy9CCMgr1fqXQ37tYcHu1LRvDT6Hw2yl1JlFS4oe+7o09mbLeFRrWwr5T30Oj16abIzEEgn6Ytp3aFZM2neYpv7RcDzGM0e2Gw7angj2cczfc9G1UkeD22xldn4lzzfvNly/oKhGZxN/JZFldfCe6qlcXVo/+Lv1Wu0ZCa7LlHMKbn/zm9+Qn5/PTTfdBMBXvvIVfv7znzNp0iQee+wxqqr0fZTvdiKJGId8Xfy6aR29pySdnGYL7x03i8bcUt0kalRN6Kx6h9Ia6qd0SMuAKlU2dBj3HwLs6G4hGI8lXZxyXFm8f/xsfrDzNd0T+ZKi8bgNbsabe0/ii4V55uh23TqBIIzUBbeAdpP25gEG2cfhZOXDuCkwXKLIZIal9yDsyXsIJ2Ic7k/trLWzu4XxXuObsctixXW+NF1dXkTDfOzvPIf9nedOLxcCcdeXUlrKCqGAJ0f7bzT7mHM9csPzw7eCWHGfYWb8ksSVhVjyXuSrj+pWiWX3jGgYIFzZMKx9JhW9kSBPHNrE5s7jg2f8pOwSPlQ311AdI5ZIcKi/k1/ue2tQ0s1ptvKB2jk05pRiTxGwWhTT6G2HE3FoO4r67E9PD4NZHYhl90LdTESKwEiYLYYZ6pEYsB4+E16rnQ/UzuW721fpHhoXFI5LW2Aog/3Ira8iNzx3ur0krwzl5k+mrWXGH4vwVttB/nJ0x2Bmssjh4eMNiylzZY+5ShNMRNnVq5eeG2Bj59GURjKXEibFxOKSWta2HcI37N5T5PAwMTsNOtuXOFbFPOrfbYZLm3NqS/jmN785qG+7bt06fvSjH/Hwww+Tn5/PF77whbQe4JVCVzjAj3a9NhjYAgTjMX6z/21aDbIjqbJCAziHPU0qQsFlTl06cZgsg7a/A8TVBD2RIJ+etITJOSW4zDbKnNncUzOLam8+CYMM5kjlGbvJrNvHudCtmDgw70YCS+/Rsps104hPXkzve7/MHkW/fQUxWEoyYqS/y/lE2F2Ihe9B3PhxyC/X7HRrpqF84J+1gad07MPqQMxaqbUyFFRq+6iajPL+f0SU16dlHxcCYbYgGuZpLQUl47XPUVqHuOvLiLrZWpZ2jITiUZ44tJlNQwJbgN29J/l10zqd5jFo+rQ/2Lk6Sas4GI/yi71rOWlQJTkn+rtR//TtZJWDaAj50q+Q7cfSs49zoMqdy9dnXM+03HLcFhulzizur1/AHeOmp6WFQ0oVuX+jZrk8tM2h6wTqH79trNd7DuzuOcmfD2/Fqpio8xZQ6c6hPdTPd3e8QncktaLJaFEQI2b33Bfp+nMu5Nvd/MP061heOgHvqcriLZVTeLDxmsxQVYbLinO6Yxw/fpza2loAnn76ae666y4+/vGPs2jRIpYtW5bO47siSKgJ1rYdSqkP++zxnRQ5vEk6em6LjXGePMOMpM1kpsBgmOOq4lrWtBq7T11dWq/LDvdFwzx7fBe3V0/nuvKJJFQVgcCkKDx36piGl9PmFFTx/PFhUlunWFpShzcNN70j/V1s627hPWW1JJCIYD/x8npaEjH+dHQv5Z68pONyW20sKBrHGoNBIYDG3Is3NJWwu/BVNxLJLwM1jrA6sDs8ZI9oDxumM+xnR/cJHGYLU3PLyLI6cKSwUBZOD6JuFrKsXgsSLDZddvtyQNhdiKpJyMJKrZfYYk3ZunIu9McibO40Dhb39bXTHwsnPbwlpMqa1gMpJ6j/dnQHH2tYaGhtLfu7kW1HofUw5BRrDxqeHIRR7/ve9Sl7p+Xap5G3/X1a/w6jxWIyUY7g7wpriOSWoQgFr8ObPpOPQF9qXeegT9PANhiOOxv6oiGeP76LT1ZNoyoWw9bchGp3Ehw/hxd6Wtjv6zDUDD8bPFY7y0rq+OsxAzMMtHL+xUKNxxC+LmTzPuhuhZJxiMJqRE7q2Zh8u3vwngDa5ztTsiVDhkuNcwpu3W43XV1dVFZW8tJLLw1ma+12O6FQxsljOJFEghOB3pTrTwZ9RIfp7LktNj5cP5/vbl+VpExgEgqfmngVWQaDKfl2FzdWTOa5YcHneE8+8wqrUYaV32JqnPePn83vD26kc4hqgFko3Fc/n0Bc3xbhsdh4T9VUXVtCuSubq4pr09KvKmNh7pIKrse+OTg8Zt/wPA0FlTyw4oO6hwSrYubGsok09bXTNqx/+APjZpJ1kXqm4mqCpr52frzr9aRjzre7+cKUawzLX33RIL/a93bSpPqfD2/lrnEzWFQ0HucILRSp+nIvN4ZP7qeLUDw24jhMfyzCUBGtWCLBMX/q7GFLsJeIGsdO8ncie9pQn3hYMxcZWGa2otz1JWTxOIQyRKc4EUe2Gpu7ANDdArHoRemdlr4u1D9/F1tPGwMhv6qYEO/5LFRM1NoixkI8nloiEKD9GIyfPqZdJFSV+0onUP7q4yhtp//OTgR3XPMB9kTHrm1sEgrzC8exvfsER4edLzdWTL5ok/aqGke0HkJ98vtJvezSla0N8+anbpWwmExkmy6/B+QMGQY4Z53bj370o8yYMYOmpqbB3ttdu3ZRXV2dzuO7IrCZTJQ6s1JK6xTa3dgMdFVLnFl8bfr1HPC1s6+3nRKnl2l55eRYnYb6iS6LjRVlDczMr2RDx1GC8SizCyopcWYZ9shZFBOvtjQlBbagGQz8bv96vjZ9pe49gXiU3miIz0xayu7ek4TjsUETio0dR7m2YmLKp/xoIk4oHsOsKCOWNSdbnVhX/VaniqB0HKNw9zoSi25PWi7jMbI3vcyDJeNoLrSxLdBLlsnMbHce2bvWYnflwAjuMjIc0DKeNodOPsqIWDxGMBpEEQqeEeSQeqMhfrL7DV0w3hn288TBzXxkwvykrJ8qJRs6jhmeJ386vIWG7OIRg9uzJaGqBOIRBCItU/xGyAGDDYk2KDSK/sZwPEpETWBVzGccwgKtvE3w1DnsdBsOSIHWNjMS7mHfvUUxUerMpqmv3fD1hQ6vTg9ZhvyoL/wyKbAFIB5FffoHKB/61ySpLmEyIworkYe2GR9UdqE2gHmBkdEw6ht/1BugqAnkX36M+Lv/b+yGHyZtWJKQXo4PgBGCr9FiFVDctDEpsNWQOF99lCkf/Jcx70OVKlu6jrOkpBaTUNjX24bdbGFidjF7e9vwxyMjulvJSFDL3FvtiDRO5Yu+LtS//kQ/pBnoRX3pVyi3fHrMmfEMGS5Vzim4/fGPf8z/+T//h+PHj/PnP/+ZvDxtSGjTpk28733vS+sBXgkkpGRGfgWrTzYZSuwsK60nkUJ6J8/uIs8+jnmFo+vRdFlsuCw2KkYxSJSQkl0pLAdjaoKOkJ+yYYM6G9uP8vrJ/axtPch4bwFWk4lnjm6nNxrCZbYxr2icrjcrriboDPt5qXkP+/ra8VhsXF8+mfHefMOgynJsj6HcF4Bl11psc25IzmQFfcgtq8jaECXLk8vk/HKIRaDlgHYzzi831NOVQR+y5YA2jBXs15y7Zq/U5KQMAiFVVekK+Vh9Yh/b+9qwmyysKB7PxOwSsgyypicCvSmldbZ3n6A/FkkKbn3REK+c2Gv4eoC1bQe51z075fqzoSvs5622Q7zTcRSzUFhaUqc9OKWxr076upB71iF3rwNFQTQugfpZKW+ooXiUlqCPZ4/uoC3so8SZxU0VjRQ7s1IGubK/G7nvHeTONwEQkxbCxPmG+/BY7UzJLWVHt/6cH+fOwzMsw2ZSFJaV1vHGyf2GShy3VDbqW0VC/XDSuD2GcADp69Idm5i4APnOc4ZyZ2Lhbectkz0ioX7Yv8l4XSKObD2is7Q+a1zZiDk3It94Qr/O7jKUKDvrXUQjqDvWpFgrsRzZAYV6F7+zwRcN8/KJvfRFQ3gtdqo8ufREg6w5eYC4VLEoCneM0yujyJAf2XYEuf5Z8PdAcQ3KvJs0/e80PNDI/p7UDw6thzU1lkxwm+EK5ZyC2+zsbH70ox/pln/jG98Y8wFdicTVBLu6W7ivbj5/PLRp0BHMopi4qaKRlkBvkm7shSIhU3k1aQTi+gGbvpjWdhKXKvv6krM6wXjEUDu3JdjHw9teHgz0OsN+/nvPGywpruW26mm6LK4S7E99XPGoPvCV8nR2or9bbz9qUPqUYT/yrWeQ2187vayvA7n3bZR7v5asB3qKjmAf39q+itAQ84VfHdzI1OxiPjR+Dt5hAa7RgNLgvpC6jK5EG1ZKhS8aQpVS115ytnSG/Xx728tJw42PHdzIurbDfGrSVWmRLZK+LtQ/PqzZ4A4se/1x5K43UW5/EDFMCSKuJtjW1cyvmt4ecpwBdnS38NEJC5mZX4lJSc7Iyv5u1D//J3SfnlSXb/4ZuXMNynu/rAsiNZWDufymKbnto8aTx0cbFhs+aOXZXHx68hL+d9+6we/Gqpi4Z/wsSp3Z+g9+Jv1XI81TTy7iji8gn/3p6WDEbEEsvhNRXD3y9s4XiYQmSZaKVIYbZ4FQFJi4AHxdyG2rT+/Pm4fynr9PT+AlZUonNwBlpLaI0e4CSeDUb90XC+sennoi+lY9GQkjt7yiDdMN0NeBun8j4q4vIsonjPm4iJ5BX/dMGtkZMlzGnPMIcm9vL7/85S/Zs2cPQggmTpzIAw88QFbWmd233m04LTbcVjuvn2zi3vGzsSgmVCkxCcHatkM4TBacJuNyc0JV6Y2GiKpxLIoJr8WeNocYu8lCttWRFOQMxSjgnppbnnJwqy6rUKeD6I9FeOzABsMM5hutB7i6tF7folA1Cd551vigC6tg+OstNiisRCom+mddR8jhxozAdWQn9u2vQdVk/Xb8fcg964jMXIG/ZhpxwB4J4t30Muorj6Lc9rmkjFk4EuKZo9uTAtsBtve20hHx64LbkSx+s60OHMP+VnaTmfqsIrZ3nzB8z8y8yjEHtgk1wRsn92MzmbmnZhZZNgcKguZAL2+c3M+R/i6mpwhuNT1Sn/YgYbGl1GGVUtWGpPoM3O86m5HH9iAmL0xa3BcN8fuDGw33++iBDYz3FuikuuSh7UmB7SC97cj9m2HGcl0bRI7NyUcbFtEfCxOIRXGaLXgs9pRtGVaTmUk5JXx9+kp8sTCqlGRZHeRYjfWpsTm1FhiDgAZAZOsrCMJsgfIJiA/+izZkpSY0eTOnF3G+ZOrOhNUO3nzwGTsYCgNL63NBuLyw+A7NCjrg0z6v0zuiVvFZYbFp6hupsukGrn9ni91koT67iN09xnJgM/MNMsPBPuSG5xGNizXnQSmRiRhy11vIl36DvOcfUkoFjhaRXZQ6SWBzaDrHGTJcoZxTlLRx40ZWrlyJw+Fg7ty5SCn53ve+xze/+U1eeuklZs6cme7jvKwJx6NUuHJ4qXkPv9z3FqBpwkokFsXEZyYtJZSI4SI5aOuPhnm7/TDPHd9JMB7DLBTmF43jlsopacmuZVsdvLdmJr/Yu1a3bkpuKdkGfWIV7hyKHF7ahskgKQjuGjdDpxkbikc5NIIG7d7etiS9XgCRW6xNzOtkkATK1ffqBqeE00P4ho+xJ9DDH0820dseQgBTcoq5++4vk+/St2jIEwfoveML/KnnBFuObkMi8Vrs3LHgZqacOIgrEkyy+A0momxNcfMC2NBxlPE5JUnLsq0OGnNK2GnwvrtrZun6oB1mK++pnsaunpO6Cf08m4vxaRDO98ei+KJhbq2ayl+O7hj8Hms8+Xy4fj7bOpuZnFuqc7ST/l7kO88id7yhZSetdsTs62HKUr0jVsiP3LMu5THInWuQtTOSDAd8sTCRFFnPUCJGfyySFNzKcAC5W3/eDq7ftRYxcX7SdziA22IbteOQKiWtQR+/27+eI/1dgKDY6eGDtfOocufqA1xXNmLRnchXH9FtS0xeZGhGAaeymJ5cLYs7qiM7vwh3NuLq9yGf+aF+ZeXEtJazhdWunU/ZqSf4z3nbDjfKsntRH/+mvuKTVzbiUNVocZit3FE9nb29rbq2swK7m2qPXttbdp5AufmTyH3voD77s1OmGi7EjBXaw304MKK282iQdrfW8mLwWxTzbkG6L41zLUOG88E56Xt84Qtf4NZbb+XIkSM8+eSTPPXUUxw+fJibb76ZBx98MM2HeCUgWN9+mA/Xz2dmfgWK0ALbCVlFfGLiVbx2skn3hB1XE7zZdpA/Hd5C8FT5KC5V3mw9yK+b3sY/gsHDqI9KCCZll/CZyUspcmg3XYfJwk0VjXyodh5ugynfHJuTBxuv5qri2sEAqMaTx1emXWtcph2CSSjk2Jw4h/STGWUihSsb5T1/r13o3TnajbRoHOLur0CBsUHIQVR+cXTbYBZaomVUv390O30G/Wu+svH86OQ+Nve0DBpY+GJhfn1sB7vLaiGFvfFIn204Hqud++rmc1NF42CWttjh5bOTlzIxu9hwuKrY4eEfpl1H3Sl3OLNQWFhUw0NTl6enH1bAnMIqfrn3raQHFM2oYC1zi6p1NzwZDqC++ihy66uny+7RMPKtp5GbX9Iyukn7EDCSdJCiaK8ZuugMt1n9n2oU+0gDXWE/D297icP9XXgsdrKsdlqDPr67YxUdYX1pXphMiAlzETd9AnKLwZMH3jzEVXchFt91WUm0ifJ6xB1fgAEzBasDMfcmlOs/enkpc+SXadbDAz28Ziti+tUodzyYtgxxscPLP0y7jvEezVTDLBQWF9XwhSkpfreeXNQNz2sVjoGqVjiAXPcMxKLINPTcKu4sxKLbEQtvO/2Ql5WPWPkRmDAX5WJVBTJkuACcc+b2F7/4BWbz6bebzWa+8pWvMHt2egZeriTsZgtTc8v56e43mFNQzUcnLAIBR/s1x7KrS+rIHXYBHNBnNGJPbyu90bBh8Hm2OC1WpuaWUeXOJaYmUIQgy+LQ9TcOJdfu4u7xM7mxYjIqErvJkjITZhIKk3NKqPMWUurKoiPkx2WxYVEUXji+m9oUzmGqK5ve+bfQNnUJgXiEMlcOXpvTcD++aIg/Hd5quJ3OsJ8TgV5dprvDZKIlhQj/k20HqCusYmi+122xMy+vgjc7jhi+Z15hteFyr9XOTQVVLM4pQVVVLCYzXostZbnZrJio8uTyqUlXEUrEUBC4LbYztqL0BProiAToDQcocmaRY3Xo2iQATAheazEekArGYxzoax+8QZ9e0Q8HNhvuV25+GTF1adLkvHB4ENOuRr7yO8P3iOnLtWzdEDxWOy6zdbAffShei1037CXsTsT0a5AtxrrOYvo1Yx7EUqXknfYjNGQXs6hoPD3RIKpUybd72Np1nOeO7+JDtfOwmZO/G+Fw0T9uCr7S8fjjURwmC16rg5zLxQr5FMLmJFBah7jtc8h4FKGYUB0eXDbnZZXxExYblNai3PEgRCPaQ5HTk5ahrQEsJhPVnjw+PXkp4VH8bgUy5bkrt6xCmbQgLcclvHnIOTeiNMzTgmjFjMhOn3VyhgyXKucU3Hq9Xo4dO0ZDQ0PS8uPHj+PxXEZP9BeQcnc2dd5C1rYdZG3b6f6vEqeXeYXjdMFkKBFLWaYFaA/5KB+l5ehoOFs7TatiJtd+5tMnLlXuGjeDPxzaxNNHT8sdOc0WPtaw2DCYiasJDvk6+dHu15P+BjPzKrh3/GydrE5UTejaJIbS1NfO5GFGDof9PSlf3xMJEh0W/FktNm6omMzOvjZdj/JVhTXkGDxoSKlC+3F48j/JGjK1rJZPQLnhY7qhqqEMqF6MhhP9nXx/1xtJlpnVrhw+0bCI3GFl8FAiptPiHMq+vnaWltThVE7ve0SnqETccEhK1ExD7nhd31pS0YAo0YvaZ1kd3D9hAT/Z9UZS4K0Iwf0TFhien6J8ArKsDk7sT15RPA5RNSn1MY+SaCKOIhTGe/P56Z41g60iArimdAIljizCahTbsMtoTyRIsKcVd+cJ8tqOEsspJFgynk5vPvljLDVfSHojQX7V9DZ7hwzfucxW/r5xGVXuvDH3f19ohMMDI0j3pYPRtrzI4RJrQxmQBksTismkScplyPAu4pyC23vuuYcHHniA73znOyxcuBAhBG+++SZf/vKXM1JgKbCbLNwxbgYLQzW81XaImJpgdkEVdd4CQ/1NizAhIOVAwJksHeNqgoSU2NI0fGaEjMdBJkbUZrQoJl5u3sPe3uHKCjF+umcNX566Qvee3miIH+xcrVMT2Nx1nHJ3DtdXTEpqA1AQOEwWQokY1e48Kt05hBNxdnSfIJSIkWeQMRupZ9mimDAN69iRUpJ7aAdfKW3gkIAuKTEBVYqZ4vbjuCMhfT9lfw/qn7+r9c8NpXkf8q2n4Zr3j1nXsifYx3/tXkMoEWNWbhk5ZisnIkH29rXxxKHNfLhuLo4hn9WsmMiy2pPsZIeSY3NiGXbOCLtrRFUNDLSBhScH5T2fQ55oQu5Yo0mBTVuGKK4xLAWbhEI9Jv6pbh6v9bRyIhqgwuZiaXYJ+SjG7SvubJSbPolsOXhK9UIipixFlNUiRiGFdybMipaN+/7OV5OWS+CVln3cX78Ai0juuQ3Goyi97RQ//UMIag9cVsBlthC49TP0m8bjuQxaE6JqnOeO70oKbEHTuf7+jtX888wbxuzs9a5mpHYIoVwUbeMMFw5VSmJqHLNQ0mJ8lEHPOUU+3/nOdxBCcN999xGPa5k1i8XCpz71Kf793/89rQd4pdAW8vHwtpep9uSxtFgT+97S1czvD2zgvrp5LCpOnj4WQjA5p5SdBjq0Xos9ZVbPFw3TEuxldUsTkUSc+YXjmJBdlF790mA/svuk1oMZDiAmzEFUTUZ49YMTCanydruxA1MkEac91E/FMFWBvT1tKa2KV53Yy8KimqTPYxIK15Y1UO7O4bi/hwO+DpxmKx+sm8uJQC/Vbv1xlTizsComogYqDvMLq/U9tIE+5LZXYcldhBNx9vZ34jSZqcoqhkQMeWgrYlay6YXsbtUHtgPr9qxDzL95zEL4XeEAUz35XO8txL37Lcx9XYRLxuGvncevWg/QH4skBbc5NifXlk0cHGwcztKSOt0wGa4syMo3Vj8onwCOFENSnhxEwzxkzTQQYsRAXvp7Mf/5PykK9vHemmlEvflY+5pQDv0avHnIe75qOD0u3NmI+lnI6smAQFjTJ4IPsD7FuQvwRut+puQOG0gK+XG/8shgYDtIPIbruV8Qfd8/XhZT6r5omLfaDhmuCydinAj0ZYLbMSCyC5GpDCzqZqYcPMxweaNKla5wgHfaj9Dka6fA7mFZSR35djf2zANNWjmn4NZqtfKDH/yAb33rWxw8eBApJbW1tTidl/5F+2IQV1Vea2kC4Eh/16mp69OsOrGXqbllSXJEoXiUZaX19EaDNA9xO/JYbNxXP5+usN5goT8a5snDW1g35Ia8p7eVIoeHBxuv0UkpnQsy5Ee+/Vfk1ldOLzu2G+nNQ3nvVxBZyf2aCakaBpADdBuUtI0GdQYIxqM6JYGYTDAtr5wf7FydVJrf1HmMleUTDfV6TwR6+LsJC/ht09uEh7Q+1GUVMjmnlKgc1hIiVXqueT/fbd6ZdMybupq5Or+Kmy1OdLf6kcr5aiItpUdFTXBrXMX5xLcZyPPbj+/BvuUVPn7b54io+oeEWm8BS0vqeP3k6XK+IgT31swiz6Y/R4Q7G+U9n9ey0EOdt3JLUFZ+BOEY+bwa3l9rSNAHp34Xyv5NJL2jt11bP0JJf1T7OEviaoKuiPHDCUBvJERcxmGI/a4lEtQE8o0IBzD5e8DAUORSI66qKQ1IALpH+LtkGAXuHJQ7HtJ0moc6RBZVoSy957yczxkuPs2BXr6zbRURVbu/7KWNNa0H+Ej9AmbkV6RN5jPDOQa3fX19JBIJcnNzmTJlyuDy7u5uzGYzXm/mqXMoqlTxx1KL8xsFbBbFxCNN67lr/AxqTDZMahwpFFqFwuOHNnFf/TzddtrD/UmB7QBtoX7eaD3ALZWNYy+B+LqSAtuk5RtfgKX3JA1qWBXzyFq6BjI5qYbMAAodHl1m0SJM/OnYlqTAdoAXm/cwM79StzzX5uLPR7byyYlLCCWi9EcjFDu9tAZ9/PnwFr44rF0iZrbygq/NMBhf3XmUhY3X6IJbkV+Wupxvd+v1es+BcsWCafXv0TWwxCJkrX6M2Hv+XveeXLuLmyoaWVpSx5H+LiyKiUp3Lp4R+nxFfinK+/8R2dsOfZ2I3GLw5qdPj9RAPzh5fer+cxkNQzgISE1OKU2BgdVkoj6rKKX9brUnD7uSnG0RAwFhXin+GcuJefMwhQN4tr2GOLEfZQRDgUsJq0nT1Db6TQFUGMjrXUgS8RhqoA+kirTYsF5GvcygVeZkYSXKB/8F2dMK/d2aLJknd8z6thkuTXzRML9penswsB3Kb/evZ3xWAfmm9FVD+qNhoqeGxL1Wu6Giz5XMOX3ae++9l8cff1y3/IknnuDee+8d9XbeeOMNbrnlFkpLSxFC8PTTTw+ui8Vi/MM//ANTpkzB5XJRWlrKfffdR0tLcpk+Eonw93//9+Tn5+Nyubj11ltpbm4+l4913rCazMZC3qeYnFOCc1jfos1k4cNVU6jvaiP7Lz/G89t/xfvH71CxfxP3lU/U9dxKKVlz0nj6FmBt60H6R3DMGi1yz9up1+1aq3MuyrY6eE/VNMPXFzm8FBqUNsvdOTr1iAHuqJ6uGy5KSJVt3am/8z09rbplhQ4PN1c2sqP7BE8e3srLJ/bw1JGtuCxWPlA7h+xh+wgoCm93pd7HBiMbY28eFOoDawCx4FZN5myMmHrbUwd+nc1YDQb2ElIlEI/y1OGtPH98J88c2c6LzbsNDSqSjtmTi1LRgNK4GFFam77AFrQybKoHL7Ml5SCQ7GlDfenXqP/7VdRffhX1xf9F9rQaOuWdLYpQmF9YjU3R5wAUBDdWNmIbVkpU7G5Ci25n+9Xv47vRfv7x+A6+2dvCG7Ovw3/DxwxtoC9Fsq1Obq0yNjgodWaRfzEsgU8R93WRWPcMyu/+FeV/v4bp6f8i1ryPmJH72yWMEALhzUWpmqT9porHZQLbK5hAPJJUhR1KXKqcDI7dLQ8gFI+xp6eV7+98la9veIb/t/k5Xjy+m74zOdZdYZxTcLt+/Xquvvpq3fJly5axfv36UW8nEAgwbdo0QyvfYDDI5s2b+ad/+ic2b97Mk08+SVNTE7feemvS6x588EGeeuopHn/8cd588038fj8333wziUTqktrFYFJOCTlWfcBmU8ysrJikK0dkma3Ud7Xifu7np12Ygj4ca5+iautrZA27eUsYsYwYl4mRh4JGy0gBkMH+hRCUu7K5vXoarlMBuQAac0r5UN1czAZPkya0CfmGIW5OXoudu2tmUWAQDEukTjx9KHGpPy5FKKxq3sMrLfvoCPvpCPs53N/FL/auRZ467uH7GJ5dH4pR64VwZaHc+lmom3VaqNXmRCy7F9EwVxPuHyNihO8cQBj8XTpDfr619QV29LTQEQ7QGfHzVtshvrN9Fd0peoTPO04vYs4NhqvE3JthuFEEIPs6NXH+pg3auSdV2L8J9bFvpnTWOlvy7C6+NG1FUqay0O7hwSnXUGTXB9zSlcWWslp+enQrbace9HyxMH9o2ctfhUr4MumlVIRgRn4F7xs/G9epB2+BYHpeOZ+dvOys1VXSRbS/B/nczzFteB4GqkFtR1Ce+Da0Hbkox5Qhw2g40wN33KCF7FzY19fG93e+OhhIB+JRnjm6nUf3b0iLPv7lwjm1JUQikcFBsqHEYjFCIePysxE33HADN9xgfEPLysri5ZdfTlr2wx/+kLlz53Ls2DEqKyvp6+vjl7/8Jb/73e9YsUIrIz/yyCNUVFSwatUqVq5cabTpi0Ke3cWXpq7g2WM7eafjCAmpMjmnlDvHzSDfIGAzhfpR1/zRcFti91vY5t6U1IOoCMHCoho2dg539dKYlV+pcw8bIBryI0L9EAqAxYp0erCmyipOmAvbVhuvq52p2Y8OwRcN8T/73iLb6uCe8TMxKyZMQqGpt40f7XqNL0xZjntYGbkt3M9/736DxcW1XF06QevbTSRY07qf9e2H+VzjsiSNX4fZysSsYvb06TO0oFkGD6c7EmBvinLzHw5u4otTl+MdcgO3CBNTc0rZmsIad3qefh+g6UwqKz8CV92l9dha7Slta88FkV+GFELvvgRa5nhYn3U0EeeF5t2GwXhPJMju3pMsLq5Ny7GdDcJigxkrkN58ePsvWr+yNw8W3oYYNwUxrLIhVVVzXjIayAkHNIWGBe8Z899ZEQqV7lw+33g1gXgUicRptqYM7vpiEZ48vtNw3ZsdR7i2spHLRe3WbbFxVUktU3PLCCdiWBQTHosNu4E6xoVC+DpRDPVhJcprfyB6x4Opr10ZMlxEnGYreTaXYR+/QOhmaM6F3kiQP6SwMd/WfYLeaCgt+viXA+cU3M6ZM4ef//zn/PCHydaMP/3pT5k1a1ZaDsyIvr4+hBBkZ2cDsGnTJmKxGNddd93ga0pLS2lsbOStt95KGdxGIhEikdMlep8vtUZqOsl3uHlf7WxuqdL6lB1mK45UE5LhoPGN+xSyq0XrexxCmSub8Z58DvYnZ61cZivXlk3EalBejfV3w1tPo+x+63SAlF9O/OZPYs4t0b2enCKonATHdicvt9ph4Xt0/Y7hRIy2kI+2kI99fXptxwN97Tp7yl3dLQTjMV5q3gPs0b0nnIjjHvJnc5qtvHf8TL619UVd9npmXoVhi8P+FIEtQGvIRzgRZ2iOLaYmWFJaz96+tqQBNIAJWUUjPnUP2IueF5wexNybkev/OmynArH8Q7rWgWA8yu4RbIQ3dRxnbkH1RRls6FEUXrDbmHjdh8k2W+mJR9gbC3ODYkIXrkSCyINbUm5LHtqKmHktpMlJy2O1Jw18piIQjxI0aAUBrbrSHu6n6DLJ3oKmRJKOQdR0oR7bk7rc2NmMuEx6mjO8+8i2OflQ3Vx+sPO1QVfMAW6snIw3DUFnKBEznAsZ4Eh/F+UXuV/+QnFOd7B/+7d/Y8WKFWzbto3ly5cD8Morr7BhwwZeeumltB7gAOFwmK9+9au8//3vHxxYa21txWq1kpOT/GUVFRXR2mqcxQP41re+xTe+8Y3zcpxnwmoykzuKwEGeIeMkbfrMUUxNsLysgckhHxs6jhBJxJmcU8LM/Er6IiHdTTUeiyA3r8K0a23yhjqbEU9+j+jdX8HqTVY/6DaZ6FxwM6U1U3HtWAPRENHqRvyNi2lHZfKwY1JOaZSqUpJjdVLizCKUiHKkvwsJOA30eoe7UQ3FopgMNU+LnV7+z4wbeKl5N7t7WnFZtIB+Yk6xYVDiMrDXHTxmIXQ3UCEEzx3dwScmXsWGjqPs6W3FYbIwr7CaLKuTY/5upuSN3af+rIlFtQD3uvuRO97QMp4FFShTl6L2dSBj0SQ3NEUIHGZrygE/j8V2UQYPArEIv256m319bbw+bF1n2M9HJyzCObTyYDKDgbLDIDZX6h7e84j5DK0m9hHOuwyjYKReX5P5onznGTKMlhpvAf8443r+dmwHR/q7yLW5uLFyMuM8+WmRAjMLZUR9fNcZ9PGvJM4puF20aBHr1q3j29/+Nk888QQOh4OpU6fyy1/+krq6unQfI7FYjHvvvRdVVfnJT35yxtdLKXU9k0P52te+xkMPPTT4b5/PR0VF6oGvdBEN+BD+bmTTRojHEHUzkVmFWA2cqsIWO9bSWoRRCc7qIObJZfhlfGPHMZ4+uo1qdx7zCsdhESYO+Dr44a7XKHdl83n31UklCRnow5SqxcDXhejthGHB7e7ukzx6aBMVrhyWLL4du6KwK+Rjw8ENWuuFOzepZGsSggWF45iQXUw0EeeIvwuPJYebKqewtvUAlQYlxMbcUv54eLPhD3RxcY3hE65JKBQ7vdw7fjaheAxFESMGyXWu7MGgezhzcsvwDJPp8lps1GUX8aNdrzMjv4IVpQ1E1TgbO45xPNDDv8y8yXA/Mh6D/m7koe3Q1wGVDYiiaoQn1/D1Z01/N3L178GTh5g4TxvM6mlDff5/tP7o6ikwxG7Ta3WwomwCv9v/juHmlpXWj2i9fL7wxcKGmX2AXT0n6Y+Fk4JbYbUjZq1EDq8gDKyfvRJxEfRkPRYb1e48jvi7dOsGypJXMuF4lJ5oiC2dx+mPhZmSW0apM2tE05SzwVQ5SetfN/jdJibMQznPDmQZMowFm8lMhTuH++sXDLb6jNaJcjS4LTam5Jax3aB9ziwUKt5FLTvnXHucPn06jz76aDqPxZBYLMbdd9/N4cOHefXVV5NkxoqLi4lGo/T09CRlb9vb21m4cGHKbdpsNmy2C/sEEw30woYXUDYP6SPesopEdSOxaz+MZViw06sILEveS+5f/ztZW9Rkxn/jRzkpEww1P1alOnhDPeLv0t1c20L9xIaXzmMRSFFCBVB7W6Ey2WL5yCnr1uOBHh4NJFvYtof6SQzbR1QmWFHWwH/vXkP7EP1awR7+rn4+fgMFhyyrkw/Xz+c3TW8nBbhlziyuK5+EeYTsjNVkHlVJ3XPiAB+rmsbPj2xLKhEVOjzcml2CNRKCIfdJk2JiaUkde3pa2dhxlI0dRwfXvW/8bEOTDJmII4/vQT7zo9PDdltfQXryUO7+MmKMBg4A0nfqe+7vQr7znP4FBgMEU3LLmJJbyo7uZIWHleWTKLpIwUHoDJq/QYNBRlFUBVOu0vprhzJxAaJYb/F7IXBb7Nw/YT7f3f5KkoyWRTHx6UlLyDaouFwphONRNnQc5ZEDGwaXvdrSRLkrh89OXkJOGgJ76cpCvf4BTC/8MjnAzS1BWXAL5iv475vhysFutpwX0waH2co9NbM4EehN6u1VhOCTk67SqQBdyZxTcHvsmPHQ0gCVlcbyR2fLQGC7f/9+Vq9eTV5ecm/mrFmzsFgsvPzyy9x9990AnDx5kp07d/Lwww+n5RjShehpTw5sT2E6spP4oe0wbVnScqvJzA9O7OGBWz5FYV8nlpMHSeQU4Sup4fetB7mtIDnTrAiFKncuW1PIVRU7vHplAotV01pNIRGm5BTrllV78ljbdtDw9UUOjy7wtComnjiyLSmwBU194DdNb/OPM67XbcduNjMjr4KaWfls6TqOLxqmMbc0rRkgq9nKxK2v8Y1Z17Ej2Et3IsYkh5eyaATvi7+C2x7UvSfH5uTTk5cQjUWIxWMIBHabA3uq3ml/L/IvP9arSPR3ob76e5QbP44Y481YePNSq2CYzIa9vllWB/fVzacz7Gdz53FsJhMz8yvJtjrSmkU4G1L2ng+sNyjnC6cHFt+FmHY1atMmkCpK/Wwti52mXtsBpFQh2K8FVA43YoQHqGJnFl+bvpLD/V0c9LVT4symIbuYXJsTZYSWDykl/bEwqpS4zDYsaRo6HI4vGkaVKi6LTe9GNwZ6o+GkwHaA5kAPq5r3cvu46SM+mI4Gi81BrGYa8sP/j8SBLQh/L1Q3IvLLsBg4JGbI8G4j3+Hmy9Ou5bi/m729beTb3TTmlpJjdY7593c5cU7BbXV19Yhl/9HKcPn9fg4cOF12P3z4MFu3biU3N5fS0lLuuusuNm/ezN/+9jcSicRgH21ubi5Wq5WsrCweeOABvvjFL5KXl0dubi5f+tKXmDJlyqB6wqVAIh5DpCr/A+atrxCtmZbUnuC12FleWk+fzc7rCkRLqlAUhQlIphVWGmYKJ2QXYTluMpQEu6Z0gk6KRLiyUaddjbLxBf1BefMNrWEn5RRjM5mJGGir3lo1Fe+wYCqhypQBt4rkmL+bcoNSSUSNc6i/k+ZAL1JKdnSdINfmIqGqaSmbi4JyrK8eIH//Zq4urgarQ5Nc8/cgFt6G9OYx/AxPJOKEo2Febt7Djr42HGYLywvHMyWvzDA4kx3HU2vQHt6haQKPNdPkyYXcktNycUM/Y+NV4DTWzfRa7XitdmqGtZ1cLDwWOw1ZRew1aE1ozClNOWzRIwTrw/2cyC8EKSiJ+JmfVUA6wxzZ343cu17raU4kEBPmwrRlOje+oeTaXeTaXcwqGN2Dfm8kyKbOY7x2cj+xRIKZ+RVcXTqBfLtrxGvt2dAbCbKt6wSvtOwjkogxLbeMFeUTybe7DfvYz5ZUv3OANa0HWV4+MaV+9dlgsTnB5sQ812DgNUOGDOTYnOTYnExNoeLzbuCcgtstW5KnlGOxGFu2bOE///M/+bd/+7dRb2fjxo1JerkDfbAf/vCH+dd//Vf+8pe/AFoLxFBWr17NsmXLAPje976H2Wzm7rvvJhQKsXz5cn79619jOk9Zj3NBJhKIkfRDI0HkMC1Wq8lMvsPNj3a9npSZe6f9CDdVNOqE5VWpsqPrBB+ZsIA/HNw0ODBkVUzcUDGZ9pCPCdmFSe8xW2z4py7FHOrHsnudphMKkF9O8PoHkDYXw0O2XJuTL05Zwc/2rBkse1gUEzdXTqEhW5/pVVFH1KA1mizvj4Z57MAGtgy7Wa5pO8jXpq9Mi2SKyC5EueMLqM/+7LRdqmJCTL8GMWmhoYRUe8jHv29fNaiW0BsN8ciRLUzsPsH9dXPJGj4FP6JmrBzRdWvUn8OVhXL755M/hxCIhvmI+TcnDZNdyrgtNj48YT6/a1rP7t7Tw6CTc0r4YN3c5GGyU3RHAvznjlfoGGpf2qkZlnxp6or02E3396A++X3oOt3DJjc+j9yzDuXer40Y4I6W3kiQn+xew9EhrUSvtOxjfccRvjptJQVpMEzoi4b45b51NA15eHi99QDvdBzlazNWUuQYu4JD/wgamhE1nhZjjQwZMmQYDecU3E6bpnecmj17NqWlpXz729/mjjvuGNV2li1bNuIFbzQXQ7vdzg9/+EOdLNmlhNlmJ1I7A/PRXYbrE1WTEcP60XojQR45sMGw5Pzc8V3MLxqX1LOjnGoWX32iiY82LMKqmEhIiUkovN1+mN5IEMcwfcqusJ//OvAO106cx6zZK1HCQbBYOSFV/ufIFj4zeSleW3LGTBEKVZ5cvjR1BcF4lLhUcZqt5FgcWMz608luslDs8NIaMpZbq80q1C3rigR0gS1oahB/PLSZj09crHN0OxdEQQXK7Q9qQWgsCk430uFFOPRBUSgS4pkj23UyYAB7fO20h/264FYUVaVuGfDk6TSBzxWRVYBy++e1snk0rFn7Oj1jbnkYjoxFIOCDeAQsdnBlJVktj5Vcm4uPTFiIPx4hkohjM5nxmu24rPpWCSkl27tOJAe2p+iKBNjUeYzlZQ1jzkjK5r1Jge0ggV7kzjUw/9Yxa+ke9XcnBbYD+GMRVp3Yw3trZo65nHgy2JcU2A4QSsT429EdfLB2HjaD3+/ZMCW3lFUn9hquq/XmY78I8nIZMmR4d5LWq019fT0bNuh7rt7txBJx/KW1ZHtyNammoVhsRGYsRwWGhmuBeJSeFHp1EsnJYB+Fw4Z/xnsLcJqt/PHQZo6eGvzKsTq5pWoKy0rqdDJEoXiMhUU1dCXifHXfW4QTWh/plNxS3lc7myP9XYZZ0tagj9/tX88BXwegtVDcO342E3OKdUGn1+rg3vGz+f7OV3XbmZhdbDg9bjTpOcCe3lZC8WhagltAy7wNyb6lCoVC8SjbelPrw27sPEZdbmnyQneOZmxxYLN+v1e/L632tcLhSWlRmw6kvwe59mnNOEFNgNmKmLEcZl6bNsvQ3kiI547vZG3rQeJSxSwUlpbUsrJ8MlnDAvVgPMa69sMpt/V2+2EWFI7TGYScDTIa1iylU63fux4x/ZokM5WzJaGqvNV2KOX6TZ3HuKFi8ph7zde3H0m5bktXM3eMmz7m4LbEkUWlK5djgeRrnILgvTWzLlo/d4YMGd59nFPzos/nS/qvr6+PvXv38k//9E/nRQrsckcIgc/moPOWTxGbtFAb9BECddxUeu/8Am1WO8MTTGfKOBnZ1salys/3rh0MbAF6okF+u389/rh+aMxqMhFVEzx3fCfhU9PoEsn27hM8fWQbpQb9mt3hAN/dvmowsAVNxunne99M2u9Qxnny+dLUFVS5NUUIp9nKLZVT+Lv6BYYatCMNuWh/l/T0IJ4VgkH9V5NQKLR7kiZPrUJ/zMLhRln+QcTiO0/rcxZUIO76IqKiQff6SxUZ8qO+/FvkrjdPD8fFo8gNz2v/xVIrboyWUDzKnw9v5vWT+4mfao+JS5VXWpp45ug2wsPUFBQBlhGGs8yKaey9qkKBoQ+E3jytD31guyYzYz0XhdBah1JhNjivzoWh+8ixOim0ewbPZ+1aMvbfVJbNwacnL2Fl2UQK7W4KHR4asor4h+nXUebMHvP2M2TIkGG0nNOjenZ2tu7GIaWkoqKCxx9/PC0HdiWhCIVdPSd5/vgu5lXVs2DKYkwItgd6eO3IVmq9hXxkwoKk97jMNkqdWbQE+3TbsygmQ5ejnd0tKd2Rnj6yjU9OXDLMglfwass+w9c3B3pJGLSFHPB1JEkcDeXPh7bw+cardQGr3WymLquQzzUuI5pIoAiBx2pPaRYwLbeMp49sM1w3O78Sdwoh6v5QP73RMMf6O3FZ7JS7c8iyOrGk6DtNqCq90SAnAn34YiEqXLnk2pyGAbdNsbAov4ois5UpNieW9uNIp4NAThFPdxxldmGV4T6EKwtmX4+YtABUFcwWxBkcqmTYD4E+5MnDYLUjCqu0FoA09s8G4hF80TCH+7uwKCaq3bl4rXZsRiYDwX44vN34WLet1jK4Y5Q180XDbBgirzaUt9oOc33F5KQ2HIfZyrLSevYPecgaytUl9WPOFAqLVcvMWu2ISQuQ3a2gJjTL48M7IKd4zA5oilBYUlLHOyk++1XF40fljHYmFhTV0Bv0cWt+Fc6eVkQ0TKyknm3hfnqFwJOmrGq22cptueW8J6Fq7T7F1Shma9rspjNkyJBhNJxTcLt6dfLkv6IoFBQUUFtbi3mMpa0rkbia4ICvg7hUWdt5nLWdx5PWHwt0E1XjOIc0Jnitdu6fsIDvbFtFRD3d5ymAD9fNI8uSXKZVpcreIYM4wznq1/bhGrKPhFRTBsMArcE+aocFLft6jYX2QZP8MVJqGMBtsaObUDMg2+bkpspGnj22M3m51cGtVVMNy6e9oX5+t/8ddg7pK7QoJj49YSF12UVYhrUxJFSVQ/2d/HDXa0nKD/VZhTwwYaGuDBwRkjtKauG1P2Ae0mbgNZl54PqP4h+hPVwoitaiMApk0Idc82ctSzqwTDEhrn8Aaqbp7I3Phf5omGeP7WT1yabBZQqC99fOYXZBlU75Qfp7hm/iNIk4jGD3OFoC8WjK/mSJJGBwntZ5C2nILtad97XeAhqyi8Z8TADkl0NBJerT/zWoqyoBMe1qRM30tCgZFDu8zCus1rUOlDqzWFBckxbHuCKzjb+zurH+8dtJg4xXTZgDS+5Oi0SQjEeRx/ch//pjGJJpV2umo6zQW0FnyJAhw/ninCLRpUuXpvs4rmhMQiHfrpWl82wuJueWYBIK+/vaaQ70kmN1Gpbiy13Z/NPMG3in4yj7+9opcnhYUlJHns2t08BUhDLixHOO1am7SVqECZNQSEjV8D15BtPmxSNkHbOsjhHbKVSpEknEMQsFywjDJU6zleWlE5iSU8rqliZ8sTAz8ytozCk1nICPx2O83tKUFNiCNoD2471r+dcZ11MwLLjtiQb5r52riQ4Lxpv62nnh+G7uqpmRdMO3oJDYsx7b8P7ZRBzrcz/H+cF/Tvl5Bo4lriawmswjBivy0LakwBYANYF87heID/9fyCs1fuNZsN/XzuqTTZS7spniziUmYUtfG48ceIdqT57OxUbYXakH4wDSYOloO8OwkV3Rr8+yOfjIhAUc83fzxskDSCRLimupcufpenTPFRH0ob75J91yuW01VE9BZI1ddMxjtfPecTNZVDSe1S37iKoJFhSOozar0FDy71ywh/yoz/9C5+xl3rcBUVaPnHb12AN1fy/ymR/qdZ0PbUXuGgdzbkC8i3Q2M5yZcDyGEBhXjDJc8oz2nn4xGPXRDMhyjYZbb731nA7mSmZuQRWV7hwEgk2dx4hLlbkF1dxY6SaRUA3NoBWhUODwcEPFZFaUNWBWlBEDowVF43ixeU+S49YAN1Y26jRovVY78wurWWsw0OIy2wyD5Wl55Tx5ZKuhvNcNFZOTrHcHkFLSFQmwvv0Iu3pa8FodXFvWQLHDm7J0LNGyq+WubOJSYhEm1BQhli8SZHWKoZy4VNnXc5KCYYNxR/q7dIHtAG+2HeTa8olJwb07Fkbdssrw9UgV25GdMMxYAzSps7aQj1ea99IdDTIhq4iFRTXk2V06QX8Z6DN2GdPWIne/hbjqrhTrR4c/FmH1iSa+Mn4OBW3HcG19HUwWVkxawNFiL2taD3BPzaxkLWF3NmQXQm+7foOVE8dcmgdtKLHclU3zUDe+U1S783CnOE+yrA6m5JadkqGTWAyC4HNFJuJIA+OVwfXvPIssq0WkQXLMHYtQ19fFuEAAFYG1twPh8KZNUUPuW29oWQsgN7yAqJsJY5TYk4d36APbgXWbXkJMXjTqCkaGK5veSJD9fR2sadV07peU1FLrLUibSU+G88vAPf2d9iPs7GnBa7FzbdlEip2p7+kXmlHfCW677bZRvU4IMWoTh3cLcamFZVu7mtnVc3rifn9fO8UOLx+ZsJCYTP03U4Q4Y2YLIMfm4hMTF/PLfW8ltQdcU1rPJAMNWqvJzMqKybSH+pN6Fz0WG5+YeBVOk77H02Gy8NEJi/h107qk4HBOQRWNOaWG2Z/WkI+Ht71EcEipckvncW6tnMI1ZRN0EmWBWITnj+1iVUuyrJDHYuMr067TqUSoSEIG9qwDdET0erPdBssGiKkJ4sO/DykhaCxnBiAMAr9wPMbbbYf4w6HT2d6Dvk5eadnHV6ZeqzevUBOa1FYKZG87UqqIMZSp42qCe4vGU/z8/0DP6XK+9+guJlU14lxwCwmpYhoyaypcWSi3fQ71ye+Bb4hkVX4FynX3pyW4c1qs3F+/gJ/tfZP20Gk3u2KHl/vq553xgplOp61BEnHkcHWTofh7k8rv54oM+lBfeQQObMYEmNAe7mR2IcpdX0Kkw3mr17g3GdDsvYdbc58LfSPsIxxIzz4yXPb0RoL89+41SRbx+/raqPHk84mJizMB7mWAdk9/OamtcUtXMzdXTmGFwT39YjDq4FbNXJjOGatiwh+LJAW2A7SGfGztOs7Kikkp398XDRFLJDApCt4RBrFsJjONOaX868wbaQ32EUnEKXfl4LE5DKWz/LEwv216m6mnnIq6wn6tLxZ49MA7fGbSUuyW5HJRdyTA6y1NfHbyMoLxKOFEnEKHm729rbzYvIv3jp+FdUj2LBiL8ocDm5IC2wH+cmwHswqqdD+E3mhIF9gC9MciPH1kK/fVz0+SNbMIZUQt3XqvXkt3nCe1+H6OzakzyRaJ7L4AALtfSURBVMBsgaIqaDMe/BFVk3XLfLEwTxzaolseScR55MA7fHbysuSMpNUOxdVw3FgrVFRPGVNgC+A0mbEd2pYU2A5gOrqTiulXGwaKIrcE5d6vga8L2d+NyCoET07aZMD6oiH+Z+9ariubiNNspTcaJNvqxB+P8D971/K5xqvTVqIfNRYronISstl46JKy2rE7zAGyq8VQLo7edk1Ld94tYx/Iqm6EPeuM1xWNS0trCRUNkCrTnV+m/YYyvOvZ3duaFNgOcKi/k329bcwrGncRjirDaAnGozxxcLPhvM7fju1gjsE9/WJwVjW8cDjMqlWruPnmmwH42te+RiRyWmLKbDbzf//v/8VuH/vQy5VEXKqsH0GT852Ooywrrdfp0AZiEfb0tvLUka10hgM4zVauLWtgcfF4vAblfwCzv4fct54m5+huTWfIm49yzfuR+WWIYdnfQCzKAV8HB3wdmISCx2IjnIgNGhUc8XdTMCxLurWrmWsrJrG27SB7elpRhCDb6uD6iskc6+8hEItitZ3eTyAeZU9f6kG3fX1tuj7ebSPYeG7pbObOcdGkv1WW08tdVVP40V69JmmB3U2pQbm10O6hzJnFCQM1ijurZ+iyB8LhQSy9B/nEw/qDcucgSmp0i4/2dxm2iAAc7u8iEI8kBbfC5kS56k7U338TXZ+K04uoSv0ANFoskRCmVEEOYN35JlQ1gsHQnnDnaJ91zEehpz8a5mTIxyMH3sGimHCZrQTi0cEKRH8sfMGDWyEUaJiH3PgCnHL8G0Qxocy9CTHGEpxUVdj+Wur1O9cgpi7TWkPGgCirQ7qytSxt8hqUpe81NC45630UViK9ecnZ/YF1S+89o0pIhiufQCzC6yf3p1z/2sn9TMkrxZmOh60M54VALMqeETTf9/S2jjibc6E4qzTQb37zG372s58N/vtHP/oRb731Flu2bGHLli387ne/4yc/+UnaD/LyRxrKag2gSn3PbUKqbO48zi/2rqXzlI1rMB7lmaPbeeLgZgIxvW6t7O9G/ePDmtB+sE+7kZ08gPrYv0G3PsAcusuEVPHHI0kOXAmDbH1DdjF/PLSJ9e1H8MfC+OMRjvi7+fmeN1lYXIPQBXMju8wZ7SPVgJu2NWm4zfHufD5eN58cqxNFCASC6TmlPDhpKTkGP7Qsm4PPTl7GrPxKlFPhWpbVwf31C5iUo2/hAO3mLW77HHgHsr4Cqqeg3P0VhCfX4HOM/NkNV+eVIe58SOtxHaBiIsrd/5Ce8rSUoI5wXGqCM31n54Oh/dQxNUFvNJTUWnPRnFuz8lDu+SqUjD+9LK8M5e6vJH9H54xEJkaoiqkq6fg+hDdPO+ahFYasAsTtn9cUIdKA8OSi3PVlqJnGoG6uJw9xy2cQxdVp2UeGyx91hOu7KuXF+61nGDUjfUUj3b8vJGeVuX300Uf5whe+kLTs97//PTU1WtbqkUce4cc//jEPPfRQ+o7wCsCimFmUV87OnhbD9XNzynAOy4f1RUI8dWSr4es3dB7lpqpGXR+ibG4yzJqgJlDXPY2y8oEkS1an2UKlM4eVlZMocnjwxSI4TGakhD8e2sw4jz6YCsQjlDi8fLxuPjGZIJZI4LE6ONDXwcsn9vLxCYuTXu8wW6nx5HGo3+C4wFCyaVpeOX8bJgM2wKScEsMWC6fDxWRLBWXePHqjIWyKGa/FRu4plQojcu0u7qufx+3V04nLBHaThWyrI+XUuLA6EDXTkIVVWiZPMYHDjUiRUTT6+w1Q5sw2/BzCYkNUTULe81VNYksxgd2Vlr5WABweRMM85AbjwTUx5aq0WerKeAyCfUh/r/Y3dWWDK9uwxO61OLCZzEmybAM4zZaUOqxxNUFfNEzfqcxqltVBltWeFmkrOJW9LShHue1zWt+olGB3jkKrOABBHzLYr50fTi/CpX+PUEyIKVchD2wy3n/DvLQ5z4mcIpSbPglhv/YQY3WkXZ5LZBeg3PAxCPWft31cKKS/V/sOo2Gt/cbpSflbzzA6nGYrC4tqOOY3Pt8XFdek7K/3xyL0x8L0xyK4zVY8FntaNKAznB1Os4Vabz4HfJ2G643mey4GZxXcNjU1UV9fP/hvu92OMmSqeu7cuXzmM59J39FdIcQjYapNFsa58zg8rNcox+pkcX4FasQPttM/1GAiaqjtOUBr0EfJEAcxqapIo769AY7tgWg4qUfQa3Xw8UmLeerINjZ1HhtcXuTw8LGGxWQbXDjCsSjXltbzgz1rBs0cFCG4vqSe28sn6Qax3BYb946fzbe3r9Jp4C4triXLqr9Z5NqczC2o0gnb20xm3jtupmE/jy8a5oXju1jd0jSYBcyxOvn05CWUu3JSSpTZTRbsjrML5rSbdfYZX+e12rm+fBIvNO9OWm4WCh+sm6tTr0jahytrTLauKbdrMsHUpVp2f7h+bXENoljfXnEuyEgIuX8T8tVHNTcz0AKdlR+B6sm6cn6W1c4Hxs/hf5v0LRMfqJ1rqMIRicfY0dPCb/evHwyKbYqZD9TOYVpeeZLpw1gRDvdpl7kzIP09qKt+B4c0IxIJkF+OcutnEAbZXlFQgSyrhxNNyStcWYgZK3TtRGNB2J1gP78BmrA50tKLfDGRXS2oz/xwUCFEIjQjlqvuSluf+bsRIQTT8ypY3dJE25DBUdCGR6fklhm+rzsS4Fd719HkOz24W+PJ46MNiw0lKzOcP1wWG/eOn8N/bHtJd09fUlxreK2+GAgpR18EcDgcbN26lQkTJhiu37t3L9OnTyccNnawulTx+XxkZWXR19eH15v+XpFEPEZHXzvNapyOkJ+NnUeJqSpTc0tpyC7GHotQ6crBOiTD0Rby8c8b/wZAiTOLQrub/liEw/2dSOChKcuZMCzrqb76KHLrq1p/3sLbwWyFna/Dsb1a7+29X0vKooRiUZ4+uo3XDHqgcqxOHppyDYXDMlRt/h7+n8FJDXD/+Nk0FlQODqWB1nawrv0wuTYn69uPcMjXicdqY0FhDXGZYFpuuaF2rS8a4kBfB3v72pBS4rXamVc4jnwDCS1VSl5raeIPh/TZALvJwj/PvIG8ETK45xN/LMzR/m62d59AlRKHycKC4hoK7O60ZRfPBenrQu5+C7nnbTCZEdOvQdRM1fpq07H9kwdRH/umwRqBct+/IgxK4aF4jNZgH88e30lr0EepM4sbKxspdngNA9Xj/h7+bcvzhiWyr09fSdUImfPzhYyGkat+h9z7tn5lbgnKe7+EMOgBl/5e5OEdyK2vQDyKmDAXMXkxIiv14GOG84Ps70b9/f8HAX0/vph7I2LBe9L6wPFupDsSYFPHMda2HUIAi4rHMyu/0rCvPhDThkp3G5gUjffk8+nJS5LuORnOPwlVpTPi5+XmvezrbcNtsXF9xSRqPPnnPZs+2njtrH6h5eXl7Ny5M2Vwu337dsrL09O/dUVhMrGm9ySrWpqocucyPa8CkxA09bWz6sRear0FfHrSUobmI91mG3MLqplVUEl7yEezv5cSZxY3VTbyZusBCgyCNTFlCT1TltJns/NO+xEiiTgzrn4fJTYXeUf36DKBfbGQocYtaCYHHeGALrjd092S0oXsbyf2anqjQy40fdEQfzq0mZiaYEZ+BQuLawjFYzx/fBddkQD5NrdhcGtRzBQ7szjm76EvGqLSnYtFMekC24F9PH98l+ExhRMxDvk6DYPbcDxObzTAhvajdEUCTMktY5wnz/B4BuiNBGkO9LKp8xgei515hdXkWJ04U1jjWhUz+XY3XquDjlA/dVmF2BTzRQ1sQevBZO6NiClLQRGINJW+QQvw1Lf/lmotcvMquOaDiGFDaw6zhXHefD46YRERNY7NZNYNWQ4QTcR5qXlPyt6vF47v5u/qFxi62Z0tMuSHvg7tQSAeg4Z5iNxi4wxe0Ifc947xhrpPgr/PUE9WuLO19oTx00+1PrguCcva7mAfh/o62dnXSr7VyezCKnIsDmyXeWZ2JGR3q2FgCyC3vKoN+KWj//0KQSbi0N+DPLQN2o9BSQ2iuhG8uSnVXXJtLpaXNTCvcBwCrcKXqh2sPxYxDGwBDvZ30h+NZILbC4xJ0Uyj7hk/i1A8ikmYcKXRHj4dnNWV/8Ybb+Sf//mfuemmm3SKCKFQiG984xvcdNNNaT3AK4GEqnLilEbqUX83R/3J2pntYb/OftdlsbGyfCLf2/kq/iHDYy+d2MOnJi4x7EHs9mTzast+Vu09LSX1Vvshqt15fLRhIQXDLh5RNTGiXW572MdkSpKWHQ8ZX/QBOsN+ndFCTCYGNWg3dBxlw7BWg9aQjykkl6LC8SjvtB/h9wc3Jn2OIoeHBxuv0QWfCakOtkgY0RzoZc6wZZFEjG1dzfyqad3gEa9rP0yezcVDU5aTb1CC7okE+dHO12gO9g4ue7F5N3dUT+eq4lpdgBtLJNjVc5Kf7XlzUDVhXfthsq0Ovjh1hU6v90IjFBMY9IGOmXgUelLbNMuuFkQ8aqjIAGA3W7Cfwac5qiZoHeFcbA35iKpxbOdmwnj6WIM+5NqnkTteP71wx+vI6kaUlffrs7CxCIw0EOnvRRRVpVwv0mCIkS46Ar18d9dr9AyxV/7biT08UDeXqbkV2KxX6ET7COcusbB2fmcAQEoV2o6g/um7p/8uu9cirXZtgLEw9bmuCDFia9YA4RE0zEFr4ctwcbAoJiyXSBvCcM5KLeHrX/863d3dTJgwgW9/+9s888wz/OUvf+Hhhx9mwoQJ9PT08PWvf/18Hetli1kxUenWT9MPUOL06kwa/LEIv9v/TlJgC1oJ/n/2rcVnoJbQGwmz6oReI/WIv4v17UeIDzPXsCrmEc0hShz6zNQ4V+rPUeTwYho2GGcRppTuUgBlBlms3miY3x/cSLUnj7vGzeCDtXNZUlxLbzTEc8d3ElWTh47MQiHHoHd3gCoDJYO+aJhfNb2ty/x1RQI8eWSr7oIaVxO8emIfbeF+FhXV8IHaOby3Zibjvfk8eWQrPdEgw+mLadqtw+XAeqMhHjuw0VAn8IrAbIWC1BUcUVQJY3zKt5vMVLhSt1CUu7JHZXxyJmRXS3JgO8CRncjDBkOPFrs2BJgCI1WNS5FgOMgfD29JCmxB6x/+3wMb8MVCxm+8EsgrSb3O5tDO7wwa/l7Uv/xYH/BHw6h/+ynSwHHwbHGYLAgg2+rgxorJfLB2LjdXNpJ7qoXBlfk+MhhwVsFtUVERb731FhMnTuSrX/0qt99+O7fddhtf+9rXmDRpEm+++SZFRfrp93c7qpRMyS3FnKJEc3VpPfFhGVR/LGIodA2aCUDHsGZ8gLVtB1Mew5utB3WuXHbFxLKSOsPXF9o9ZBuUHhtySnCkKBXfVjGZrGFtDNk2BzeU6w0OQLtYDR2KG2BndwsfmbCAqbllvHHyAE8d2YovFuYTDYtpD/bjjyYH9llWB7dWTTXch9tio8qtLyHu72tPqUG7pfO47qFioN/5U5OWEE7EefrIdla37KMhu5iPNSxiy5CBvAGO+3uIp8ji7ek9qdvHlYKw2lHm3gRGiriKCTF9+Zh7Fs2KiRVlDYMybkn7R7CyfBLWMe5DxmNaC0Wq9ZtfRgaH/Q5dXkTjVcZvKKwG9+UxjBSIR9luYPQB2vXs8EhuZJc5IrsoZduBmH3DmG2KryiCvtTOjb3tEPSPeRceq533jZ/DHeNmsLe3jSePbGVn90luqZrK+8fPxpNpSchgwFnbHY0bN44XXniBjo4O3n77bd5++206Ojp44YUXBiXBMiSjSpWtnc18ZMLCpIZ5p9nKveNn09TbrtNEPZNWnD6zGB/RgjaUiOlCuZAaZ0ZeBUuL65Jcz6o9eXxy0lW0GgTQOQ43X2xcllRStylm7qmaSp23QPd6RSjMK6zmhorJScF9hSuHh6YsNxwgKHF6Wdd2mL8c3U57uJ9APMrWrmZ+svsNVpRP1H0OIQRT80q5rWpakrtWiTPr/2fvPcPkOMu07bOqujqnyTnnkUY5WLJlWZazsY1tMDZgclqzgIm7C+wu+37vsi/sYrwseQkmeQEDhgUDzpYsy5Js5RxnJE2OPZ1j1fejNaPpqeqRZLVGkl3nceiHqnqqu2e6q+66n+u+Lj7dcZ3uNO1MXVMFFWX6nKWqcnPVHL67bz1bh08QSsYYjoZ44sQenus9SK3O8NJMz6Fy6fgBXhDyShBu/2imw4DDi3Dng1N8gs+PQquTv52zGveUi5tLtvLR9qs14SOvCSWlDW+YSjyqkSAIsgXhitsQ5lwFU29mq1oRb/+byybIIKUqWW/+YObP9uWO4MpDvPvT6UTCCSQTwuKb0lZ5l4AW+lJBPVP8tKK19jtXzKKEKAj86OBGjgWGCSfjdAVH+MmhTSRVJSMR08Bggtf8qcjPz2fZsmW5fC2vW8ySiQqnhw39R/hgy5WIgoCCikkQWdd3GJMgarz97CYzHtlKk7eExYVVqGpaxH3IN8CL/Uc0HU+TaGJBQRXbhk/qvoZ2byk2KXP5xibJfGvPC1xf2cY/LLiRSDKBWZIIJWJ8d9+L/E371ZrjSKJElbuIz7StIphKkFQUnLIFt9mOnGWp2WW2clNFKyuKawknE+kEKkkmL4u1kixK7NcZIEiqCk/17Od9zSs1+5yylesqWllWXEMwEUcWRZyyNaumq2GGAqvc7tF02QVB4LneQ8R1NMpH/cPodSln8rkttDqwS6/f5TTBbIX6+Qjv/Oe05ylC2q/V6c06OHKumCUTbXllfH7eWoKJtN2YUzbjtbmzWr+dC4LZitC8FDVbHHLDAtC5OROcXlhzH8LyW9NexbI17ZGaY8siNZlIDz7FI+mlcpsrbfWVA2ySiRKbS2PXNEFjTgIsLl2EvBLEOz8JkSAkY2B1gN2DcIkNzZwN0WQCfyJKLJXEZpJxm605KwgFpxdVlE6Fv0xDtp61fd5MjMejPNapb3P5eNdO5hdUUijlxg0nnkriT0SJJBNYJBMu2YrNiI2+LDFueWaJNk8pLtnKTw9vpj+SXsbxmm3cUTOfJk9xRscR0kvtfzv3GjYOHONHB18moaQQgLn55XyyY62u3Uadq4Ayu5u+actEZlHi1uq5uC2ZP+O12Hlb4xK+uVerKVxaVIMnS2GojvTi/MsPcA6eGg6zOhGueRtq/QLdi2sgHGDTUBd/6tk/mYDW7C7iXY3LKNKZON87lj3a7/D4IIks3QBZkiiQnBScxSpVQlFo85ZqimgBgVur52q6VnElxb4ZXtfOkW7m5pdnbHObbSwtrOYVHcnCvfWL8byOJ84BBFEEV3763wVATSUR+o/h/uuPcE8sk7sL4cb3opY15CSMQqjr0I+UtdgRFq7N+hyC2QoX0BJHDftRdzyH+uqTp/SOAtR1IK59Z06S7Dx2N/fVLuQ/96/X9G8X5JXheQMsBQt2F1xCA36vhbFYmF8f3cr2kW7UUw2Va8qbubGyLWuE+zlhcyMsvxX15f/V7BKuuisnEo7AqcJcj4SSwh+PUpgDq0d/PMJfT+5lXd8RkqqCgMCiwireWr9o1qO/Dc6fc5YlGLw2EqrC9/dvYCDip8TmotzuIZCI8dPDmwgltdpLRVXYOnSC53sPTToaqMDu0V5+fWyrbmytLAg80Laaa8tbsJtkJEFkfn4Fn5t/A2ZR/0/d5C7ms/OuY15eOYsLq+nIq+De+sXcU79INylG9Y+g/PorMHg8HT9aWAHJGOpff4jar7UVSykpto9285sTuzOifQ/5h3h43zrGdPRaMw0CSYKYk87fUCTAypJ6bqmag1u2IiLQ6C7iI21X8erQcd0IyOk3IFPR03c64lHeanVzb0VrOhYYgXpnPp9pWErj+AhqKrtThcFZMD6E8th/wFT9p38Y9bcPZW47DwR3AeJbP4ew4Np0J0oyQesViG//IlwkD1o1lUTdtQ510x+nDPKo0LkL5Q/fRM1iY3Wu1LkK+Nzca2l0FSIi4DXbeGt1R1rneJGdPgzOTCAe5UcHNrJt5OTkzXpSVXim5wBPdu8nnqVgPBcEsyXtkX3LhyGvBAQhHSF+x8cQ2pbnxA9YOsP5Ptssy7kQTyX584m9PNt7aHJOQkVl6/AJHjn08ut2PuL1jNG5nQUUVWXLYBeLCqtZWlRDd2iMpKJQ7cznqH+IPx7fzQdbV2KdMvXpi0d4tveg7vE6AyP44hFN93b7SA+PH9vB/c1pdwFID6b94MAGnLKVB9qv1hSsVpOM12JnbWUrXYER8i0Oapz5WZdi1O5DJCqa8S++nmPxCOFUkka7G29/F44Nv0Mtrs7QFfojQf7YvV/3WMPRIAMRP3nTdIhz8sr4w/Fduj+zsLAKcw48Ypu9JfzTq3+kyVPMbTUdWCWZ3vA4vzjyCnWuApzTlh89spVlxbW82H9E93hLi3Qsb8aHcT7+n6wqa2DBvNWoNifycDf2P30vbRl1/5d0B1eCiRi+eJgj40NYJBMN7iI8ZiuWLIN8KSWF4h9FHTqBOj6MWFwD3iLkHHpxqqrKWCxMT9jHYCRIpcNLic2F9yJ1NNRkEnX7s/rLoUoq3dFc+w6EHExSC55CuPoehKW3AKc8aGdwAHktqPEohMZRew6nQxwqmtMSDr1l3dA46qt/1T/Q0AkIjOYk3c5qsVFvsfE3VidxNYWIgMvqQLrIHs2vF1RFgeAo6uDJ9MpAcTWCtzhnccX+RDQj0WsqL/QeYk15c06W8wWbC6F1GWpVS/r7KJlyqi13yVa8Zhs+Hf17riJ4x+MR1mc5tx/wDeBPRGd0/TG49DCK21kgqaSwSjJFViff3PtCxjLfsqJaFhZWEU0lM4rbaCoxowftUDRA1ZQ0KUVVODQ+QJwUP9SJMLXEI8SVFNNVf0ORAN/Z9yI9U7xbLZKJj7avpt5dgDxNmxVPxNiz4Bp+2Lk1Y+hqnqeYd6y4Hc+0bkBcSc3oQXsyMEprQaZt1GAkwI2V7Tw5LbY232JnZUl9VgeCc8EtW7mvYQmPHn2VQ+ODGdvfUr8o428BYDHJ3FjZxkHfAIPRTB3ideUteHWW+NSRXgCEvqO4+3ScLHS6Af54hF8d3cqrU6QMAgLvalrGosIqzetKKSnUgeOIv3sIYlNO/nmlJO/8BKYcaSN7wj4e2vVcxipDodXBg3Ovzc3w1rmSiKL26QeQAKj9nQjxaM5smwSTDK7cpLdNR41FUA9uQX32Z0wsGagAbSsQr36rNiwiHk3/y3a80X6E0rqcvT5nDnSTBplM+sP+9qGMoUU1vxzxrgdzIi0ZjWntCSdIqgqRHA8FXqhYYq/FzofaruLru5/LuCaaBJEPtl6Zk7jXSCox44DvWCxMuY6zj8Gli1HczgKSKFLh8PLwnuc0+7YMddHoLtII/E2ChIigCUWYYLr9iSiIlNm9bB/pptlTzNqSBiRBZLevn3UDRymwODIcESAdDfvbzu0ZhS2krca+vW8dn19wEyXT7sB91a38YM/zGk3qrvFBGh35XCfJTO3rmEQJi2TKqpnSK4ySqkJSSfGRtlXsHOkmnIzT5CmmwOrgt53b+dica3SPdS5YTTLLimtp8hRzMjhGQkmlrckc3qxZ5UU2Fx+few1H/ENsHz6JzWRmVWkjhVaHfgfTc+oCZXWSaFlKwuHGMngC6ehOEEXdwmvHSHdGYQvp5bGfHN5MrauA8mk/o/hHEX//n5mFLcBYPzz3C+I3fwBzluJTVVXCyQSiALYZisB0eMU6jXxmOBrixwdf5oE5q2e/q2GS07KYweP6+z1Fl48fqX8Y9Zmfarfvfxm1ug1hzpWZ203mtJdutpvfy8RL9w1NYAzl8Ye1bhyjvSjPP4p40wcQzlOT757hOylA1pWgS5FaZwH/tOgWXhk6TldghGpHHsuKaymwOnIyPGoRTQiQ1R9ELzTJ4NLGKG5nAUVR2DzYmXX/i/1HmFdQkZFQJgoC8wsq2T6idT/It9h1vWaXF9ey0FvKcDTIswNHiadSLMkr418W3sRoIqZxDggmYuwc6dF9TdFUkr7wuKa43eYfymoR9MxQF8srW/FO2ea22FlTUs9few9pHm+TZKp0wi2aPMU8euQVXuw/QnteGVaTzMaBY/SGx7mrdkHWQbdzJaWqBBIxtgwdx5+I0O4to8TuRlHVrCfMIpuLIpuLxYXVSIKIlEXLDGm/zPBVd9FX3shTY734klGamxdx9dKbKeg+pEkHG49HeDKLhANg48Ax3lK/KHOjfzg90a33/F17ESLBtEvBNEZjIXaOdLNpsAuTILKmvJkmd7HukJs/HtENqYB0/GUwEZv14laQLYhLbkQ59IrufnHZzemhrkscVVVRd+mEREzsf+UvqHUdmcu8DjdC2wrUvRu0P+DwIni1lnwGlxaqbwCiIf2dx3amHUbOs7j1mO0UWZ0MRbXnh3n5lZdVwSaJIsU2F7dWzyWpKJhmOO++FlxmK3Pzy9k92qvZV2Jz5aQ7bDC7GMXtLBBXUrp6oQkCiagmxCGSjLOipI5AIsoR/+nhmAKLg3c0LmU4GqLSmblMakXldyf3stN3eqq/KzjCswPH+MzcNZrnTSiprJ1hQPc1j8SynJBPvY/p/rCySWZNWQuDkSDbxk6fOFyylY+1XYVXZ8kzz2LnwY5r+dbedewY6Z7cvqK4jhUldYg5GCAIJ+M83b2fv06RPpwIjvFC3yE+N/8G3eS0qZxNQEDUYmNDSTWPd27NeI51Iyf5bMe11EzvwqoqgRkkHCOxkKbwVsKBGaZCVd2o0NFoiK/tfpbhKRe9I/4hWr2lvK95habAPZOn6fTEuFkjrxTh+nejPvcLmFgZECWEa+6Fac4VlyxKCjUwmn1/2A/TBg8F2QJXvjn9cyemSHeceYh3ffKySUF7QzM9/GMqqgpn8o89C7wWGx+bcw3/tfeFjAK3wVXIvQ2LZ1ytuZTJdWELaevNtzcs5fuJl+gMDE9uL7a6+Oica4zi9jLEKG5nAZsk0+Ip0fVuBahzFWo8T22SmW8eXsd7m6/AabYyHA3hka1IosgP9m/kQ+1Xao7THwlkFLYTjMXDPNd7kDfXzsc85W7dLKZ9/LIVVHpd1bneUjYM6Gsd65wFmHU6nl67i/ublnFHMkEslcQkithFEx6rA1HnRCUJInWuAv5x0S2Ek3GSioJVknGbLTk7IY/HIxmF7QTRVJJfHd3Kh9tW4ThPT8tAIsbvT+zWbE8oKX5+9FU+3nIVrilWQxbJRIOrkH1ZPicd+RWajrJ4pqjQabHEKVXhpYFjGYXtBAd8/ZwIjdJhqcjYnm/J7s8qixL2i3SRFCw2aL2CSN08wqcCTOwmGbvZlvOBrwuFIJkQ6uahdh8kNncVoboOVMA63I1j2zNQVK1rKSY48xBv/XB6uGx8OG1b5cxDOIMuOJpKEEzEUFQVmySf1TDOeCxMTEkhCSJus3VG15DZIp5KEkhESakqFsl02RUfQkF59raC1ZEzG7kSu5vPzLsOXzzCeDxCgdWBR7blZAjr9Ua+1cED7Vfjj0cYiYXwmm14zfbXvWXj6xWjuJ0FRFFkUWEVT/cc0OgWJUHk1uo5OMyZF+M8i5VPz1vLId8gPzu8hZiSAlRWlNTziY5rME27wKRSKV4a7Mr6GjaPnGRteTMFUy76kiBye00HvziiXdptchdh1+lO1iBQYHFoOrgC8JbSBhyJmMbYPpKMczg4yq+PbWM4GkQWJVYU13FL9dys/oG+WIQ/Ht/N5qEuUqpCqc3N2xoWU+8qxJoD/9KDvoHs+8YHCCfj513cdvqHsl7ATgTHCKUSTBUM2E1m7qxbwP7tT2qkHx6zjVaPNtpatbtJ1XUgdWqL6NTy25CmTV4HEzFeznJzArC+7wht3tKMz5fLbGV5US2bh7o0j7+psh2PfHFO/oqq0h8P86tjWzkwnv57NrmLua9xCWUmOScd/tlAqJ/PcGkNvx3sYueJXaio1Djzue9NH6HC6kDKcnEVbE6wOREKK3T3T2c4GuTxzh1sGz6Jgkqlw8t9DUuodubrrkSEk3EO+gb4Tec2hqMhZFHiypJ6bqqac1F9P8di6WTAlwc6SaoKxTYX99QvptFddPkY7ju9UD0HTuzV7lt5JzhzN7zotdgvmqvJ5YbbnA7+mb4qanD5cXmc/V8HlNjdfHreWlq9pwuUdATttRlRthOoqsDu0V5+cfQVRuNhQskYoWScZ3oO8Otj21D1jFhnQFVBmPYjKWXiwrBoctpfFiVWltTzpuoO3Thf7+HtfLJqLovyyhBOpXKV2Nx8on4x5TvXoWcQe2h8kG/vWz/ZLUwoKdb3H+G7+17EryN98MUi/NfeF9g4eGxygrU/4uc/9zzP8eAMS7jngHpGx4Vz+/3milJb+nMyMZkrAPPyK/jMvOvI1xl0Mzs8CGvfRWrhdacHqOxuUmvfCa3LkaZf7M/ibU1/iN1k5u66hdxU2T7pQewwWbinfhGry5qQL1Ic6Ug0yFd2Pj1Z2AIc9g/ylZ1PMZxNz3gJMiqb+Y+uHezw9U3e1BwPjvLvR19hKEcd6NFoiId2PcurwycmpUjdIR//setZesP6vrj7x/r57v4XGY6GsEgmUqrCC32H+e8DG/DP4NZwIRmPR/jW3vW82H900jVlMBLgm3tf4Kg/N97Gs0HIJDN49d0k5q0+/b11eAhf+3YGqppRcxPiZ2DwhsXo3M4iJTYX72q6glgqgaKqWEwmPLJNt2syFg/z55N7dI+za7SH22o6yJ9i7CVJEiuLatiik4YFsKygAsf0DpAAPzywkQKrg9tqOrBJZlRUdox08429L/DZeddpjiPUdeBd9xhvufY+3lTdQVJVsAGu/ZuxqGkP0KmMx8L8+ph+dGJXcIThaEiTlNMXHs96wf3V0a18suNa3WW1SDKOPxFjJBrEKsnkWWx4zHbd4bBWV3YD/iZXIfZzvHnQo9ZdlHUCt8qRh0Ony2SWTDR5ivlUx1rCqTiiIOI0mWeUY8jufFJX3YWycC2kEmAyI7nydf1ILaJpchVBj+XFtbrLzh6LjdtqOri6rImkkkKWJLxmW867o/54FH88wngigtdsx53FxzKlpOUVUZ0bsFgqybrew9xZN1+zwnEpst/Xx7jOTV5KVfjfE7t5b/OK816t6AyM6OrlVVR+17mDj7Stwj5lpcIXC/Pbru3cWdrEIocXU9CHarHTLwr8sv8oo7GQbrS1oqr4YmF88QjRVIJCqxOXnDs50WAkwMnQmO6+Xx/bSrUzLzfJWxeY8XiU/3toI8vK61jduhxZVfCrKk+MdjNweBNfWHiTrhwomkzLMUZiQSRBpMDiwG22Xhafc4M0aiQIYT9qYBTB5gKHJ2fexganMYrbWSKWSrJ/rI8fHXp50hZLEkTeXDuPK0saNOEKkVSC8AxDBf1hP9XTNLFlNjdzPSXsGc9ccveabVxX0YZl2nMoqPgTUfyJKJ2BafGipL1N69yZRWDKW0zXtffynaOvEDo1aCQgsLa4lhs7VmnsZ6JKUlffOcFR/xD1057jQBbN6cRriilJpve6/fG0jOHF/iOTxaRLtvLR9qupceVrijBXwMd1Zc0805fp4mARTdxbOx97PAbnuZLnMll4U0Ubf+zJdECQRYl31i+eMeXJZbbi4ux1cZJsQToLT9uokqA9r4xtwyc1xU6DuwiTIBFPJXVvuEyilNUmLRcMR4N8d9+LGcVLrTOfD7Wt0jxvJJVg75h2snmCfb4+bkq24zJf2hf9pKJkdSyBdNx0JJU47+J252h31n1H/UPElGSGW0s0leTd5W3UbnsG0+HTA5FeZx6fvOWDHAn7qXVlerGmVIXjgRG+tW/9ZKKTAKwua0rHf+eg6Dw8rh9KADAQCWS1HLzU6An5UFSVTSMn2aTjiBNNJmBa0z6YiLG+7zB/PLF7cnDXKpn4QOuVtHhKzmrI1eDiogbHUJ56BLrSjSsVwFuM+OaPI+TPMD9hcM4YsoRZYiQa4rv7N2ScfFOqwm87d+gutZ9paEPPesnr8PDOhqV8oHEZ9a5CKh1e3lzZzmfnXkuhjsG2JIiT0gI93Dr58WOCwMOHNk0WtpDu/jwz2MkenalvSRBnjEfU6/7MpOezSCbEaa9ZURU2D3axfkphC2n3hq/veU7XzNwhm7kxFODjtQtp9RRTYfeytqiWLzYsofToTtQcXCjsVjvXlDXxqfbVtHtLKLd7WF1cxxfn30DlRbpTlwSRJ0/u496GJdxSNYcqRx61rgLeWreIq0sb2TvWm5M4y3MlEI/ygwMvabpyXcFRfnxQG39pEkVcp7S+DpOFhQWVLCo4bW/klq0XZKr6XDmT/EUUBPLM2T/vTtl6xvjRs2GmoUCnbNGcBeyCQPWhrRmFLQDBMTx//DbteueGWJiHdj+X8bdSgRf6DrNt+OQ5S6n08J76XZkEkbl55SwpqqHUlrZJk0XpstFZz2TDJYBuJ/aYf5g/HN+V4UgTTSX59t71MwY2GFwaqPEo6obfTRa2k/gGUX73ddSg/oqEwWvDuNWbBVKqwov9h7P6wz5xYjc1zvyM7q1ZlGjxlHBwXDv45DBZyM9SAMpmC+XuAtZIEoqqUmB1YsrS9ZEEgXn55ewc1XaOLJKJAqvWpmv3WE/WhLA/ndjNnPyyjMllSRBYXFitO4xkEkQq7F7N9va8sqwBFqtLmzQF8Xg8qkkzmyCWSnLUP0ThtPeijg/hePbntDq91LZeQcrqwHp4B9LRHWAyIzQu0j3eueK0OWmxOal25pFIJbHJVuTzHFQ7H1xmKytL6/nWvnU0uYvoyK845aBwlN7wOJ9fcKOug8WFJpCI6a4eQFpHG5wWf2mV0olxy535NKZSuA9tRVAV/M1LOGaSsbsLL5rVkaqq4B9B7dyFevIAQmEFQstycBekk86mIAoCq8oaWdd/WPdYN1a25aTjuby4lr+c1BleAq6raNU8hyMegz0v6h8sEsTqG4JpnaYDYwNZUxX/fHIvCwoqz3uwqclTzKrSRublV7B7tIdgMsaqskbyLXZOBEZz5oF9oSmxubFJsu5cw9z8ck1ITzAR4086ziuQXoHbOHCMO2vnI+TgRsjgAhEOoB7YrL/PPwL+0ZwOEr7RMYrbWSClKPSF/Vn3D0VDmotCPKXw9oYl/Ne+dRnL+lbJxAPtq0gktCfFYCLKn0/s5dnegxnbqxx5fHTOak1HNKGkWFXWxEAkQH/k9OuTRYl3NS1nKBLQ+L32BH1Z38dILERKySx840qKJUU19IR9dIdO/6xJELm/aTm9oXHNZKrdZOa9LSv48aGXM7oU9a5CVpc1aZ0iVIWATpTtBL0hHf2u79TwSdCH9dW/Zu6LR7KnP71GbBY7l4oSsM1byvLiWjYPdnF4yhDO3bULL06ULuhqZ6cS0VlurpfMsHMd4tEdk9s8BzYzv3YuXP/uXL/Es2e4G+XXX5lMjVMPb0Xd/ATCHR+D6jaEaasChVYHb6tfzK+Pbc24nVtcWE1Hjvx688x23t20nJ8e3pJxk92RV86y4lqtxVwqiTLDd0oY1w5v9U5LOpzKeDxCKgedW7skU2b38K19p4Mvtg2fJM9i55Nzr71stKdei42Pzb2G/9z9PLEpPtHFNhf3NizRuD4kldSMQ5I9IR8pVcEkXB7v/w1JMjbjdUUNjCLQMIsv6PWNUdzOAiZRot5VmNXnttLhnZxCnyBPSWFZ90sevOpu+lMJukKjFFuc1Nrd5L/8R5RlN2uOMxQNaQpbgJOhMTYOHOOmqvaMCF5ZNPHokVd4U/VcTKJET2gMj9lOgdXBU937uLtuoeZYDZ4iNgwc1X0f5XaPRk4hixK/PraVGyrbsEoy3SEfbtlKsc3FMz0HuLlqjuY4wUSUzYNdfKZj7eQFURJEesM+nurex1vqF2Xoy0ynBiuyBUzUuXRy2ourAVDLGgh0rEK12LEMHMe6a92paNzcWgqpofH0iU22IFwg3epwJIiCilmUZuyQuc027qlbxA0Vbez39adXCbyleMy2i2alNJNXrgA4dPZLA12oUwrbCcSuPQi9R6Fl9sMM1LAf5S8/0MYhKynUJ76L8K7/A+7Mz6PNZGZlSR3zCyrwxSOoaloq4JTNOHWW/18LVpPM4qIaGj3FHPQNEE7FafWWkm+263uemszp4dAsBZWe/Vi9u1D3/ANpM/xcyF2CyRiPHduq2T4WC/PHE7u5v3EZlsvADkwURGpdBfzT4ls4HhhhKBqi1pVPqc2t+901SyYqHV7dlTyARndRTgv7eCo5KT1zyZbL5qbhkka2pr9XWUJxLnayoKIq+OPRtM+2JF8+tnpZMIrbWUAUBK4oqeOpnv2aDq0A3FYzT7OEak1EUQ9vJf/wVvLzSmgvrYfxIeg9kj5my1LIK518vKqqbOg7kvU1rO87zJUl9RknTq/ZyqrSBn56eDMOk5lCq5NQMsZwNESpza1Zygdo8ZTgMJkzNLcT3Fm3QHOh9JrtXFfRyi+OvILdJFNkdRFJJhiMBiiwOCidFu8LsHu0lxUldewe62PTQCehZIwmTzFrylvYNnySYCJG/pTi1muxc0ftPH508GXNsdyylWqdxCYhvwz/nZ9gu5rk6eGT+Md7aXIX8Oa7Pk5pKIg5R5pYNTSOemwn6pY/Q2gcSmsRV70VCioQzLmzedo71sfTPfsZjYWpcuRxe808Kh3erGbtTrMV5yXk5+iWrczPr9CVyCwurNYs06qxCOq2p7MeT932NGpN+wW7kchKJAjDWYa34lHU8SEEt/ZmK5JK8mLfETYMHCWupJifX8GbajqwmyxZo6DPFYtkotjm0rUe1OD0ICx/E+q6X2n3eYoQ8rSey3WuQlyyRXcV5c2183Nihr/f15/VzW7r8AnurJ1/WRS3kL5hL7Q6dc+z07GbzNxRM4+v7tJ+5i2iiSVF1Tl5TaqqMhwN8peTe9k6fBJJEFhRXM/aihZdK0KDc8DhQVi4FvWVv2j3FVVfVEmCLxbm5cFOnu89RDSZoC2vlDtq5lFsc122NzaXh/r+dUCBxcGnOtZSbD19YXHJVj7ctooym7bAE6Yu748NwP6XJwtbQHP3pwKRVPaY1FgqqbkoSKLEVaWN3Fw1h7iS4nhwlOFoiBZPCR+bqx85mG+x85l512VoZe0mmfubltGoY68lCgKLi6p5c818korC8eAog9EADa5CHuy4Vnd4rNLh5YW+w/zl5F7G4mHiSoq9Y318Z996rilv1r24zckr5576RVinFL01znw+Pe863WGaoMXGo+ExftlzgJFYWhayb3yQ/3d4CycLShFy0GVSoyHUF3+D+vRP0jcmyTh0H0L5ny+j9ma/ETkX/PEIfz65l58f2cJAJEBCSXEsMMzDe57jgK8fRTmTn++lgV028/bGpSwtqpkcchQRuKK4lrfWL9J2ERQlawcEgEQs/ZjZ5kxyFh0HlLFYmG/seY6/dO8jkIgRSyXZMnScL2//K0PRGWJaLyCCKCG0XYGw8s2nfVgBypsQ7/4Ugs6FuMDq4NPzrqPKcXqfVZK5r2FJhr/3+TCTfEVRVU389+uJcoeXD7VelaE9L7a5+NS8tTkrPEdiIf5tx1OTNnuhZJxneg/wtd3PMDpD9LrBmRFMMsKi6xEW3QBTV2pr5iLe8bcIOkPfs8F4LML39m/g9107GY9HiClJdox086/b/8pA5OKcf3KB0bmdJSRRpN5dyN/OWU3klM+tw2Qmz2zDrNNpSJitSA4vTNGpnkZAnTbMIQoCy4vrOB4c486iOqokGUFJ4ZdM/HHkBF6rQ3fp1222cmNlG1cU1xFKxrBIJpwmS9ZlbUEQKHd4ebDjWoKJGEk1hdNkwWO2IWUZRHLJVtaUNbOwsCr9HKIJh2zJ6oogCaKu5U9KVXiyez8fatVGDztlC1eVNjA3r5xQMo4sSjhM5qwn/bF4WLdLqKDyP8e28uBcfS/dcyLkQ923UWeHivrcz1Hv+Ttdf8OUqjAeixBMxpAEEadsyRovGkzE2NCvXyg/1rmdWlfBRdPRqqoCQV+6mykIYHPN6Ofotdh5R+Mybq+ZRzSVOBW5bMUq6XTirDaElqWo/Z26xxJalmo8l2cFqwNsLtC7KAiCbsfzmH+YpKLy4eoOKkQTgqIwLkn8YeTkpLPFxbB5EuxuUotvQG1ZBrEwmMyoNifSDBfhMruHT7RdRSARJ6mmcJgseCw2TDka7mv1lgI7dffVOPMv+6XUmbCZZBYWVlLvLiCYiKU9sGc4N5wrSSXFC72HNSmaAMPREHtH+1hV1piT53qjIjg8cOWdCAuvhWgYzBawuxEuYoJcX2ScY4FhzfakqvDbzu18oPXKixaxfj4Yxe0sEUnEORka4yeHN08OiLlkC/fUL6bZU6wpJn2yGeuqu3H89YeaY8XmX4NPgOmjJvWOfD5X1oLz6Z+ku72kuykfuOpuKG/V6Hoh3fl7tucgz/QcmHRBaHIX8Z6WFTMul03EFJ4NgUSU9X1H+MvJvZOyjFpXAe9vWam7RLpvBp/bo/4hXbeGYCLGpoFj/O/x3ZMDGlWOPD7QupJSu/ZifGgGv8zukI9IKnFOPrN6qH1d2Xf6BtODa3gzNkeScXaN9PDLY1sJn+pMltjcfKB1JZUOr8bqqCfky7pMOx6PTB5jtlETMdTug6hPPZKWYwC4ChBuej9CeYNmqGoCm+nstF6CIELTEtStT8N0Cx2HN911vBhWYE4vwrXvQH3iu5pdwpKbYNoqTUpR6Bof4lMlDbif+Vn6cwEUWJ18+Oq38DwC4WT8ohS3gXiU53sPZcipGlyFvLdlRdYbJnWkF/sT38M+Ic0wWxGuvAu1dXk6Lvg8KbA46MgvZ/dopsexKAjc17AkZxrlSxVREMmzOMibwdrttRJOxtmu47k7wZahLpYW1eQk/vyNjCCbwVMEF6dRq2H7cPa/+b6xPqLJxGVZ3BqyhFliLB7hG3tfyHA+CCRi/PDgRoZ0Qg4UVJ4QFAJ3fgK1bh5UtkB1O6Hr7md343z08rucsRDOx78BY4NQWAkltZBMYH/mpzh8Op1QJcWL/Uf5a/e+jILxsH+Ib+x5gfHpQzGvAUVVeXXoOP97fFeG3rgrMMLXdz/LmI4/o26n7hQmQdT43ALsGe3lsc7tGZPHJ0NjfG3Xs4zqDMXYZngOASE3OsczFf860ocTwVF+dOjljKJ0IOLnP3Y9w2hU+7uyzPA+gIwBwlllbAD19984XdgCBEZQf/u1tEQjBwjuAsS3/T3CwuvSHVOrA2HBtYj3/j2CWyuRmQ0EQUSonYtwz99BWQPIFsgvQ7jlwwiLb0SwWKc9Hq5zF+H+/X9NFrYARIPYn3qE1bJd9/N+oUmpCpsGO3ni5J6M7+3RU5IXn873VvWPpF0ipmqO41HU5x9FPbFf8/jXgsts5f7G5dxbv5gCiwOLZGJuXjmfX3ATlY5LQz9+uSIIQoasazpWSc6Z/tvg0mGmwtUsmrhc/+RG53YWSKZSvDxwLKsH5BMn9vCe5isyurdO2UIYgUBRJd1Xv4XjwVGKrA4qnPkc6N7Pm0rqNcdRj2wn1ryYQMfVHIiME1EU2uwe8gaO49j4e9Tb/zaje+KLR3mqW/+iMxDxMxwL6g6BxFIJxuNRDvoGCCVjtHhLJ2MgpzMeD/PECf0Y4dFYmP7wuEae0OYt5XHdn4BFhdUaU3tfLMz/Ht+l+3h/IsqJ4KhGntDkKUZA0PUeXlBQgdN0/sNeQkk1qijp6zCr2mBaJyuYiPG7Lv0l11gqyc7RbtZWtGZsL7G5MIsScZ3nSHsnz/4dt5qIoWx5AvT0j0oKdecLcPVbs3ZvzwmLHRauRWheAqjg8MIMoQizgWCxIVQ2o77542ldsGRC0BmchHQnztG5K6t+2LHlz4h3/O2FfLm6jMcjWX1xh6MhBiMBzWqT2ncsLUHRQX3pd6iVzTnRFXosNq4pT8uc0pPdpovmafx6wiVbuba8hZ8e1vdivba82UhBuwhEkwnGExEO+AaIJhNplxNLFpeT18CSohr+nOW7flVpA07T5bkaYnxSZ4FoKpE1Dx2gNzyuiY10ylZurZnLf+19IcPf0CKZ+Nica3BNO5mrikLEZOaVpkX88uiWyZLtcWC+p4S3L74Rz7QLaFxJzjig0R/20+DOtCeJJhPsGOnmkUObphSGO2nzlvLelhUa/VdcSc3oQXsyNEZbXqZ+eCDs503VHRrT8kKrkyuKa0lMkyUkVSWrDRhAZ2CEBYVVGds8Zhv3Ny3TnMjzzHburluYm6U3hxfh5g+iPvE9mFpE292Ia9+pmeRPKCn6wno9+TSHx4dYU96S0T3xWmy8t2UF/73/pYzQC4fJzP1Nyy7I8uUZScRgMPtSl9rfiZCIZw5VvAbUSAB1y19Qtz6ZuWPBWrjiNgT7xdEaT3A2y/BqKonQdyz7MUZ60r+rWdYPx1MpXUeUCXpCPpqnD4n16VsEAumudA6jcQVBOO9ACAMtc/LKafWUcGCa5dgVxbVGZ/wiEEkmeGWoi0ePvJLRhpmXX8E7G5flxIEkz2znjpp5/GFag6jE5ua6ylZk6fJ0SzCK21nALJkosbmz+twWWh2aO2JfLMKjR17VGHfHUkm+s289f7/gRoqnFGCCKDJW1cz/7H1Bc/yd4wO0uwq52pQZsymLErIoZe0o62luffEIjxx6WdPv3O/rZ2P/MW6sasvQhYqIWCU5axFdZNUWIIqg4o9H+Gj7anaP9RBOxGnyFOOQLfymczsfn7sm4/GSIOKSLUQSCW6pnkO1K594KsmzPQc5GhimfFoQBaRvEhYXVlHnKmTT4DFGoiHmF1TS6C7K2eSxYJKhfh7Ce/+VyFg/yVQSq9WB7CnStYOa8OvtzVLgVjm8mmVBiyTT5injHxfdzObBLoaiQRrdRczJK6PoLCyGLggmGbzF4NP35BTySnPiI6wOdWsLW0Dd8Wz6914797yf40IjSCaEwgrUY/ode9yFM/6u1GQc4lGQZIQcXOgmkEUJi2jKkPlMRddObML7trKZ6JIbSclWbD2HETf9b1prfJlaCr2R8FpsvK91JX3hcTYOHEMWJFaW1lNsdeWsU2hw9ozGQvziyCua7btGe9g2fIJrypvPO5XOLptZXd5ER34FGweOEkjEWFJYQ40rP+vQ9+XARdXcrl+/nttuu43y8nIEQeD3v/99xn5VVfnSl75EeXk5NpuNa665hr17M9vnsViMj33sYxQWFuJwOLj99tvp7s7iM3mRkASRxUXVkxZH07mmrBl52vBLKBnLatgdSsYztLsTvDRtyGIqTw93EZAyn8NttnJViX4iisds072AbR06nnWA6ZneA/jj0YxtIumlDT0cJrOuTVeTu4jNQ118b/+LjERDKKg823OQHxx4iatKGzTdYY/ZxjsblvH5RTcRSMb4XecOnu89xJryFj4773oasugvrSYz5Q4Pd9Ut5AOtV7KsuDbnXo4h4KCg8v3QMA/5+/l11M+ASSapc0PhMlu5raZD9ziSILKkuEZ3n02WKXd4ubNuAR9oWcm1FS2U2N0XJUYXQDDbEJe/KdtehMXXa2JozxU1HkWdniw3df8rf0HNgWZ8NhDar8xa+Akr79DtAKupJOpIH+pzj6I89u8of/oOyskDqDOkWJ0LHrOVa8qbdfe5ZAtlOkOaQlUroXf+M7uvfgvfCgzx9cGj/LGwlNH3/z/U698FF8nqyODc8JhttHpLeV/LSu5vXk6Du8gobC8SLw9kX9V5qmc/4/HcnOMcJgtVzjze1rCE97esZEFh5WVd2MJFLm5DoRDz58/nm9/8pu7+r371qzz00EN885vf5JVXXqG0tJTrr7+eQOC0zc6DDz7I448/zi9/+Us2bNhAMBjkTW96E6lUbuNTz4ekqrB/rI93Ni3LEOybBJFbq+cyFA2QnObJma2bOkFw2lK/oiqMxbVDHlMfr0xbzjeLJtaUNzMvPzNtqNDq4CNtq3SHrkZ1BkkmCCViGp/JmJKkypHHksJMk/E8s533tqzgZHBUc5w8i51PdazFbjKzd6yPbcMnGYoGuaasiaVFOlGhgkChzclXdz7N872H6A2Pc9g/xA8OvsS6vsP62s9pXIhM9kgyzvO9B/n67ufY7xugLzzOi/1H+P+2/TkjingqzTY3t5U2ZbxHu8nMxxqXkqee+TVerIJWQ2E5wnXvyuw6yhaEWz+cnhQ+X1JJCM/gwRgJ5HQZ/EISsNqJvukjMLXzKkpEr7iNSFGV/g8NHEf5+ZdQ97wII71wfC/qY/+OuvMF1Gk3mK8FkyixtqKFZUWZN1T5Fjuf7ND3VQ3KFn4z3MV3Dm/mqH+I3vA4T/Yd5st7nmO4uPqCfMcMDF6vqKo6o7dwKBHXnRk5X14v39OLKku4+eabuflmbYwspP+wDz/8MF/4whe46667APjJT35CSUkJjz76KB/+8IcZHx/nhz/8IT/72c+47rrrAPj5z39OVVUVzzzzDDfeeOOsvZeZsEgm7CYzXYFhvrDgJpKqgqKmY1J3jfbQFRzBLGb+KeySjN0kU+nIY2VJPWbRhIrK9pFutg2f0HROREFkQUEV20f0u9ZNnhKNC0EgHuUHB19iRXE9t1bPZTwewWEyE0kl+MnBl/no3Gs02tP2vLKs8bv17iJM04orWZB44sQeVpU2cn1lG/54BJvJTDyV5Led27m3YanmOKIgUuPM5+8X3IgvHiaaTFBgdeIxW3UHR3zREL/r2qErfdgy1MW15c0U5tDrdTgaJJpMYBJFnCYLzixdDX8iqjtMl1QVfnZ4s8ZLV00msG/+E2tdBSxZcBMj8Ug6SlcQ8L7wa6RF14G9VXM8SN8M+eMREoqCRTLhMdsu6mSzYLFD2wqEmjmogREQRARXXlqHnIuhFLMNoa4DdfC4/vPXzM0sFi9hOiN+ngoMcdcdHyMvEYNUkojdzXO+XuTBY9xpd2W4YqghP8pTP9Yt3tWNjyM0Lz2zU8dZ4DHbuK9xKbdWd+CLh7FJZjxma1at60g8wmad8084meAPx3fxrsZl2C6TvwmkI2j9iSjJU98pr9n2urnwG1z6CILA/IIqtmax6mr0FM3oLPRG55LV3HZ2dtLf388NN9wwuc1isbB69Wo2btzIhz/8YbZu3Uoikch4THl5OXPnzmXjxo1Zi9tYLEYsdrrz6ff7L9wbAaLJOHM8pbR5S/nZkS2THquVDi9vq1/MnLwyQskY9imT7XkWBx9pXcWxwAi/7dxOIBFDFiWWF9fyiblrcMnaaf5mTzF5ZrumgysKAnfoRPyGknHm5JWjqCrf3reeSDKOqsLiomrurF/I8eCIRprgki0UWZ0a+zIBgRsqWlGUaXeSAryjcSn9ET/f3fcioWQMVYX5hZXc07BY43wwwWgszO86t7NjuBtBFPCYrZOewNPfR1RJsm+sT/c4ADtHu6nLgTVUIB6lMzDCY53bGIwEEBDoyC/n7roFul66Xf6RrPfV3SEfoWQ8c7kvEiTiLeV4bTu/PvgSw9EQiqrQ4inlnmvfTsmel1ArWzQXWF8szF9P7mPDwFESSgqXbOG26g4WF1VfVN/PtJ9jIYIn97ZcgiTBnCvT+trp8gPZijD/mtwU0ReYtJNKJ0eCI3w1OIIkiIiCMLly45atrC1vocg25SIWDcFols+7qqIOnUDIK87J67ObzNhNZt2Y7OnM5JG6fbSXtybjl01xOxYL88fju9g82EVSVfCabdxVu4C5+eU4dM69BgYXgiZ3EXkWu8YyU0Tgztr5hkvIDFwia5ha+vvTw1clJZkTuSUlJZP7+vv7MZvN5OXlZX2MHv/2b/+Gx+OZ/FdVlWXpL2cIiJLEQ7ufzQgP6A75eHjP8yRURaPHFQU4Hhrl98d3TroNJJQUG/qP8kz3Ad28ZwGBdzdfwYKCyklvzBpnPh9qvUq3iDQJInaTzGOd2xiPR4grKRJqik2DnTx5ch+FFq3Wb7+vn/salrCksHrSQ7XS4eUDrSvZMtSF3sr5aCzM/xx9dTJKN6GmeHXoOL85tk33t+WLhfnPPc/x6vAJkigklBTD0RDf3reezsCI7vtmBi9QScjNIEtP2Me39q1j8FT6lIrKrtEe/nPP85PbMl7XGbo8mr2CyEDDPL6xdx19YT8JJUVKVdnn6+OhvS8w1KFNZgsmYvzs8Gae7zs0WRAFEjEePfoqmwa7SJ0pDvZyxlOIeO/noWYOk7/NqjbE+z6fHsS6HBDI8JFMqUqGJEn3MzR1kyCmE9GmrrBcpO7iTH68l1PDczwe4Xv7XuSlgWOT/t++eIQfHXqZPaO9qK/jiF+DS4t8q4PPdFzHksLqye9XtTOPz86/nlKboWGfiUu+tTH95K6q6hmLhjM95h/+4R/41Kc+Nfl/v99/QQtcEwLbhk/qWuukVIU/n9jLfQ2LM7aPx6M8cULfe273WC/+eCQjYxxgv6+PXx7dyvLiWj7Qli6EBsJ+fnVsK2U2Dx9sy4zRU1F5qvuA7nMcCwzr1ovz8iv52u5nWFZUw/tbVoIAw5Egj3ftoMVTgkPnTnK6pdcEJ0M+3bSx3vB41kzrx45t45Md1+KeMlQmixJz88s0qUUTzJ1mNfZaGIuFeTyLB+1oLExXQNvlrnUVIIBu91bPg9ZvkvlD56u6Oip/IsoB/wglzvyM7ePxCHuydK3/dHw3CwuqKLgYMbSzgCCIUFCOeOtHYEKbZrFrLNYuZUyixMqSerZlWXpcWlSDd7rEwOqAkjpCbcsIlNYzlIjgMlnIC/nxbPgdQjad7gVmYWEVf+49qLtvcX7FZZNyNBoL0RnU3kQD/K5rB83ekst+2Mbg8qHQ5uT+5uXcVbcARVWxmeTXfRJfLrhki9vS0lIg3Z0tKztdnAwODk52c0tLS4nH44yNjWV0bwcHB1m5cmXWY1ssFiyW2VtaCqfiHMrifADQGRgmmkpmpPGFU/EZPWgHIoEMiytFVdg12kNMSbK+/wjr+49kPD6SjBNLJTMuMElVIZDIPnzSGxqnzpXZASu0OlhcWMVLA8d4acokp1u2cnPVHI2lmYI6owft8cAoLdP8Mg9msZCCdOE7PbBARWVNeQvH/MOaG4iVJfX4cjBRmlBSHNfpGk9wwNfPsuLajG1u2cKdtQv4XdeOjO1mUeKdTcs0J6iYmtLtTE8+x3g/V5U2IE3RNQ9EsktqIqnEjJ+h1wuC1Q7Wy7fYKLd7mOMtY68v8yalwOJgdVkT8jRdnWB347/jo/z46KscPLplcrvXbOPjd36cch2JzGyQZ7ZxVVENG4YyddBO2cLt1R1YLxNJwvGAdsh1Al88ovEkNzC40Fgl2dDXniOXbHFbV1dHaWkpTz/9NAsXLgQgHo+zbt06vvKVrwCwePFiZFnm6aef5p577gGgr6+PPXv28NWvfvWivfbpmCUZj5z9xO6SrRnesJC2fsrW9QOwTxv0EgVxxg6dW9YOGMmChIiQYf4/lemWW5C2q7q7bhHLi+vo9A+TQiXfbKctr5QCHV9VkzCzl26+TlEykx2XVZI1EgtBEPl953be17KSA75+Do8P4ZDNLC2qwRePzBgicbYIpC/SgUSMMrubGmcB8VNa32gqqdvJsZrMrCptoNFTxFPd+xmLhWnxlLCqrFH3byUKAi7ZSiwVpNjqotGVT1JV2OPrJ5xM4DXbMgpbSH92ZnrNsuEteslTYHXyjsalHAsMs77/CAklxaLCKhYUVOna8cVSSf7Qc4CD/swYY188wsMHN/L5hTeSx+zrQl02F3dUz2NxYTXP9B0mlEywwFvC0pI6Ci9Swf1a8M6QcCcJomZo9nxRVQVSqfO2xzMwMDjNRS1ug8EgR46c7jB2dnayY8cO8vPzqa6u5sEHH+TLX/4yTU1NNDU18eUvfxm73c7b3/52ADweD+9///v59Kc/TUFBAfn5+XzmM5+ho6Nj0j3hUsBqklld3sSmoU7d/ddVtFI0zctSEkRavaW6wQ8u2aK7LHFlSQPP9OgvC95Y1aYpVp1mC4uLqnllWqcF0kWknpdl+rUJ2CSZoWiQUDJOSZE7q4evy2xhRXGdppMM6cKr1qUNM2j3liIKgsZWDGBNeTPuae/dLVtozyvnm3tfoNVbSrO3mFgqye86dxBMxvg/i7N5rp49+RY7N1a0k291MBYPc9A3gE2SeVfTFZwMjbF4mtXZBHbZQoNcxPta8kgqKaySCSlLwVlodXJjZRulKYVi3yCug9tQZQvjrcvZg0Kdjs9tgcWB61TRPZ32vDKNdMXg0qTA5qTA5qTVW0pKVXDL1qy2bv54lM1DXfr7ElGGoqGLk0wHuO0u2u0u6l2FJJUUNrMN6TJLOKpy5mUNsFhWVKM5/7xW1HgU/COou9ej+gYRqtsRGheAu9BwZTAwOE8uanH76quvsmbN6bSpCR3su9/9bh555BE+97nPEYlEeOCBBxgbG2P58uU89dRTuFynuxlf//rXMZlM3HPPPUQiEdauXcsjjzxyyZ1Q3bKVu+sW8rvOHRmaypUl9bohA5FkgusrWhmLhemfsvRsN5l5V9MVjESCVExL3sq3OHhP0xX89PDmjG7ssqIajZctpAvYu2oX0B8e5+QU31WrZOITc9eQp7OMGErE+Gv3Pp7q3j+5bddoD0VWJ5/S8b80iyZuqZ5Ld8iX1vGeQhYlPjbnGrw63WGv2c5H21fznX3rMzS5bd5Srilr0hSHJlFiTXkzXcFR9vv6J28ITILIR9tXz9iJOVskUWJ+YSX/uef5jACNzUNdXFfRekY9oUUyYTmL6f0V9jyExx9GODUNLwDeA5tZ0XE1FNdpHp9nsfOxOWt4eM+zhJOnJQhldjdvb1x62egcDdKcjVl+Qknq3vhNMBYNwUVulF4uEgQ9vGYbH5t7Df+154WMArfGmc8dtfM10qvXgppMoB7bhfrn7zOxPqd27kLd9AfEt/3D6bQ3AwOD14SgGqOf+P1+PB4P4+PjuN1ntrx5LewZ7SGpKBTbXBweHyKhpGj2FpNUUhwaH+SGiraMTk1/2M9/7n6OW2s6MIsSAxE/XrMdu2zmieN7uL95OXU6Xc9YKoE/HuVYYJhoMkmjpwiP2TZjB288HmEkGuJ4YIR8q4NKh5c8i10jlQA4GRzl/27XT4ZaU97M3bULdbOo/fEIQ9EgxwOjeMxWapwFeC02XdcHgEQqhS8eoSswQiAZpcFVSJ7FgXuGi38wHiWYjBFNJRFIJ7A5TdacZGMnlRSPHdvGC32Hdfd/ceHNVDnPL3tdTaVQN/0v6uY/6e4X7/sCQlm9ZruiqvhiYXpCPoajQaqceRRaXXgv4wLjQuOPR0kqKSRR1JXfTCehpE4Fp6jYTeYM39nZZiQa4v9se4JoFu3n5xfcSI3OucHg7EkpCr54mJNBH754mBpnPgVWR8Yg6/mgjg+hPPJF/aCR0nrEOz+hm0xnYPBG52zrtUtWc/t6IpZM8GL/UXacMji/oqgOi0niD7t2Ek+lKLa5WFJUQ+EUzapAevr4Z4c3Y5Nk8ix2gokY/kSUOlcBUhYZgEWSKbLJFJ1DaIHHbMNjtlF/FvZJW7KY5gNs7D/GjZXt5EmZndJQIkZ/OMATJ3cTiMcQBIGFBZWsKKnPqhMOJWNsHuxk+3A3Cgp7Rnp5c918bJKsW6wmUilGYmF+07mNw+ND2E1mri1v5qrSBrzS+XduA4koG2eIQnxl6Ph5F7dE/Ki712fdre5ep1vcioJAvtWR8+jg1yOhRIzD44P8/vhO+sN+Cq1ObqvpoN1blrVrOhwN8uTJfWwa7CSlqiwsqOT22nkUWV0XJSjDY7ZyY2U7fzi+S7Ov2pF30SQJryckUaTA6tSdI8gF6nBv9gS9/mMQCYJR3BoYvGaM4nYWSKlqxkDVdO1tIpXSJMRGUglK7W6uLW/hxf4j9IbHERCYn1/BqrJGhmNBql2ZtlCzwUyTwgk1pTuadjw4yjf2PJ+xrzs0xlH/EPc3LdcUZePxCP994CWOTBmY6Q2Ps397P5+bf71uIEN3aIyv7nx6Uo4RSsb444nd7Pf18+G2q86746KqaCKSp5ITVwIVmOk4iRiqqqQtsAzOmaSSYsvQcX559NXJbUPRID86+DK3VM3hpso5WEyZp8TRaIiv7XomI3b61eET7B3r4wsLbzqnm8hcIQkic/PKCSfjrOs7TFxJIQBz8sq5pXpO1mAUg0sIHVvIDHQsEg0MDM4eo7idBeyymSWF1ezN4kc6v6AC5zTPU7tJ5qkT+3h781JWlTYQSsaxSjIpVeEnB1/mg22rcvb6ovEogUSUUCKGWTLhksy47PoX7SVF1azr11+an5dfgX3acu1INMivj21jWXEtiwqrSSkKJlFkIBLg6e79+OIRTXE7FAlmFLYTKKj8+tg2PjpndYbMIpiI8atjW1lYWMWyohpSqookioxEQzzds5+haPC8i1u7SaYjv5ydoz26+5cUaYe9JlAjAQgHIBEDqxMcbgQ9mYjVjtCwEHXfRv0Dta+8rApbRVXwxSIEkjEE0s4OFzMWeDwe4fFptmwT/LV7HytL6ikyZX7u94z1ZhS2E0RSCZ7rPcRdtQtyIns5F8bjEb69bx23VLTy+Y7riCtJZFFiPBbiv/dv4BMd1xopWpc4QlFVVicc3IXwOvfRVVSF8SlONi7Zgsdsv6iR4QavL4zidhaIp5JUOwsos3voC49n7HPKFlaXNRFLJTOi9ArMDj4+bw1PnNjLlsGuyY5kg7uID7WvwpEjzd94JMBfTu5j3eCxySGVGmc+H2i+guJpA2sAJTY3TZ4iDo9nFp8W0cTtNfOwTrOziaWSrC5rYiDi57/3b5gcEKt0eHlP8xWcCI5q5BB7xvTDGCAdLhFNJTKK22gqwcKCKoLJGD84uHGyS15md/POxmV0+UdocBed/S9FB6vJzJ21C9jv69f47DZ7SijJ0sFTfYMoT3wPBrrSG0QJYf4aWHYLgiNz6keQLbD8TahHt2sjZUtqEYr0HRkuRaLJBPt9/fzs8OZJ72G3bOV9LStodBfPekEIEDzl9ayHoqr44pGMTmw0leDVoRNZj7dzpJubqtrxSLOrbQ4n47yzoo3G/ZuQ926EU5/HkrJ6HrzmXnpD41mdTgwuERxuhEXXo257etoOAeG6+xGc3ovxqmaFWCrBId8gjxzedErHnr7xfXfzFbR4inMysGdgcPm0gS5jREFkQ99hPth6JbdWdXBDRSs3VbZzfXkrn+pYy7M9BzR3rFElyVPdB9g02JnhfHDUP8R/73+JWA5iVRPJBC/0HuL5gaMZ09fHg6N8Y996fGFtQEBCTbGmvIU3VXdQZHXiki0sLarhI+2r8Me1gRBm0UQgEeX53kMZzgfdIR8/PbxZ1wpspgl/WZQ0MZ8yIoIAT3Xvz5B/9IX9/PjQy9R7chPFWmx38cWFN7OiuA6XbKHI6uRtDYt5f8sK3aEkNehD+e1DpwtbACWFuv0Z1J0voOoVWt4ixLf/I0LH1WB3g6cQ4eq3It7xt5fVBW8gEuB7+1/MCNXwJ6J8Y+8LDMeCM/zkhcN0hq63edpwo3QqnjobNpN8UTpNdlGk4cgO5N0vTha2APQdo+ivP6LRcMi45BEsdoRltyK86W+gqCqtr63tQHz7FxEqmi72y7ugDEWCfGvf+snCFtIzDd/au46h6MU5Nxi8/jBukWYBkyiypLiWUCLGgsIKdo50k1AUFhRWoqgK9a4CzbK5PxHl5SwDTD1hH/54VFfvF0nG8cUj7B7tIZJM0JFfQaHVqesy4I+FeHbgqO5zDEWDDEeDeO2Z04gHxvo5ND7I1WVN1LsKAAFZFOkP+1nff4SPO67JeC8qKi/06ssY0gEL2oK4dVpi2VQWF1ZrNIUpQeW5nkO6jw8mYgxHQpqktdeCJIiUnLLYCifjCIKAW7Zm9aRUx4dgXCuvAFC3PYUw9ypwZxb3giBCXgmsuQ9hxe2ACA7XZSVHiKUS/OXkHt1lV0VVeb73EPfUL8rqlHGhcMoWim0uBnWind2yVfMdlEWJa8tb2H5qEHQ6aytas4ZojEZDnAiO0RkYptTupslTTJ7FjpSDv6MrFoNd6/R3jvVjjwThPGcbDS48gt2F0LwEtbIlPVxmtiBchnIERVUZjYU45h+mJ+Sj2plPratAd1g4nkryZPd+3YhxFZWnu/fz9qalmEWjNDE4P4xP0CzhtVhZ33uEJ3tO+8M+2b2PefkV3FO/WPP4aCqZ0emcznAsRAOZS+3hRJyXBo7ym87tk9v+fHIv7d5S3tNyBZ5pfq/xVGrGAbGB8DiN+eUZ2xRVpcLh5d93ZS6n1bsKubV6rmboSkEllMyeENYbGqdjmgdvb8jHnbXzebxrZ8b2EpubpUU1JKb9XhRVZSyu1UVO0B0aYynZNbHnilkynd3S2ag2gGOSeDStwc2CYDKD8/LswMVSSXqmyW+mcjI4RlxJzXpx6zHb+FDrVXxt1zNEpgzuyaLER9pX6Xbfy+weri5t1ISQzPGWMSevTPN4SEcif23Xs4xPiX02ixKf7FhLrStf12LvXBCTcZQZBpJE3xCUN57XcxjMHkKW+YbLhe7QGF/b9WzGUK1TtvDpjrUZEfFw6twwxVNdeywf8VTKKG4NzhvjEzRLjEbDGYXtBLtGe5ibV85qW+ZSlFUyZU3pAijQucMfiYX4Ted2lhTWsKqoGkkQORQY5k8n97N16CRrypszuoyyNHM0bpFN6yFX5czj/+18inK7h0WFVciiRFdghJ0jPRz1D9M4TdsqCxJ2k5wRMDCVch1toCikB87+ds5q9o/1E0rGaXQXYZYkHju2jU92XJvxeEkQcctW/DpdYEjrey8K3hl0viZz+t9lRiQZJ66ksEimrFnnZtFESZYOKaT/5tMlALNFhcPLFxfdzIGxfo4FRqhyepmbV0G+RX+YxWW2ckfNPK4qbWTLUCcJRWFpUQ0lNpfukGIwEeORgy9nFLYAcSXFN/e+wBcX3nz+lm2yGSRTVispIUcyHAODM+GLhfn23vUat5hgIsb3D2zgUx3XZawaWiQTpXY3PWGf7vHK7G4sRmS4QQ64fNY6L2OiyYRu/OwEL/QdYnhaIeA0WVheVKv7+BKbS7fLtGWwi3+efwOtznweO7Gbnx7bRiyZ4J8X3kinf1ijifWY7azWSb2CdPLV9EhggD2jfby7aTlXljSwc6SHDf1HccoWPjb3Go76hzTdZo/ZxnUVbbrP4ZItmpQ1gAZ3IduGT/Ltfes5GRojlIzxxMk9/Ojgy6wpb9a8d4/Zxi3Vc3SfwybJZ+XfeyEQvMWQxftWmL8GHJfP0E84GefQ+ADf3b+Br+58mh8ffJkTwVHdzr/VJHNL1Vzd4wgIXFvRMutd2wlEQaDQ6uSqskbe1bycNeUtFNmcSFmibgGcZis1rnzeWr+YtzcupclTnNV9I5iIcSwworsvlIzrOi+cMw5PWpOth7sQjOLWYJbwJ6JZV836wn6N7Mwsmbixsl338QJwQ2UbsjFQZpADjE/RLJBUUoQS2ZcRw8k4qWkdWpfZyg2VbYSScXZNsZ+qsHt5V/Ny7FJm109RFVYWVvHosW0cmhJz+2RfgI3Dx/nM3GtRyCw8ZdnM9ZWtBBMxNo+cnFRBldjcPNB6JXk6XdVqVx4v9B5mn++0rdlQf5Ctwyf4YOtVTA+8k0SRVaUNjMXCbOg/Oqm1KrQ6+Wj71bpdrDyLg0/PW8u3963n0PggACIC11e0sbiwWqNxFQWBJYU1DEdDPNdzcHIAL89i54H2qy+aqb3gykd8y6dR/vBNGJuQKAgI7SsQltyAMMOw0qVEPJVky2AX/zPFH3Y4GmTnSDcPtF/N3PwKTdezzO7mPc1X8D9HXp2MMLVJMu9pvoKiC2SMfymQbRVkgkjqDP6mZ4FgMsOyW1EjITi4hYn4VgrKEW//W4TzDRMxMDhL4qmZP+9Jne9Dic3JB1pW8vMjWyZT9qySzP1Nyyi2Xt4SDYNLB6O4nQWcZivteaUZBeFUmj0lOEyZvpRDkSDf2fciN1S2cXtNBwlFQRYlxmJhvr9vAx/vuAaH+fTPiILIaCycUdhOEEjEeLbnAHfWztfs89rc3FO/kFuq5xKIR7CazDglGW+WrqJZlHTfRziZ4MW+I9zXuFSzz222cXfdAm6obCMQj6a9dGUL3izDE6IgUO3M5+/n30AgESWeSuE223DJFo3V2AQus5U3Vc3lqtIG/PEoZknCZbJSeIaUn2gyQSARJakqWCU55z6sQn4Zibf9PYFUgoSSOvUcFsQcxXjOBv5ElMeObdNsV4GfHd7C5xfeRN60v6XNZGZpUQ3NnhLG4xEEATxyOglvpi7p5Y7DZMYmyRma3qkU5ejiLTi9iGvfCStvRw0HEMw2sLs09nLTUaOhdPqVqoDZdkEcOCLJOIFEjJSqYJPkrN9zg8sfj9mKiJDh6DOBLEq6fstWk5mFhVXUu4smzw1u2YbXbEUyJAkGOcIobmeJefkVPN1zQKPFk0WJG6vacJozTwKRZJzVZU2MxcP8bvd2wskEJkFkcVE19zQsojfkp3RKZzWlKGwczu7J+epoDzdVtGGfdqEJJmJsHe3lD8d3E0smUASVpUU13Fk7X7fjuSPL5DjAztEe7lEX6e6zmczYTGaKzyHRyWuxn/WFcTwWYb+vn8e7djAejwIqc/PKeWv9Ikrs+vnTI9EQvzm2je0j3aiouGUrd9UtYF5+Rc5M8EdjIR4/sZtXh4+jqCpO2cLt1R0sLqrJ8Oq9lBmNhrION/oTUYKJmKa4BTCJEgVWR9aI5dcjHrONO2vn8+iULvcEK4rrdF1LXiuC1Z4O/sgrPavHq2MDKM/+HE7sS29wFyKsfSdCRRNCjl7XUCTIL4++yt6xXlQgz2znbQ2LafWWZPh4G7w+cMlWri1v4ZneA5p9t1TNwSPr38S/Ec8NBrPL67eFcolRYnfzyY5rWVxYPdkZbPGU8Nl511Nk1nYXLVLaH/aJE3smh7GSqsLmwS6e7T1IgXVaMaGqmGboOEqCwPTdiqqwdeg4jx59lVAyRhIFRVXZPNjF9/ZtwD+tEE8fJ/tHJv2+Lk7CzNHAED8+9DK+eAT1lPhh91gv39j7AkM6g02+WIRv7n2BbSMnJ6US/kSURw5typokd6744xG+t28DW4a6JgcDg4kYjx59lW3DJ1Auk4jNbFZnk/tn6XVcDkiiyJKiGj7QciWFpy7cTtnCXbULuKtuwYwezhcS1T+C8uuvnC5sAfzDqI8/DEPZb1jPhbFYmId2P8OeU4UtwFg8zHf3v0hnFh2yweWN1SRzU1U79zYsxn3KFi/PbOfdTVewqrTxooS1GBiA0bmdVQosDt5St5Dba+ahoiILIg7ZgiXLUvvzffrerWkdamZJIUkSVxXXsWnouO7PrCyswWXOvEv2xSP84fhu3cd3BkcYi4U1gzMLCqp4tveg7s8sKaw5o1H+2ZJSFHzxCAMRP6FEjAqHF7fZptvtHIkG+X3XLt3jDEeD9ITGNZ7AQ9EAvVnsqn7XtYNmT/F5L6eOxsJ0BfUv6n84vou5+eXkXyQ98LmQb3FgEU2T2tmpFFgcl00HerZwyBaWFtfQ7C0moaSQBDG9fHsRvYrVnsMQ0v+8K+sfQ3zzxxDOIOE5EyeCo1kH5tIuJ2tz2rk2uDRwma2sLmtmYUEVSVXBJIgXRIoyHo8wEg0xHA2mu74WhyF5MciKUdzOEtFknB0j3fz08BZSpzp26enQdq6vaMU17aSfUGf2oB2OBqlx5WdsK7I4WJpfySujmZ2YQquT1eVNyLI2GncmD9rukI+aaQligUSE5cW1bB7sytieZ7aztLjmjAM1Z0NSSXHMP8w3963L+B0sKqji3oYleCyZBXdCSTEQ0aapTXDYP8iCwsqMbZ062uQJxmJh3ULuXOkOjWXdF0zEZvz7Xkp4zOlozP8+sCFDWWcSRN7bssK4wGRBz9HkotG1J/u+gU6YwTf3bDnoG8i6rzc8npNzg8GliSgIF/Q8MBQJ8l97X8g4zxdanXxi7ppzkroZvHEwittZYjga4seHNuEx25iTV4YkCBwaH+LJ7n00uAuZX5BZfJmEdMisvstt2kZrOhEB1pY3c0VxDS8MHCOmpFiaV06Tt5TucICCae4HsihNeuleW9ZMtSufYCLGX0/uJZiMk2fWnqwGo0Eq7F4+2HIlY/EwKVXFbkoPYv2haycPtGexKDoHfPEI/7nneY3Oc9vISSqdedxU1Z4hjxAFccYhnkIdXddEx9QqmejIr8AmyfSEfRz1DyOLEibh/JfTJoobsyjRkV+BQzbTH/ZzaHwQSRBzklY1G5hEibn55fzjolt4rucg/RE/da5CVpU2nJVmLp5KIgggG8bsFw9vcfZ9Di/kYMhPzzpw8ilMlosSVWwwe6hKCpIJMJkRcjg0GkxE+cGBlzQNjOFokO/sW2+sCBjoYlxtZoGUorC+7wj3NizGIslsHz5JUlFYXdaES7bwXM9BGtxFGcu7oiAyJ6+cPWO9muO5ZavuwNPLA5082b2Pakcea0ubkCWR7aN9/KJrB6U2N3XuwoyTgMtk5a7aBbTnlbFt+ASbBjvxmu08MGc1Q5EApTqDWK2eEvb7+lGBY/5hIqkE7XmluFQry4pqEHOgwDwwNpB1gOmZngOsLKnPGGDKM9u5uqyJJ7v3aR5vEkTavNqBm1pXATdUtFJ3ylN3MBqgxVPKzVVz6A6O4snByTLP4mBteTPN3lK2DZ1gMBKgzlXIjZXtHPT150zCMRtYJBMVDi/3NS4loSQxi6Yzuh74YmGOBUbY0H8EURBYXdpEtTNf03k3uPAILctQN/0v6ITCCMtuBh3bv3NlTl45orBNN3jm+orWnHynDC491EQ8rd/etQ51uAehtBbmXpUeWMyBZ60/Hssq7+oNjxNIRI3i1kCDUdzOAilVodlTzKahTnaPni5W9/n6qLB7ubN2vsYPMJKMc015M754mO4pcYUu2cK7mq9gOBrMCEBQVIXBU3e2J0Jj/Pjolozj+eLhSTnEBBaTiRZvCf++8+mMruemwU7eVr9YN0UqkkowFovwh+OnNa77ff0UWBz8TfvVM0YGny1DUf1kK5jwBJ7m1ytJrC5rpDs4xt4pNmVmUeLDbat0J/ntkoxDtvC9/Rsmtx3wDeCULXym47qchAxEEjEKbS6+s299xnOs7zvMgx3XXpbLtCZRxCSeeSjKFwvznX0vZlyUdo/2Msdbxrtbrri0luzfCLjyEN70N6h//n5mslnbFQiNi844NHg25Jnt/G37ar6z/8WMz/aCgkpWltZfVM2xwYVBVVKo3QdRf/+NtL0coJ7cj7rtaYS7PwUVzef92YqfQSJ2uci7DGYXo7idBWRRQhRFDvgGuKK4jrn55QikO58vD3bSFRyhyZO5bGiRZH6+dz231MzFabIwGA3gNduQBJHfHNvGe1tWZDxeFETavGVsz2LVVeMswDJtWXgsFuZ/jryqu5z/2LFttOeV4px2RyyLEuv7D2sePxIL8VzvQV0v3XOl0ZmfdV+xzYXe+F2B1cn9zcvwx6N0BkZwyVYqHV68ZhsWnZhYfyLG4107NduDiRiPdW7jg61Xnrd1kUO28NhRrT9sJJXgV0e38v6Wled1/Kkoqoo/HiGlqsiidNE7GbtHe3S7LXt9fXT6RzQa6PMhlkpMhqTYZXPWWOBLnXgqSTAZAzU9hZ5LZwVBtkBdB8J7/hV16ATEIgildWD3INhyM9QoSxIt3hK+tPhWekI+QskYNc4CPFkGQQ1eBwR9qH/578nCdpJUEvXP/43w9i9kTWk8W+wmc1YvXYG0t7SBwXSM4nYWSKkKe0Z7+Zv2q3l54BiPHHyZlKrQnlfGB1pWsr7vCDEliXVK2WYWJa4oqePRI69gFiU8ZhuhZJxwMk6juwirjn5xbn45juNmQtOGQwTgzbXzscuZJ4FIMs6xLINVCiqdgZEML12A7cMns77PV4eOc0uVfgzuuVApiORb7LqT13eXNOJO6t+p51kc5FkcmiE4PQ6NZx9+2TfWRzARP+/itjvk0z0hAxwLDGs60K8VfzzClsEu/tq9n0AiSqnNzd11C2n0FF0U66lAPMoLfdoboAme7ztIW16J7k3HuTIYCfCHrp1sGzkJarpL+Oa6+RRbXTnpRs4WI9Egfz65l00DnaRUhba8Mt5St5BSmztnoReCyQyeQoQLGM9rEiUKrU4KX8cpdAZTCPshGtLfFxxLB4acZ3HrNltZWVrPhv6jmn1Li2ou+o28waWJsU40C6iqyrLiGn52aDOvDB0nqaZLnr1jfXxv/wbWVDRrpHCRVIKO/ApurZqLKAgMRYNEUwmWFtbwjsZlDOucUCQBPth6JXVTirt8i513NV+BQ8duTE8bNxW9ZfP4DEvpE+/rfPF07eFTVR20T+lmu2Ur76vuoLFrb+ay6mtkJkmACpPet+fDmYrXXPyuwok4v+vcwWOd2ydz3Psjfr61bx27RnouipeuyszvPakoZ/zsnQ3D0SBf2fkUrw6fQFFVFFS2jZzk/+14ipFsF9xLkNFYiK/tepYN/Ucnv0P7xvr4tx1PMjiDRMfA4KJzJmlVDqRXVknmjpp5XFfRinxKLmYSRK4pa+buukVGOIiBLkbndhaQJRO9oXHG4tpOZExJsmmgk7fWZyZ7OUxmvr9/Pe9tXsmSompCyTgWyYRJEHnk4Mt8sO1KzbGO+Id5ofcwb6lbiCxKpFQVkyDy8uAxdo/08O6WKzKWbG0mmVKbm/4sNlqN7iLNtsWF1bzYf0T38fPzK7Fn8exV49H0XX4kCCbzjFGhQmUL+b99iA8suJZg3SKSqootGsb98p/Sy6vTAyxeA606Q2YT1LkKsnY846kk/niUQCKKSZRmjBGuc2XvkJXZPTnpqvoTEV4e7NTd95vObbR4i3WT5i4kTtnMsqLaDF32VK4sqc96QQomYgQTUcLJBHaTGZds0R2eVFSFzQOdBBNaK7twMs6GgaPcVt1xWUT9Hh4fZCSmLcYTSoonTuzh/sblWEzGqXoq4/EIgXiUhJLCKVtwyzbjd3QxcHjT53M9KzmrA2z66ZDnitts482181lT3kwslcQimXCbrZgNBxaDLBifjFkgnIhlDJJNZ7+vn3AynnER91rsfHTONfy+axfbhk9MdvlqnPm8t3UFLjlzKUZRFU4GfaypaOb7B16ajPmVRYmbqtrTVlnJeEZxW2B1cl/DEv5z7/OaTto1ZU26RUWxzUmrp4QD05b1rZKJ22o6cMraJSI15Efd8gTqjudOa7PyShFv/yhCQbn2F+IugOo2rJv/hHXzn05vN5kR3/4FhBz4KXrNNlYU12kKQ5Mg8raGxboawWAixvq+wzxxYs/k4Fyexc5H2lZR7czTDMy4zVbWlrdoQi9EQeCdjUtzspzWF87u7xtIxAglE+TNstxRFESuKK5jff8RxqZJS0ptbtryynR/bjQa4ieHNmV8ttrzynhX03LNUGAkmWDnaE/W17BzpJu15S0a/+hLjaSi8OpQ9tjsfWN9hFNxo3CbQk/Ix3f3v8jgqeRBURBYW97KDZVtxhL1bOPwIKy+B/XZn2t2Cde+E5zn78IxgXxK8mJgcDYYZ8xZwCRKWTuacEowP00fGIxH+cvJfWwdzrzwHQ+O8r39L/LR9tVYpxxTFETmFZTz9d3PZSy5J5QUfzy+m/c0X4Gko0KpdRbwD/Nv5MX+w0RTSaRTFmQN7kLdifYCq5N3NS9n12gv6/sOp63AvGVcX9lKkVVrpq0qKdS9G1C3P5O5Y6wf5bF/R3zHPyJMC6MQ7G7Ea9+B2rAA9dUnIRpCqJ2LsOQm8Gq7ya+FWCpJo7uIGlc+L/UfI5iIUe8u5MqSegbCAaqd+Rof2v1jfZpuZDpy9Fn+adEtmhOv3WTm5uo5NHtK+MvJvfjiYRrchdxa3UGxzu/qtWCb4XMFIF+kCfV8q4PPzruel/qPsnmoExGRK0vrWVZcq+teEUrEeOTQJg5Ou2naN9bHzw5v5gMtV2ZoxiVBnPE75TCZL4uurSgImhvVqeidG97IjEZDPLT72YyOvaKqPN2znzyLjTXlLcbvaxYRTDK0LIP88rTV3NgAFJQjrrgDCsoRcuA6Y2DwWjCK21nALJm4uqyJV4f1OzSry5oomFYYBZIxTQrYBH1hP+PxiCZS9oBvIKuW9Lneg8zN03ZJrbKMS7HQllfGQd8gRVYHNa78GS+4siDRkVdOvasAFTALErIgYdIrJoI+1Ff/qn+gsD/ti+jSuiMIDg9C+0rU2o60bstqTw/E5IiesI+fHdlCic3N0qIabCaZnpCP7+x/EVmUaPYWZ0TjjscjWZfZY6kk+8f6WVXWqNnnkq0sKKyk0VNEUklhk+SscctTn2sgEmDH8ElsJplFhdXkme2agUCAYqsLqyQT1XG8aJzmnTzbFFgd3Fo9l9XlTQiAU7ZmLTz8iaimsJ1g71gfgUQ04/1bTTLXV7SxP0sq1vWVbRdlmO5cEQWB1WWNvDSgHZYBuK6iFfcM38U3GieCY7pSFIA/n9yb/q4YiXmzimB1IFS1oBZ9NC1PkC0Ihpe1wUXGKG5niUKLg2vLW3hu2hL1HG+ZbtEZSyVnHMqZ7iSgqAongqNZHz8QCeh60A5FAnxt97MZy8e/7drBA+1X0+Yt1fi9+mMRNg118tvOHRnba5z5fKjtKu2yUSqZfZoWYKQX6jqy7hbsFyZasTc0DsBAxM+fTuzO2JdQUpqbhJSqMBQNZj1eV3CEVWiL2wnOtsj0xcL84MBGDvsHJ7f96cQe7qiZx+ryJhymzON4zDYeaL+ab0xLdPOYbbyrebmutGQ2kUTxrDxtI0n9dLkJwjrFe7Uzn6tKGtgwrTC8org2Y6jyUqfQ6uJN1R2az2Gbt5SFhZWXlevDhaYn7Mu6L5iIXZbe0a8XBKsdMG4sDC4NjOJ2lvAlIkiCwEfbV3PA10/yVLCDLxbh4Hg/hbbMwsgqyUiCmLXAnR4pKwoita4CdmTxuS21uScnTScIJ+P8z9FXNbpIRVX53v4NfGnxrZpidTwR1RS2kJZLvNB7mDtq5iFLU55HktODBdkKXD3N7SwwNQBjOg6TWfO7MgkixVZX1un1Wuf5F1OKqrJ5sCujsJ3gD8d30ZFfjsOZWaxKokiDu5AvLX4T+8f66I+M0+gpptZZQP5ZRONeKpxJXmHTsQ1zma3cWbeA1eVNbBs+iaqqLCqqJt9in3Hl4VLDIZtZW9HCosIqtg+fJJpKsLCwiiKrE7cRdpFB5QzfW5ds0XxvDQwM3pgYxe0skFQUnu89xCtDx3m+9xB1rgIkUWTLYBeRVIIyu4d5+ZUZwy8isKyoRncSvszuQdaZEl1cWM0TJ/bodi9ur5mn6R6GEjH2jfVpHgvp7mV30Kcpbl/JIpUAeGngCKvLGjPlEk4PwtKbUV/8jfYHHB6Egoqsx7uQlNs9uGUr/lP2WVO5qbId77Siwm22cUftPP77wEuax1skE2152d0XzhZ/PMrzvYey7t/Qf5T7GrUSDpMoUWRzUmRrOu/XcLFwyVbdQUWAuXnlWZfmnbIFp2yheobgj8sBu8mM3WSe8abLAKoceThli6404ZaqOUbynYGBAWD43M4KiqpMJiglVYXD/iEO+AYmk8EiOpGy4VSCOfnlLC6sZuqiZK2zgLfWL2Ioou0gFlgcfLLj2ozCzCxK3NuwRNeW6ky+tKGk9gIyPSBiKpFkUnM8QZQQ2q9EWHQ9TO2q5JchvvWzCO6LU5RYJBMfaruKMvtpqxpRELi6tJF5BRW6UaGt3lLurJ2f0R0qsDj4dMd1Gfrc14qKSiSV/fcbSERz4g97KeKULby75Qrap1m0zckr451Ny3T1xgZvPPKtDj7dsZaSKTfQkiByY0UbS4tqjWEyAwMDwOjczgpmycTiwir2+fS7pHPzyjTDL3aTzK+PbWVJYQ0PtK8moaaQBYmesI8fHtjIp+et1RxHEkXqXYX8/YIbCSZipFQF1ykPyAypwClkQSLPYtfIEiao0kmW6cgv12gcJ2jxFmPWWRYUHG648k6EBWshEjjlc+tOb5+BcCJOMJnW0dlMMl6zfcaLl6Kq+OJhIskEsijhNFmyFkVj8TA/PLCR6yvbKLQ6SKoKsiCxfeQkPz64iY/NXa2xNXPKFtaWt7KkqIZAIoZJEGf0uZ0gmozjP6UHtEpyOkZZZ/jOJsm0eUsZjga5o7CGQkFEFUQOxUI8MdTF4sKai3rxjp3y+I0rSaySCY/ZptFknw/5FgcfaL2SQCJGJJlOiHPL1jdMYTsWCxFKxlFUFZskawZGDdKUO7x8et51BBIxEqlTPrdmKxbJuJwZGBikMc4Gs0R7XpluIWkRTdxQ1Y552ok532Lnjup5/OzIFs0Q2vKiWpxZJsEFQSDPYj/rieFbq+by8yNbNNs78suJp7TyBoskU2730Bsez9g+4TWZLdlLkC1pG6+ztPIaigT42eEtkxP0LtnC3XULmZ9fgV1nSCqciLFztIffdm4ncGrJstVTwjuallGsUyQc9A0wFg/z62NbdZ8/nEzoevbKkkShdPbxoqPREL8+tpUdIz2opIuWW6vnckVxncaD1WqSubdqLmLPYRx//XE6vhIoLq1j/jX3IrovXGzqmfDFwvzv8V28PNiJoqqYRYlry1tYW9GSU12oI0tow+sZRVHoDvv4+eEtHD81FJpvsfO2hiUX3fHiUsVjthkSBAMDg6wYsoRZIt/q4DPzruOqknpMgoiAwLz8Cv5hwY0U6RRKJtFER345H2m7ijJ72gjbLVu5q3YBb66djycHdjcpFDoDw7yvZcXkoIZTtnBTZTtLi2oZ10lUOzQ+wJ21C1hV2kiTq4hWTynteaX8TdvVrO87BJx/Z3EsFuZru5/NsIYKTPqgaoetAA6OD/LIoU0kUinavKVUOfI4MD7A16c5QUww0+CJAAg5eB/j8Qjf3reO7SPdk0V/JJXgN53beWXouO6woNs/iuPP358sbAGE/k7cv/8G7qh+h/1CE0rE+PnhLbw0cGxSFhFXUvy1ex9Pdu8nnoM45DcyQ9EgD+16drKwhbQbynf3rad/hpAOAwMDAwN9jM7tLGIWJW6qmsPVZU2opB0RLKKkq+8E8FjsLLRUU+0sIKUqCKQ1nuIM5vTxVJLxRJQTgRGiqSR1rgI8ZptuN8xhstAX9nNwfICrShu5pXou0WSCTYOdPNWzn/+z+DbNzywsrOKVweOsKW+mOzRGOBmnzlVIOBmnyOrGlYMu08ngWFapxO+6dlDvKsQzxUdxPB7hT8f38Im5a7CIJo6HRnGaLJTZPbzYd4Tu4Jimk93iKUEA3T7znLxynDlYCh+NhjgZ8unu+9OJ3SwoqMxwNFCjIdQNv9U/WDSE2rUHYf415/26zhV/IsruMf2EvRd6D7GmrJlC2+WTHOSLhRmKBhmI+Cmyuii2OWc9ongq20dOTurvp6ICfzyxi/c1r8z4vF/KqKkkBMdQh06mw1dKasHpRTAkFgYGBrOIUdzOEmPREDtGu/n10W1MHeNaVdrITVXtMy5zF5ylpVM0mWTXaDc/PvRyxuDRypJ67qydr1k+dsoW7m9axld3Ps3vu3Zm7Lu/aTlus7ZQdctWalz5fHn7XzN8VRfkV/LW+kU50WAe9Q9l3TcYCZBQM+USCSXF+1pX8Jtj29jn65/cLosSH2y9kpSi0yE127i7biG/6dyesd0lW7infhG2HAQATJduTCWUjBNTpnU8E3EYzB7Fqp7YhzrvaoRZTh3zZbnRgPRQYniGIbhLjaFIgP/c83yGZ7HXbOPBjmsnV0hmk3gqyVH/cNb9J4Jj2s/JJYqajKOePIj6x2/BKd9iFaB+AeJ19yM4vRfz5RkYGLyBMIrbWcIXj/DLo1p954v9R2jyFM9Y3KYUhbiSRBalGYvHsViIHx3cqOlGbhw4RpO7iJWlDZqfKbN7+OKim+n0jxBIRJFFkTpXIQVWJxYdb9FwMs4PD2xkus/CjtFu6twFXFfRpp9Udg5MaGSdsoX5+ZVYJRMngqMc9g+lY1WnFXdmwcS6gcMZhS2ki97v79/AFxberHkOm0nmytIGmj0lPN93CF8szLz8cuYXVGrS4l4rM+meTYKolUaIIji9MNav+zNCXmnOC9toMo4gCLp/6wnOpIG16NjSTSWSTCAKzPgcs0EwEeUHBzdqwjh88Qjf3reez3RcN+sdUpMgUjBD1zjPbMd0kSKUz5mgD/UP/5VOFJzKsR2oe+tg6c1GHKuBgcGsYBS3s0A8mWRd3+Gs+5/tOUCDq1CztJtSFIZjIV7qO8KRwBBFVidrK1opsrp0Te9fGjiW1drrr937mJtfruneqqioqkpv2MfB8QHyzQ6qnPlZB8O2Dp/QFLYTPNd7iMWF1ec95d3sLeHNNfMpsDp4Zeg4/ZE4zZ4ibqyaw0g0iHvaIFZUSfBi/xHdYyVVhQPj/ZQ7tF05u8lMjSuf+x3LSKkKsijlNA2q1ObGYTLr2qddUVyn8W4VHB6EK96E+pcfaA8mCAjtK3L22sZiYfaP9bFxsBOTIHJteTM1zgLd4s4j27IGWLR6S3UH7yae49D4ABv6j6ZjZkubqHcXntFd4kIRSMToCozo7huMBPAnorNe3IqiyFWlDbzQd0j3W3VDZdtlE8ahdu7WFrYT+7Y+hTDnStBxYDEwMDDINUZxOwvElCS+eCTrfl88ojtcdCI0ytd2PTsZynDUP8ymwS7e3bScJUU1GQ4LiqownCU96/RzaC+fPaFx/n3X08RODQUdZZhXho/ztvpFrCxpwDqtiB6dYajJH4/M6Jt7tlhFE4FElN8fPy2VOOofwiUf5TPzrtd0bhVV0dUsTjAyU/wvaQs16QLMVuZZ7DzYcS3f2PP8pIMDQLOnmDfVdGgcMgCEmjmwYC3qjmdPbzTJCDd/CFy58QQejYV4ePfzDERODyvt9/UzN6+cdzUv10yheyw2PjpnNd/Y8zwjsdO/y0qHl3c1Lceho08ei4X51t4XMjTHB3wDNHuKeX/LyotS4Oq5f0xlps/QhSTfYue9zSv46eHNk1IfAVhd1kyLt/iivKbXxHh2ORHREOjIgwwMDAwuBEZxOwvYJZlGdxH7ffrLzXXOfE0n1h+P8MjBTbppY7848grN3hIKpdOdXlEQmZtXzvYs8bv1zkKND2QwEeVnhzdPFrZT+fWx7XTkV2iK22ZvMVuGulhcVM28/AoEQWAg7Gd9/xHcslVTeL4W/Ikoz06zP4N05+1/j+/kXc1XYJ2yxG0SJUptbvoj+pPlTe6LUyAIgkCVI4/PL7iJ4VgIfzxCic2N12zT2IBN/ozdDVe+GWHBtajD3SBbEPLL0mluZ4ioPRsUVWHTQGdGYTvBnrFeeoI+PPna7mWp3c1n51/PaCzEWCxMkdVJnsWuawOmqio7R7p1h+kOjQ9yzD/MoqLq834v54rdlD3SWgA8Wf4mFxq7bGF+fgX/uOgW+sLjxFJJqp15p/xbL49BMgCqWmHb0/r7CisgB59fAwMDg7PBKG5nAUmSWFJUw7O9BwgnM7tDoiBwS3WH5iIWSsSzFmtJVaE/7NfodNvyynDJlowuIaRtrd5cN18TFBFKxDPsh6aiotIVHNVIDOqdhXx0zmo2DXby44Mvk1QVapz5vK1+MR7Zikk4f03dziwFOsD24W7urotnFLeFVidvrp3Pd/e/qHl8kdVJhY4kYbYQBIF8q+OclpYFix0sdoT884/0nU4gEeOlLCEcAOv6DtHsLdbVdp+tf3IoGcsqEwF4oe8w7XllmhunC43HbGNNeTPP9BzQ7FtaVIMri7xiNrDKZkplM6X2mYNNLmWE4mpUdwH4tdIPYfW96Rs3AwMDg1ngMplUuPwpsjj4ZMdaGqYY8ZfZ3Xx8zhrdgZJsmtcJ9GJYJVHg/S1X0uw53akstrl4b8sK3fSeM0kI9FwGVEHll0dfZfNg1+QS6vHgKN/fvwFVAJh56fds0OusTT4/KnqvvN5dyPuaV5BnThdfaR/hcj429xoj6Wkqqv5nZ4KkeqZP3lk8hYquBGYCRVXO+Pm+EJglEzdWtnNbdQfWU98HWZS4rqKVt9Qv0tz8GZwbgisf8S2fhfr5TPpduwoQbvsoQmntxXxpBgYGbzCMzu0sIUkS1c58PtByJZFUAkVVsUqmrIWXw2ShwOLI0DhOICJQptMFOegb5FdHt3JVaQNry1tQUBmPR/nLyT2UWN28p+WKDIsru0k/bQzSl6Y6V4Fme09onIGIVturoPJ4107e37xypl/DWTG/oJI/ndiju69dJ6oY0l255SV11LsLiaaSmEQRp8mM63Ja1j1F/FTMrS8ewSSKuM02vGZrVj/kc8Epm1lWVMtfu/fp7l9V2jBjwMXZ4JAtrCiu43ddO3T3X1XamBOrtdeC22zl5qp2VpTUE1eSmEUp5zHCrwVFVfDFI4zHI6QUFa/Fhlu26uqyL2UEbxHizR9Mx2wrKTDbDAswAwODWefyOnO+Djjb5WmPxcb9Tcv5xp7nNe4Et9fOwyVnFm2KqrB9+CShZIwndQqXsViYaCqRUVS4zTbe0biUh3Y/p+mW3ljZrnElANibxcwf4Mj4oMaD9rWQb7GzrKiGLUPHM7ZbJBNvrZvZg/Zy79IGEzE29B/hj8d3T3bGnbKFD7VeRYO78LyLMEmUuLqskU2DnZohxzpnAbWu84/4FQWBpUU1rO8/wvA0261Ku5cWb8l5P8f5IInSWXtHzwaJVIqj/iG+f2DDpLOGSRC5s3Y+K0rqL7s4YsFig8skdMLAwOD1iVHcXqLEkkm6Q2M8MGc1+8f6iCspJEGk3l2ALxYhqiSwTvnziYI4Y9a6w2RBnKZCSSopTobG+Oic1bw8cIyuwAhes42VJfWEkwmiqaSmkPSYs2su7SYLYg5ia52ylbfWL2JhQRUHxgdQVRW32cry4joKL6Gi5EJwZHyIx6cFagQTMb6x53n+efGtkx7A50OB1cnn5t/ApoFjbB46jiyKXFPWREd+Bd4cFSX5Vgef7ljLlqEuNg12IiBwdVkjCwqqzkq3+0ZiNB7iG3tfyLjBTKoKj3Vup9TuYW5++UV8dQYGBgaXH5d0cZtMJvnSl77EL37xC/r7+ykrK+M973kPX/ziFycjaFVV5V/+5V/4/ve/z9jYGMuXL+db3/oWc+bMucivXkswEWUkGmbTYCdxJcmyolpK7W7dotSfiHLA10+bt5Qyu5sj/mHybHYq7F56Q+OMxcJ4p/3cqlN+mXpcX9Gq6cSOxyP8tnMHAIsLq7mytIFwIs7/Ht/NWDxMtStfU4h05Jfz55P6koErS+qRxNz4xLpUWChKzO8/CSEfQuNChFRyxhCDsViY44FRdo5047HYWFZUQ57FoesJfCkSiEf544lduvuSqsKrQ8e5pXpuTp6rwOrgpuo5rCprRBSErF6150O+1cENle2sLGlAIN2BzqWP8KWKqqqMxsIcHO/nkG+QcruXBYWV5Fvsup33TQOdWXXmfzy+m1pX/gX5+xgYGBi8Xrmki9uvfOUrfPe73+UnP/kJc+bM4dVXX+W9730vHo+HT3ziEwB89atf5aGHHuKRRx6hubmZ//t//y/XX389Bw8exOW6dJaoA4kofzq+mxemhDls6D9Kq6eE97auwDutI5pSU9xeM5+v73mO4BT3gz+d2M2H21ah6lwMC6wO3lq3iMc6t2Vsn+MtY1FRtaawiCupSauxTYOdmuMNRQIZw2kAJ4Kj3F23gN917sgQSzS4C6l3F5LIgZelGo+gHtiE+uzPT2/b+xJqXgni3Z9GcGu1wKPREA/veS5DD/yXk3t5R+NSlhXVYL0MhoWSqsJQJJh1/4ngGIqqIuaoQJQE8YJbTYmCoCtveT3TFx7nP3Y9kxHe8fvjO/n4nGto8hQjTUnwSyoK3TqWaRMMRgM5+U4ZGBgYvJG4pN0SXn75Ze644w5uvfVWamtrectb3sINN9zAq6++CqQ7JA8//DBf+MIXuOuuu5g7dy4/+clPCIfDPProoxf51WfSH/ZnFLYTHBgfYNdIj2a7SZB49MgrGYUtpCfdf3RwI3aTVodnM5m5srSBf1l8K2+pW8itVXP5u/k38N6WFbrdYbNo0nVRmKDEph1aU0kXWR+dcw23Vs9lbXkLH2y9kqVFNTxxYjdSLgqv4HhGYTvJ2ADqlidQpyV+xVNJnji5R3fQ7RdHXsEXj57/a5oFZFGa0QqqwV2Ys8LW4MIQiEf50cGXNal0KVXhO/tf1OicTaKoO7g5QbndjdmIrDUwMDA4Jy7p4vaqq67i2Wef5dCh9FL7zp072bBhA7fccgsAnZ2d9Pf3c8MNN0z+jMViYfXq1WzcuDHrcWOxGH6/P+PfhSSpKDzfqy8XAHiu9yD+aRe9qJKgK6gfFRpLJRnKkkYWTSXYMdLNofEBjgdH2TzYOenOMB2P2cr15a26xym2unT1re15pewZ7eVbe19g10gPXcFRfnFkC788upUbK+dQMM1797WgHtuZfd/elyCc+d6DiRibBrSd5wn2jGYfgruUcMoW3lw7X3efWZRYUFA5y6/I4FwJJmOcDI3p7oumErruJ0uKarI6VNxRM/+yGygzMDAwuNhc0rKEv/u7v2N8fJzW1lYkSSKVSvGv//qv3HfffQD096cTv0pKMqevS0pKOH78uOZ4E/zbv/0b//Iv/3LhXvg0FFUhkswe7RlJaotPPY/ZqUST2lQxXyzMt/eu50QoM5hh02AXn194o6YTaxIlVpc3EUkleKHv8KTur9ZVwAeyRKQWWJw82HEtPzy4cfIibhFN3F23gCZP0Yyv+ayZKS43lUwbqU5BQZ10FtAjlIxl3XepUesq4P6m5Tx2bBvRU3GwhVYHH2y96pKa8DfQZyaPZkA3DbDA4uBTHWv5wYGXJotfmyTztobFVDnzLsjrNDAwMHg9c0kXt7/61a/4+c9/zqOPPsqcOXPYsWMHDz74IOXl5bz73e+efNx0LamqqjMOrvzDP/wDn/rUpyb/7/f7qaqqyv0bOIVZMrG0uJrBqJ9ry1vJt9hRUYkmEzzfd4g6Z4GmO2OVZNyyFX9Cf0m9XCd160RwTFPYQrpj9JcTe3l741KNb6bbbOOO2vmsKW8mnExgliRcsiXrAItJFKl1FfDxOdcQTsZJKAou2YLHbJsxcUpNJSE0DrEwSDLYXQjZirW6DtjyhP6+sgaYpuG0SjL1rgKOBfQ73ZfTtLndZGZFcS3t3lICyRiSIOAyWfAYDgM5I55KMh6PEk0lsEomXGZrRuLd+WCXzDhMFt0bKgEo0XG7kESRenchn5t/PcFkjJSqpv/mZluGPtfAwMDA4Oy4pIvbz372s/z93/899957LwAdHR0cP36cf/u3f+Pd7343paXpeNIJJ4UJBgcHNd3cqVgsFiyW2V3qa/OU4mqw8ptj2ydjdb1mG3fUzKfJU6xZlhSBO+sW8JNDmzTHWllSjzq9e6mqbBo8lvX5d4x0c0ftfF1TeMsMYRJ69IXH+dHBjZwIpju3DpOFe+oXMa+gQjdgQQ0HUPdtRN30vzChf61sQbzxvQg63V7BW4xa0Qw906QcooS45j4EW6b0wSlbuKdhMV/d8bTGE7jJXUxRDqQSs4kkSunIXoxOba4Zj0d44sQeNvQfJaUqiAgsKarh7roFuisV54rXYuNt9Yv40aGXNfvWlLfgmkFi4LXYc/IaDAwMDN7oXNJtgXA4PGn5NYEkSSinluzr6uooLS3l6aefntwfj8dZt24dK1eef1JWLkmoCt/fv2GysAXwxSP89PAm3S5PTEmBqvLR9tVUO/MQECiwOLi3YTGrShs02j0BsMzQfTJLphw40MJINMR/7HpmsrCF9LL/jw+9TKd/WPN4VVVQj2xDXf/r04UtQPdBlN98DTWo1ScKDg/irR9GuPJOsLlAEKG6HfHtX4BCfd1ppT2Pf1h4I22eUkQEXLKVO2rm8YHWlRfcEcDg8iCaSvCHrp2smyLBUVDZMtTFL468Qihx/vIVURDpyK/gE3PXUOXIQ0Sg0Org3U1XcEvVnIuWzGZgYGDwRuKS7tzedttt/Ou//ivV1dXMmTOH7du389BDD/G+970PSMsRHnzwQb785S/T1NREU1MTX/7yl7Hb7bz97W+/yK/+NIqqsmWwi7iiTe9SSXtZfrB1ZYZdld1k5tneg1hFmTfXzsclW4mlkmzoP8JvO3fwjwtvzjiOIAisKm1k44B+9/bq0kZcWSyZxuMRhiNBuoKj5FvsVDvz8VpsSDqesofHBzUODhP8rmsH1c78zOcJjqNu/L3u4xkfQh0bQNDRFQpOLyy9GWHOlWmNrdmKMENXSz4Vb/yh9quIpRIIpC2ochFZa5CW+ozFwvSEfQxGglQ6vJTYXJdVpzEQj/JylsHDXaM9BBKxnAxv2WUz7XllVDnySKgKEgIeI7HLwMDAYNa4pIvb//qv/+If//EfeeCBBxgcHKS8vJwPf/jD/NM//dPkYz73uc8RiUR44IEHJkMcnnrqqUvK4zappDjiH8q6/2RojGgqmVHc5lsdvKtpOQ/tfo5v7HlhcruAwPtbVugmSRXbXKwua2LdNMuxCruHK0sbdIvV0WiIb+1dR3fYN7nNIpn4xNw11LoKND9zeHww6/voCfkmfXMnScYhPIMbxcBxqNJ3bBBECc5xoMZuMutKIwzOj56wj4d2PZexylBodfDg3Gsvm8jjcDKhka1MxZ+IUkp2K7ZzJdvNpIGBgYHBheWSLm5dLhcPP/wwDz/8cNbHCILAl770Jb70pS/N2us6VyRRpMTmYr+vX3d/gcWhawVU4cjj8wtuZNvwSY4Fhim0OlhZ0kC+xa4rQXDKFm6v6WB5cS3r+44QSSVYUVxHnatAt8MWTSX4Tef2jMIW0hPd39jzPP+06FbNhL7eINsEeRa7tlMqmUC2QpbBOLzF+tsNLhnGYmG+uWedRj4zHA3x44Mv88Cc1TgvA7uqmTydARw5vilKKiliqSSyKOlq3Q0MDAwMLgzGGXcWkASRq091VPX6RrdWz9VdDjWJIiV2NzdXzyGeSmISRI0GeTpO2YpTtlLnKkBVmXHaOhCPsX34pO6+aCpJX3hcU9x25Ffwu84dutZbt1TN1XaUHR6EBWtQX/mL9kksNoTi6hnfj8HFxx+PMBYP6+47GhgmmIhdFsWtS7bQ4inh4PiAZl+F3YsrRxG3SSXFUDTI872H6AwMU2R1cUNlGyU2l6G5NTAwMJgFDEHiLFFodfLB1qsyOrQiAnfUzKN2hoSiCcyS6YyF7VREQTyjjVBSTc24TDs+LVgCwCaZ+EDrlRnWSQJpB4c2b6nm8YJkQlh4HTQtydxhdyO+5bPgMnw8L3XC09K2phNXtN6tlyIO2cK7m6+gZprUpdTm5iPtq3IWE9wZGOH/2/YX1vUd5kRwjK3DJ/i3HU+ydfgkcR2fWwMDAwOD3GJ0bmcJi2RiXn4F/7L4VoYiQZKqQonNhVu2YpnBH/ZCYpVkXLKVQBbJgJ6B/HA0xJPd+7i/aRmKqhJLJfFa7Owd6+WJE3u4r3GJZglWcHoRr38XrHwzqm8QweYAVz4482b0IzY4e5JKCn88SlJVkEUJr9mWs99tviW7JZksShdd4xxOxCfjbu0mecahsAKrg7+ds4bxeITRWAiv2Y7XYtONp34tjMciPHLoZd0wh/858gqt3hIKpcvLms7AwMDgcsMobmcRWZIokJw5iajNBR6zjTtr5/PTw5s1+5o8ReSZtTrdLUNddAZG+O8DL2EWJWRRIpyMowImQeT22nm6+kLB6gCrA6GgTLPP4PwYj0V4rvcgz/cdSt9smG28uXY+8/IrcjL97zJbWV5Uy+ahLs2+myrb8cgXxwlAUVX6w+P86uhWDpySGjS5i7mvcQlldndWpwy32YrbbL0g6V/BZIzhLAl7SVVhKBKg8BL5/hsYGBi8XjFkCW9gREFgQUEl721egfdU50oWJVaXNfH+lit1p72nihjiSorQqcL29AOyyxwMck8wEePRo6/w1+59k9GuvniERw5t4pWh42eMgz0b7CYzd9ct5KbK9smhrIngjtVlTciSdhhyNhiJBvnKzqcnC1uAw/5BvrLzqawF5sXG+HYYGBgYXHiMzu0lzlgsTDARYzQWwiVbcctWCm3ZOz8pRWE8HmEsFiaupCi0OmaUPjhkC8uLa2n1lhBLJTGJIi6zFbOo/9FYVlTL872HdPctKarOSafQ4OzxxyPsGOnW3feH4zuZl19BfraY43PAY7FxW00HV5c1kVRSyFJa+nCxfIRTisJLA8eIphKafbFUknW9h7mzbj4mHReSQDyKPxHFF4/gka2nOrm56T47TGYKLA5NyAqkVzaKrZeHbZqBgYHB5YxR3F7CDEeC/OzIZg74Tnemim0u/qZtFeUOr+bxiVSKw/5Bvr9/A5FTF30RgVuq5rCmohlnlmlwQRDO2oy/yOpkSWE1rw6fyNjulC3cWt1hWB7NMlMT76YTTiYmPwe5wCRKGveMi0UklWDvWG/W/ft8fdyUbMdlzixuR6Mh/vvASxwLnE7Tq7B7eaD96hlvGs8Wr8XOu5uv4OE9z6FMW8W4p2FxzobWDAwMDAyyY1QiFwlVVWcc+AkmojzetSOjsAUYjAT41r51uub5o/EQ/7X3hYyLqoLKn07uodKZx8LCqvN+3S6zlbc1LGZZUS1HA8MoikKe1c78gsoLpiU80+/qjUy2G5YJ9DqXrwdMoohLtgHa+GYAt2zFNM0tJJSI8bPDmzMKW0gHVHx3/4t8Yu6anAQv1LsK+ceFN/N09wG6giMUWp3cVNVOmc1j3PwZGBgYzALGmXYWCcSjDEYDbOg/SlJJcUVxHZUOLx6drmkgEWNbFg/a4WiIsXhEU9xuGezSdIsm+OPx3TS6i3Jy8ZYEkTyrnagvQSgRo8FTpJt+dj6EE3FGYyFeGjjGeDzMosJq6l2FOVlifz1RaHHgMJkn3QKm0uYtxfU6lYlYJZkbK9uydm9vqGzXeMoGEzH2ZQlSORkaw5+I5uT7IUsS5Q4v9zUtIZZMIktShnWegYGBgcGFxShuZ4lAPN2JfWng2OS2LUPHaXAX8qHWqzSygFgqOaMH7Vgs01RfURV6Qr6sjx+OBXWDF86VUDLGc70H+dOJPZPbXh0+QanNzSc61sxoG3W2RJJxNg4c5bHO7ZPbtg6fpNDq4JMda41p8yl4LXY+Nucavr7nucmBMkj7Kr+zcdlFt+m6kFQ4vNxY2c6T3fsytl9b3kK1jhNC9Awes3o3COeDWTRhNhunWAMDA4PZxjjzzhJ94fGMwnaCo/5hto+c5Jqy5oyld6skI4sSCSWle7ziaQWeKIg0eorZnmW4qMLuxZyDJeqxaDijsJ2gP+Ln2Z6D3FmrP8RzLozHIxmF7QTD0RB/PrGHexu0XrpvVERBoMaVzz8vupWuwAhD0QC1zgJK7G7yzlJHfbnilC3cVNXOFcV17Pf1oaoqbXll5Jlt2HU61jaTjICAmuWm8fXa5TYwMDB4o2FYgc0CSSXFC1kcBgCe7z1MIBHL2OY121hV0qD7+Bpnvu5094L8SqxZir47a+fnxMlgy9DxrPs29B/RvI/Xws7Rnqz7Ng92EczBc7yeEAWRAquDxUXV3FQ1h9a80td9YTtBOBnnxf7DbBs+yfaRbtb1HiacZYjOLVtZXlyju29OXhnuHMXvGhgYGBhcXIz21yygqCrRGSJK46kkyjTJgNUkc11lKwpqWqN7an+7t4y3Ny7RnVrPt9r59Lzr+OGBjZNT9A6ThXsbFufMsD6azD59H0+lsnbFcvUcSVWZUa5h8MZhJBri33c+jW9KTPQR/xDbR07y9wtu0ISlWE0yd9UuRBJEXh7sRFFVBAQWFVby1vrFho2dgYGBwesEo7idBcySiWVFtQxHQ6ytaMEtW1FQT3V0D1PjysOpc2EtsDp5U1UH15Q3E07GsUgm7JI561CVKIhUO/P59Ly1BBMxUqqCw2TBa8mdH+miwirW9R/W3Tc3vxxblsGZRCrFeCJCOBlHFiVcslX3PQP/f3v3Hh1VdbAN/Jl7ZiZzIddJIIREAgHCJSaIEBUoN3nRV4taVBRrxYpAAeGrliIL7CcX6Qu1SomCVqBqoVVwUQUhLRqKfCiXRmKgEEgggAmBXIeZZCYzs78/aOZlyEUuM5nMmee31qxF9j6Zs8/aZPJkn332Rv+oRGw/W9RqXZoxrs1zUPjwCIFDF8t8gm2z+qZG7K88jXuT+rZ40NGk0WLSbVkYn9QPDe4mRChUMKo0iJDw3GQionDDcNtB0s3x0CpV+EvJYVxqvAzgyhy/B5IHoo85vtV5qlWNNnx+tghfXSjx7jSVbo7H5J53IE7b9mLwRrXWb4vSXytBb0KqIabFckoquQI/7jGwxRPqAGBtasSe8pPYcbbIO4e4hyEaz/Qe1up1xEQY0MdswbFrnmxXyOT4yW23c4SN0OBy4uCltqfIHL5UhnssPVtd/UCjUCFWyz+QiIikinNuO4jT48a6f3/lDbbAleW+3j/5TavzVBtdTcg7dwx7Kk76bKH679oLWPfvvagK0vaiJrUWz/W5Cw/2GAizWguNQonbo5OwIPNexGuNLY73CIGDF89g25kjPg/HnbZW4XeF/2ix6gMAGNUReLrXUDySmokuGh00CiUGRnXFgsx7kagzBfT6KDQoZDLvVsCtUcuVfl+ejoiIQgNHbjuARwj8vwslba588LczhXi2T47PqGetswF7L5xq9fiyyzWoczYEbbcos0aHcd36YGhcCgSuPIXe1jqedU47PmtldQUAqHbYUWGva/XhJ5NGix8lpmNwbA94hIBWoeStY/KKUKoxums6TtRVtlo/umtv6FT8/0JEFI44tNEBXB43Sq1Vbdafs9f6rFEKAI3upjbDMABcvGoEOBjkMjnMGh26aHTtLlDv9LjbXUHhXDtr88plMpjU2ivnYLCla6QYopEZ3a1F+YCoruhpigtCi4iIqDPgyG0HUMjlSNAZW8whbRYboYdK7tsVGoUScpmszR3HokJkqSeV7MruTI1tLM8U387cYaL2GNVaPN5zMEZ1Tce+CyUQAhhmSYVFawjYnHMiIur8GG47gEImx92WNHzxfXGrS2VN6N4f+mtuoRpVEciO6d7qurIxEZHoog6NcGtSR2B0Ym98erbl1AS9Uo1uev8sUUbhqfnhyTSO1BIR0X9wWkIHiYnQ4/m+d/tssqCUyfFISiaSI6NaHK9XafBAj4Hoa7b4lMdrDZjRdzhitKGxBa1CrsA9iWm4My7Fp7yLWocX+o8Km80GiIiIqGPIhGjjvncYqa+vh8lkQl1dHYzGlk/8+4vL40adsxG1DjtcwoNojR5GdUS7W8nWOuy43ORAtcMOg1oDgyoCMRHtB1u7ywlbk+PKg1hKNYytLId0qxpdTlhdTrg9HmiVKph+4Daw3eWE1dmIGqcdWoUKxv/MpSUiqnXY0eh2QSmXw6DUQKPkUm1E1NL15jVOS+hASrkC0RH6G1rlwKzRwazRodt17jB2ocGKzacO4mhNOQSABJ0Jj/fMRo/I6HZD9I242HAZH5UcxrfV5yEgEKc14LHbspFqiEFEG7+UdEo1dEo14nWB++OBiEKLvcmJ43UX8NeSw6hy2CCXyZAd0x0/7jGozc1qiIh+CKclSEhVow3/820eiv4TbAGg3F6HVUd2e7fjvVXVjTasKvw7CqrPeecPVzZY8cZ3X7S78gER0bVO1l/EW8f+iSrHlXW7PULgm4tn8GbRF6htZQ1sIqLrwXArIcdrK1Df1NiiXEBga+m3sLuct3yO05erUN3KLx0B4OPSw7jczrJfRCRtbuFBdaMNJ2ov4Nuqc6iw18Puav0zoc7RgL+WHm617nt7PS40WAPZVCKSME5LkAiP8OBI9fk260usF+Fwu6C7xfVii6rL26wrtVbB6XYB3B6XKOy4PG6UWKuwpigfDVct/TcsPhU/7jGwxfJsTo8Lle0E2JP1F9HbHB+w9hKRdHHkViLkMnm7c3mNKi3kMtktn6e9cxhUEX45BxGFnhpHA35fuNsn2ALAvgslOHDxTIs1uxUyOTTytsdX+MApEd0shlsJyYm/rc26cUl9fnBFg+uRFdMdMrQeYMd07cPF84nC1LHaCriEp9W6HWePot7Z4FNmUEXg7oSerR6vlMnRi2sXE9FNYriVkCiNHj9NuxPya8LnHbHJGBDV1S/nMGt0eDY9p8UI7cCorhgS34Mjt0RhqsJe12adtakR7mtGblUKBcZ0TUeaKda3XK7AjH7DYeYfykR0kzjnVkIilCrcHpuEnqZYlFgvodHlQk9TLExqLSL9NA9Wo1Cif1Qi/m/2/Sipv4QGlxOpxliY1VoYArCeLlG4aHK7cdnlgACgVaigDbG1XnuaYvGP74+3WhevNUApbzmWYtbo8PP0u1HjsKHUWgWjOgLdI6NgVmuhlCsC3WQikiiGW4nRKFSI1aoQqzUE7BxqhRIxisgf3EyCiK5PdaMNeef/ja8unEKTx40BUV3xYI+BiNMaoJCFxg22HpHRMKoiWl2xZWJKZpvToozqCBjVEUg2RAe6iUQUJkLjU5OISKJqHDb87rvd2P39cTjcLniEQEHVOSz7105cbLgc7OZdt6gIPf7PgNFINcR4y/RKDZ5KG4Jexth2vpOIyL84cktEFEQn6y62uiSWw+PCjrPf4fGed0Djp90FAy1eZ8SMfsNxuakRLo8HOqUaJo02ZEafiUgaQuMTk4hIglweDw5cPNNmfWF1ORpczpAJtwAQqdL4bY4/EdHN4J/TRERBIpfJoFe2HQS1SiVkXIGEiOiGMNwSEQWJXCbDPW2s9QoAIxN7w6jiKiRERDeC4ZaIKIjitAaM69a3RXlPYyyyY5I5cktEdINCZyIXEVEIcbib0Oh2QSVXQKdUt3mcXqXBuG59MDi2O76uPI1GtwuDY5Nh0Rn9sqsgEUmL2+1GU1PTDx8YglQqFRSKW1/jmuGWiMiPHG4XKhus2F72Hcps1YjW6PFf3TOQpO8CfRsPWulVGuhVGiRFRnVwa4koVAghUFFRgdra2mA3JaDMZjMsFsst3bViuCUi8hMhBE7WXcSbRV9C4Mp2s5cabTheuBsPp2TinoS0kFr5gIg6j+ZgGxcXB51OJ7kpS0II2O12VFZWAgASEhJu+r34KUtE5Cd1zgb8qfhrb7C92pbTBRgUk4RYhbR39mtwOWFtcsAtPNAqVDBrdMFuElHIc7vd3mAbHS3d3fy02itTsSorKxEXF3fTUxQYbomI/MTmcqLGaW+1ziMEKu31iJXwttUXGy5j06mDKKr5HgJAF7UOk27LQro5Htp25h0TUfua59jqdNL/Y7H5Gpuamm463HK1BCIiP/mhm4RS3qmrxmHHqsK/47v/BFsAqHHa8daxf6LUWhXUthFJhdSmIrTGH9fY6T9pz58/jyeeeALR0dHQ6XQYNGgQDh065K0XQmDx4sVITEyEVqvFiBEjUFRUFMQWE1G40qs0iNMaWq1TyRWI0Up31LbscjWqHa2PWv+15DDqnY0d3CIiCledOtzW1NQgJycHKpUKO3bswNGjR7Fy5UqYzWbvMStWrMCqVauwevVqHDhwABaLBWPGjIHV2nKvdiKiQDKptXi611Co5C1vpU1JGwKTSrpLex2vvdBm3ff2OjR53B3YGiK6XjKZDJ988kmwm+FXnXrO7WuvvYakpCS899573rIePXp4/y2EwOuvv44FCxZg4sSJAIANGzYgPj4eH374IZ577rmObjIRhbnukV2w8Pb/wlflJ3HKeglxWgN+lNgbsRGRUPlh/cbOKradUWm9UgN5GNxOJeqMKioqsGTJEnz22Wc4f/484uLiMGjQIMyZMwejRo0KdvMColOP3G7btg3Z2dl45JFHEBcXh8zMTKxbt85bX1paioqKCowdO9ZbptFoMHz4cOzbt6/N93U4HKivr/d5ERH5g1KuQLzWgAdSBmJmv+GY3PMOJEV2QYRSFeymBVS/LoltBtgxXdNhUnMbYaKOdvr0aWRlZWH37t1YsWIFCgsL8fnnn2PkyJGYMWNGsJsXMJ063JaUlCA3NxdpaWnYuXMnpk2bhlmzZmHjxo0Arvw1AgDx8fE+3xcfH++ta82yZctgMpm8r6SkpMBdBBGFJYVMDq1SDaW8U3/M+k0XtQ4z+w5vMSVjUHQ3DLOkQi7hh+mIOqvp06dDJpPhm2++wcMPP4xevXqhX79+mDt3Lvbv39/q97z00kvo1asXdDodUlNTsXDhQp8d0b799luMHDkSBoMBRqMRWVlZOHjwIADgzJkzuP/++9GlSxfo9Xr069cP27dv75BrvVqnnpbg8XiQnZ2NpUuXAgAyMzNRVFSE3NxcTJkyxXvctU/WCSHafdpu/vz5mDt3rvfr+vp6BlwiolugUijQ2xyPxVkTcN5WC5vLgeTIaJjUWkS2sTMbEQVOdXU1Pv/8cyxZsgR6vb5F/dXPL13NYDBg/fr1SExMRGFhIZ599lkYDAa8+OKLAIDJkycjMzMTubm5UCgUKCgogEp15c7UjBkz4HQ6sWfPHuj1ehw9ehSRkR3/IG2nDrcJCQno27evT1mfPn3w8ccfAwAsFguAKyO4V+9kUVlZ2WI092oajQYaDT9siYj8SSlXICYiEjESXsuXKFScPHkSQgikp6ff0Pe9/PLL3n/36NED8+bNw+bNm73htqysDL/85S+975uWluY9vqysDA899BD69+8PAEhNTb3Vy7gpnfo+UU5ODo4fP+5TduLECSQnJwMAUlJSYLFYkJeX5613Op3Iz8/HsGHDOrStRERERJ2FEFdWnL7RdWM/+ugj3HXXXbBYLIiMjMTChQtRVlbmrZ87dy6mTp2K0aNHY/ny5Th16pS3btasWXj11VeRk5ODRYsW4ciRI/65mBvUqcPtCy+8gP3792Pp0qU4efIkPvzwQ6xdu9Y7CVomk2HOnDlYunQptm7diu+++w4//elPodPp8Pjjjwe59UQUruwuJy7Y61Fafwnf2+pg5RqvRNTB0tLSIJPJcOzYsev+nv379+PRRx/F+PHj8emnn+Jf//oXFixYAKfT6T1m8eLFKCoqwoQJE7B792707dsXW7duBQBMnToVJSUlePLJJ1FYWIjs7Gy8+eabfr+2HyITzdG+k/r0008xf/58FBcXIyUlBXPnzsWzzz7rrRdC4JVXXsHbb7+NmpoaDBkyBH/4wx+QkZFx3eeor6+HyWRCXV0djEZjIC6DiMJErcOOv5QcxuFLZd6dulIiozG1Tw5v1xPRTWlsbERpaSlSUlIQEXH9K4+MHz8ehYWFOH78eIt5t7W1tTCbzZDJZNi6dSsefPBBrFy5EmvWrPEZjZ06dSo++ugj1NbWtnqOxx57DDabDdu2bWtRN3/+fHz22Wc3NILb3rVeb17r1CO3AHDfffehsLAQjY2NOHbsmE+wBa6M3i5evBjl5eVobGxEfn7+DQVbIiJ/cbia8Mnpb3HoqmALAKWXq7CmaA/qnQ1BaxsRhZ81a9bA7XbjjjvuwMcff4zi4mIcO3YMb7zxBoYOHdri+J49e6KsrAybNm3CqVOn8MYbb3hHZQGgoaEBM2fOxJdffokzZ87gq6++woEDB9CnTx8AwJw5c7Bz506Ulpbi8OHD2L17t7euI3XqB8oIqG60odR6CSfqLsKiMyKjSwK6aHRQtrIDEhEFV31TI76+eLrVuvP2WtQ6G2BUS3eXMiLqXFJSUnD48GEsWbIE8+bNQ3l5OWJjY5GVlYXc3NwWxz/wwAN44YUXMHPmTDgcDkyYMAELFy7E4sWLAQAKhQJVVVWYMmUKLly4gJiYGEycOBGvvPIKAMDtdmPGjBk4d+4cjEYj7r33Xvzud7/ryEsGEALTEjpCZ52WcMFej/858nfUN/3vfD2lTI5ZGSPR0xgLRZisn0kUKs5ersGr/9rRZv2MvsMxILprB7aIiKTgZqclhKKwmJYQrmxNDmw4sd8n2AKAS3iw5uge1PH2JlGnE6FQtrvNbBcNR22JiAKN4baTsjY5cMp6qdW6RncTKhutHdwiIvohRpUWQ+NSWq3rpjfDxCkJREQBxzm3nZRLuNutb3A1tVtPRB1Po1Tiv5MHwulx4+DFM96HylIN0XgmPYfzbYmIOgDDbSelU6oRqdLgcpOj1foEnamDW0RE18Os0WJyz8H47+QBsDU5EKFUwaDSIFIl7XlyRESdBacldFJmtRaPpNzeat2w+FQY1dw+mKiz0irViNMakGKMQYLOxGBLRNSBGG47KblMjgHRXTGz33DvKK1RFYGfpGbhxz0GQqdkuCUiIiK6FqcldGI6pRr9o7oiOTIaLo8bcpkMJrX2hveJJiIiIgoXDLchwKjmLU0iIiKi68FpCUREREQkGQy3RERERCQZDLdEREREYcLW5ECFvR6l9ZdQYa+HrY0lR/1pzZo13u10s7Ky8M9//jOg5+OcWyIiIqIwUO2w4U8nvsbR2gpvWV+zBU/2GoIojT4g59y8eTPmzJmDNWvWICcnB2+//TbGjx+Po0ePonv37gE5J0duiYiIiCTO1uRoEWwB4GhtBf504uuAjeCuWrUKzzzzDKZOnYo+ffrg9ddfR1JSEnJzcwNyPoDhloiIiEjyrE2OFsG22dHaClgDEG6dTicOHTqEsWPH+pSPHTsW+/bt8/v5mjHcEhEREUlcg8t5S/U349KlS3C73YiPj/cpj4+PR0VF60HbHxhuiYiIiCROq1TfUv2tuHbzKSFEQDekYrglIiIikjiDSoO+ZkurdX3NFhhUGr+fMyYmBgqFosUobWVlZYvRXH9iuCUiIiKSOL1Kgyd7DWkRcJtXS9AHINyq1WpkZWUhLy/PpzwvLw/Dhg3z+/macSkwIiIiojAQpdFjanoOrE0ONLic0CrVMKg0AQm2zebOnYsnn3wS2dnZGDp0KNauXYuysjJMmzYtYOdkuCUiIiIKE/oAh9lrTZo0CVVVVfjNb36D8vJyZGRkYPv27UhOTg7YORluiYiIiChgpk+fjunTp3fY+TjnloiIiIgkg+GWiIiIiCSD4ZaIiIiIJIPhloiIiIgkg+GWiIiIiCSD4ZaIiIiIJIPhloiIiIgkg+GWiIiIiCSD4ZaIiIiIJIM7lBFRwNiaHLC7mgAAepUaOqU6yC0iIiKp48gtEfmdW3hw7nIN1hzdg5cPbsPLB7dh3bG9KLfXQQgR7OYREYUt0WiDqC6HKC+BqK6AaLQF9Hx79uzB/fffj8TERMhkMnzyyScBPR/AkVsiCoBLjZfx2re74PS4vWVHayuw4ttdWJA5HjERkUFsHRFReBLWanh2rQfOFP1vYXIG5GOfgswQFZBz2mw2DBw4EE8//TQeeuihgJzjWgy3RORXLo8b/zh/3CfYNrO7mvD1hVKM794PchlvHBERdRTRaGsZbAHgzHfw7NoA+YSfQxah9/t5x48fj/Hjx/v9fdvD3y5E5Fd2lxPHaivarP+uphyNblcHtoiIiGCvbxlsm5357kq9RDDcEpFfKWUKGFSaNuuN6ggoOWpLRNSxHA23Vh9C+BuGiPxKp1JjXLe+bdaP6doHagVnRBERdSiN9tbqQwjDLRH5XaohBsMT0lqUT+iegQSdMQgtIiIKczojkJzRel1yxpV6ieDwCRH5nUEdgQeSB2BEQhqO1VRALpcj3RwPk0oLnYpr3RIRdTRZhB7ysU/Bs2vDlTm2zZpXSwjAw2TBwnBLRAGhV2mgV2mQqDcHuylERARAZoiCfMLPrzw85mi4MhVBZwxosL18+TJOnjzp/bq0tBQFBQWIiopC9+7dA3JOhlsiIiKiMCGL0AMdOEp78OBBjBw50vv13LlzAQBPPfUU1q9fH5BzMtwSERERUUCMGDGiw3emDKkHypYtWwaZTIY5c+Z4y4QQWLx4MRITE6HVajFixAgUFbWxjhsRERERSVrIhNsDBw5g7dq1GDBggE/5ihUrsGrVKqxevRoHDhyAxWLBmDFjYLVag9RSIiIiIgqWkAi3ly9fxuTJk7Fu3Tp06dLFWy6EwOuvv44FCxZg4sSJyMjIwIYNG2C32/Hhhx8GscVEREREFAwhEW5nzJiBCRMmYPTo0T7lpaWlqKiowNixY71lGo0Gw4cPx759+9p8P4fDgfr6ep8XEREREYW+Tv9A2aZNm3D48GEcOHCgRV1FxZX96+Pj433K4+PjcebMmTbfc9myZXjllVf821AiIiKiAOroB7OCwR/X2KlHbs+ePYvZs2fj/fffR0RERJvHyWQyn6+FEC3KrjZ//nzU1dV5X2fPnvVbm4mIiIj8SaVSAQDsdnuQWxJ4zdfYfM03o1OP3B46dAiVlZXIysrylrndbuzZswerV6/G8ePHAVwZwU1ISPAeU1lZ2WI092oajQYajSZwDSciIiLyE4VCAbPZjMrKSgCATqdrdxAvFAkhYLfbUVlZCbPZDIVCcdPv1anD7ahRo1BYWOhT9vTTTyM9PR0vvfQSUlNTYbFYkJeXh8zMTACA0+lEfn4+XnvttWA0mYiIiMjvLBYLAHgDrlSZzWbvtd6sTh1uDQYDMjIyfMr0ej2io6O95XPmzMHSpUuRlpaGtLQ0LF26FDqdDo8//ngwmkxERETkdzKZDAkJCYiLi0NTU1OwmxMQKpXqlkZsm3XqcHs9XnzxRTQ0NGD69OmoqanBkCFDsGvXLhgMhmA3jYiIiMivFAqFXwKglMlEODx69wPq6+thMplQV1cHo9EY7OYQERER0TWuN6916tUSiIiIiIhuBMMtEREREUlGyM+59YfmmRncqYyIiIioc2rOaT80o5bhFoDVagUAJCUlBbklRERERNQeq9UKk8nUZj0fKAPg8Xjw/fffw2Aw3NSiyPX19UhKSsLZs2f5QFqYYJ+HH/Z5+GGfhyf2e+clhIDVakViYiLk8rZn1nLkFoBcLke3bt1u+X2MRiN/EMIM+zz8sM/DD/s8PLHfO6f2Rmyb8YEyIiIiIpIMhlsiIiIikgyGWz/QaDRYtGgRNBpNsJtCHYR9Hn7Y5+GHfR6e2O+hjw+UEREREZFkcOSWiIiIiCSD4ZaIiIiIJIPhloiIiIgkg+GWiIiIiCSD4fY6LVu2DIMHD4bBYEBcXBwefPBBHD9+3OcYIQQWL16MxMREaLVajBgxAkVFRUFqMd2q3NxcDBgwwLuQ99ChQ7Fjxw5vPftb+pYtWwaZTIY5c+Z4y9jv0rN48WLIZDKfl8Vi8dazz6Xp/PnzeOKJJxAdHQ2dTodBgwbh0KFD3nr2e+hiuL1O+fn5mDFjBvbv34+8vDy4XC6MHTsWNpvNe8yKFSuwatUqrF69GgcOHIDFYsGYMWNgtVqD2HK6Wd26dcPy5ctx8OBBHDx4ED/60Y/wwAMPeD/c2N/SduDAAaxduxYDBgzwKWe/S1O/fv1QXl7ufRUWFnrr2OfSU1NTg5ycHKhUKuzYsQNHjx7FypUrYTabvcew30OYoJtSWVkpAIj8/HwhhBAej0dYLBaxfPly7zGNjY3CZDKJt956K1jNJD/r0qWLeOedd9jfEme1WkVaWprIy8sTw4cPF7NnzxZC8OdcqhYtWiQGDhzYah37XJpeeuklcdddd7VZz34PbRy5vUl1dXUAgKioKABAaWkpKioqMHbsWO8xGo0Gw4cPx759+4LSRvIft9uNTZs2wWazYejQoexviZsxYwYmTJiA0aNH+5Sz36WruLgYiYmJSElJwaOPPoqSkhIA7HOp2rZtG7Kzs/HII48gLi4OmZmZWLdunbee/R7aGG5vghACc+fOxV133YWMjAwAQEVFBQAgPj7e59j4+HhvHYWewsJCREZGQqPRYNq0adi6dSv69u3L/pawTZs24fDhw1i2bFmLOva7NA0ZMgQbN27Ezp07sW7dOlRUVGDYsGGoqqpin0tUSUkJcnNzkZaWhp07d2LatGmYNWsWNm7cCIA/66FOGewGhKKZM2fiyJEj2Lt3b4s6mUzm87UQokUZhY7evXujoKAAtbW1+Pjjj/HUU08hPz/fW8/+lpazZ89i9uzZ2LVrFyIiIto8jv0uLePHj/f+u3///hg6dChuu+02bNiwAXfeeScA9rnUeDweZGdnY+nSpQCAzMxMFBUVITc3F1OmTPEex34PTRy5vUG/+MUvsG3bNnzxxRfo1q2bt7z5ydpr/6KrrKxs8ZcfhQ61Wo2ePXsiOzsby5Ytw8CBA/H73/+e/S1Rhw4dQmVlJbKysqBUKqFUKpGfn4833ngDSqXS27fsd2nT6/Xo378/iouL+bMuUQkJCejbt69PWZ8+fVBWVgaAv9NDHcPtdRJCYObMmdiyZQt2796NlJQUn/qUlBRYLBbk5eV5y5xOJ/Lz8zFs2LCObi4FiBACDoeD/S1Ro0aNQmFhIQoKCryv7OxsTJ48GQUFBUhNTWW/hwGHw4Fjx44hISGBP+sSlZOT02I5zxMnTiA5ORkAf6eHvOA9yxZann/+eWEymcSXX34pysvLvS+73e49Zvny5cJkMoktW7aIwsJC8dhjj4mEhARRX18fxJbTzZo/f77Ys2ePKC0tFUeOHBG//vWvhVwuF7t27RJCsL/DxdWrJQjBfpeiefPmiS+//FKUlJSI/fv3i/vuu08YDAZx+vRpIQT7XIq++eYboVQqxZIlS0RxcbH44IMPhE6nE++//773GPZ76GK4vU4AWn2999573mM8Ho9YtGiRsFgsQqPRiHvuuUcUFhYGr9F0S372s5+J5ORkoVarRWxsrBg1apQ32ArB/g4X14Zb9rv0TJo0SSQkJAiVSiUSExPFxIkTRVFRkbeefS5Nf/vb30RGRobQaDQiPT1drF271qee/R66ZEIIEcyRYyIiIiIif+GcWyIiIiKSDIZbIiIiIpIMhlsiIiIikgyGWyIiIiKSDIZbIiIiIpIMhlsiIiIikgyGWyIiIiKSDIZbIiIiIpIMhlsiIiIikgyGWyKiELNv3z4oFArce++9wW4KEVGnw+13iYhCzNSpUxEZGYl33nkHR48eRffu3YPdJCKiToMjt0REIcRms+Evf/kLnn/+edx3331Yv369T/22bduQlpYGrVaLkSNHYsOGDZDJZKitrfUes2/fPtxzzz3QarVISkrCrFmzYLPZOvZCiIgChOGWiCiEbN68Gb1790bv3r3xxBNP4L333kPzDbjTp0/j4YcfxoMPPoiCggI899xzWLBggc/3FxYWYty4cZg4cSKOHDmCzZs3Y+/evZg5c2YwLoeIyO84LYGIKITk5OTgJz/5CWbPng2Xy4WEhAT8+c9/xujRo/GrX/0Kn332GQoLC73Hv/zyy1iyZAlqampgNpsxZcoUaLVavP32295j9u7di+HDh8NmsyEiIiIYl0VE5DccuSUiChHHjx/HN998g0cffRQAoFQqMWnSJPzxj3/01g8ePNjne+644w6frw8dOoT169cjMjLS+xo3bhw8Hg9KS0s75kKIiAJIGewGEBHR9Xn33XfhcrnQtWtXb5kQAiqVCjU1NRBCQCaT+XzPtTfnPB4PnnvuOcyaNavF+/PBNCKSAoZbIqIQ4HK5sHHjRqxcuRJjx471qXvooYfwwQcfID09Hdu3b/epO3jwoM/Xt99+O4qKitCzZ8+At5mIKBg455aIKAR88sknmDRpEiorK2EymXzqFixYgO3bt2PLli3o3bs3XnjhBTzzzDMoKCjAvHnzcO7cOdTW1sJkMuHIkSO488478fTTT+PZZ5+FXq/HsWPHkJeXhzfffDNIV0dE5D+cc0tEFALeffddjB49ukWwBa6M3BYUFKCmpgYfffQRtmzZggEDBiA3N9e7WoJGowEADBgwAPn5+SguLsbdd9+NzMxMLFy4EAkJCR16PUREgcKRWyIiCVuyZAneeustnD17NthNISLqEJxzS0QkIWvWrMHgwYMRHR2Nr776Cr/97W+5hi0RhRWGWyIiCSkuLsarr76K6upqdO/eHfPmzcP8+fOD3Swiog7DaQlEREREJBl8oIyIiIiIJIPhloiIiIgkg+GWiIiIiCSD4ZaIiIiIJIPhloiIiIgkg+GWiIiIiCSD4ZaIiIiIJIPhloiIiIgk4/8D9O84wB/POSgAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "\n", "data = pd.read_csv(\"Diabetes Missing Data (1).csv\")\n", "\n", "\n", "plt.figure(figsize=(8, 6))\n", "sns.scatterplot(x='Age', y='Glucose', data=df, hue='Class', palette='Set2')\n", "plt.title('Age vs Glucose')\n", "plt.xlabel('Age')\n", "plt.ylabel('Glucose')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 56, "id": "32c816d3-268f-4b8a-a9fd-b0b79eaf0ce1", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\alaam\\AppData\\Local\\Temp\\ipykernel_16484\\918050597.py:2: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.boxplot(x='Class', y='Diastolic_BP', data=df, palette='Set1')\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 6))\n", "sns.boxplot(x='Class', y='Diastolic_BP', data=df, palette='Set1')\n", "plt.title('Diastolic_BP Levels by Class')\n", "plt.xlabel('Class')\n", "plt.ylabel('Diastolic_BP')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 58, "id": "056cbcb7-2ff8-4603-a311-4aaaa3bbb7bf", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n" ] }, { "cell_type": "code", "execution_count": 59, "id": "4b8a6ff2-716d-4c40-845d-d4ae14a757b8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Class\n", "0 500\n", "1 268\n", "Name: count, dtype: int64" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ " data['Class'].value_counts()\n" ] }, { "cell_type": "code", "execution_count": 73, "id": "666fdc46-5fc6-4b04-b788-f7471d6d85f7", "metadata": {}, "outputs": [], "source": [ "label_encoders = {}\n", "for column in data.select_dtypes(include=['object']).columns:\n", " le = LabelEncoder()\n", " data[column] = le.fit_transform(data[column])\n", " label_encoders[column] = le" ] }, { "cell_type": "code", "execution_count": 74, "id": "368f20a9-11f3-4294-bd83-bef41f9e9dc3", "metadata": {}, "outputs": [], "source": [ "X = data.drop('Class', axis=1).values \n", "y = data['Class'].values" ] }, { "cell_type": "code", "execution_count": 76, "id": "8ac86369-2f12-459b-a2a7-68625ff20add", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=0.30, random_state=42\n", ")\n" ] }, { "cell_type": "code", "execution_count": 95, "id": "6cd232c3-71c7-4ba5-9d0c-5adc47a4541f", "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import numpy as np\n", "\n", "def evaluate_model(model_name, y_true, y_pred):\n", " \n", " accuracy = accuracy_score(y_true, y_pred)\n", " precision = precision_score(y_true, y_pred, average='weighted') \n", " recall = recall_score(y_true, y_pred, average='weighted')\n", " f1 = f1_score(y_true, y_pred, average='weighted')\n", " cm = confusion_matrix(y_true, y_pred)\n", " \n", " report = classification_report(y_true, y_pred)\n", " \n", " metrics = {\n", " 'Model Name': model_name,\n", " 'Accuracy': accuracy,\n", " 'Precision': precision,\n", " 'Recall': recall,\n", " 'F1 Score': f1,\n", " 'Classification Report': report\n", " }\n", " \n", " plt.figure(figsize=(4, 4))\n", " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False,\n", " xticklabels=np.unique(y_true), yticklabels=np.unique(y_true))\n", " plt.title(f'Confusion Matrix for {model_name}')\n", " plt.xlabel('Predicted Label')\n", " plt.ylabel('True Label')\n", " plt.show()\n", " \n", " return metrics" ] }, { "cell_type": "code", "execution_count": 102, "id": "8d7ecbf2-af72-4ff7-a086-0e1244e48f82", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Model Name: \n", "RandomForestClassifier\n", "Accuracy: 0.7273\n", "Precision: 0.7265\n", "Recall: 0.7273\n", "F1 Score: 0.7269\n", " precision recall f1-score support\n", "\n", " 0 0.79 0.79 0.79 151\n", " 1 0.61 0.60 0.60 80\n", "\n", " accuracy 0.73 231\n", " macro avg 0.70 0.70 0.70 231\n", "weighted avg 0.73 0.73 0.73 231\n", "\n" ] } ], "source": [ "\n", "\n", "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42) \n", "\n", "rf_classifier.fit(X_train, y_train)\n", "\n", "y_pred = rf_classifier.predict(X_test)\n", "\n", "evaluation_results = evaluate_model('RandomForestClassifier', y_test, y_pred)\n", " # Print the evaluation results\n", "for key, value in evaluation_results.items():\n", " if key == 'Classification Report':\n", " print(value) # Print report separately for better readability\n", " else:\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")" ] }, { "cell_type": "code", "execution_count": 110, "id": "58e26582-9d8f-4c0b-a10a-fb9bb0519ecc", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\alaam\\anaconda3\\envs\\Alaa\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py:136: UserWarning: Could not find the number of physical cores for the following reason:\n", "[WinError 2] The system cannot find the file specified\n", "Returning the number of logical cores instead. You can silence this warning by setting LOKY_MAX_CPU_COUNT to the number of cores you want to use.\n", " warnings.warn(\n", " File \"C:\\Users\\alaam\\anaconda3\\envs\\Alaa\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py\", line 257, in _count_physical_cores\n", " cpu_info = subprocess.run(\n", " \"wmic CPU Get NumberOfCores /Format:csv\".split(),\n", " capture_output=True,\n", " text=True,\n", " )\n", " File \"C:\\Users\\alaam\\anaconda3\\envs\\Alaa\\Lib\\subprocess.py\", line 554, in run\n", " with Popen(*popenargs, **kwargs) as process:\n", " ~~~~~^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\alaam\\anaconda3\\envs\\Alaa\\Lib\\subprocess.py\", line 1039, in __init__\n", " self._execute_child(args, executable, preexec_fn, close_fds,\n", " ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " pass_fds, cwd, env,\n", " ^^^^^^^^^^^^^^^^^^^\n", " ...<5 lines>...\n", " gid, gids, uid, umask,\n", " ^^^^^^^^^^^^^^^^^^^^^^\n", " start_new_session, process_group)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\alaam\\anaconda3\\envs\\Alaa\\Lib\\subprocess.py\", line 1554, in _execute_child\n", " hp, ht, pid, tid = _winapi.CreateProcess(executable, args,\n", " ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^\n", " # no special security\n", " ^^^^^^^^^^^^^^^^^^^^^\n", " ...<4 lines>...\n", " cwd,\n", " ^^^^\n", " startupinfo)\n", " ^^^^^^^^^^^^\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Model Name: \n", "LogisticRegression\n", "Accuracy: 0.7403\n", "Precision: 0.7494\n", "Recall: 0.7403\n", "F1 Score: 0.7435\n", "Classification Report: \n", " precision recall f1-score support\n", "\n", " 0 0.82 0.77 0.79 151\n", " 1 0.61 0.69 0.65 80\n", "\n", " accuracy 0.74 231\n", " macro avg 0.72 0.73 0.72 231\n", "weighted avg 0.75 0.74 0.74 231\n", "\n" ] } ], "source": [ "from sklearn.ensemble import HistGradientBoostingClassifier\n", "\n", "clf = HistGradientBoostingClassifier()\n", "clf.fit(X_train, y_train)\n", "y_pred = clf.predict(X_test)\n", "\n", "\n", "evaluation_results = evaluate_model('LogisticRegression', y_test, y_pred)\n", "for key, value in evaluation_results.items():\n", " if key == 'Class Report':\n", " print(value) \n", " else:\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")\n", " " ] }, { "cell_type": "code", "execution_count": 115, "id": "3cde2032-d397-4304-8bd6-6af8151adff7", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Model Name: \n", "DecisionTreeClassifier\n", "Accuracy: 0.7143\n", "Precision: 0.7310\n", "Recall: 0.7143\n", "F1 Score: 0.7193\n", " precision recall f1-score support\n", "\n", " 0 0.81 0.73 0.77 151\n", " 1 0.57 0.69 0.62 80\n", "\n", " accuracy 0.71 231\n", " macro avg 0.69 0.71 0.70 231\n", "weighted avg 0.73 0.71 0.72 231\n", "\n" ] } ], "source": [ "from sklearn.ensemble import HistGradientBoostingClassifier\n", "\n", "\n", "\n", "DT = DecisionTreeClassifier() \n", "\n", "DT.fit(X_train, y_train)\n", "\n", "y_pred = DT.predict(X_test)\n", "\n", "evaluation_results = evaluate_model('DecisionTreeClassifier', y_test, y_pred)\n", "\n", "for key, value in evaluation_results.items():\n", " if key == 'Classification Report':\n", " print(value) # Print report separately for better readability\n", " else:\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")" ] }, { "cell_type": "code", "execution_count": 125, "id": "8679c930-1104-45e8-bbea-bb342166b837", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Model Name: \n", "Support Vector Machine\n", "Accuracy: 0.7273\n", "Precision: 0.7171\n", "Recall: 0.7273\n", "F1 Score: 0.7155\n", " precision recall f1-score support\n", "\n", " 0 0.76 0.86 0.80 151\n", " 1 0.64 0.47 0.55 80\n", "\n", " accuracy 0.73 231\n", " macro avg 0.70 0.67 0.68 231\n", "weighted avg 0.72 0.73 0.72 231\n", "\n" ] } ], "source": [ "from sklearn.impute import SimpleImputer\n", "from sklearn.svm import SVC\n", "\n", "\n", "imputer = SimpleImputer(strategy='mean')\n", "\n", "X_train_imputed = imputer.fit_transform(X_train)\n", "X_test_imputed = imputer.transform(X_test)\n", "\n", "SVM = SVC()\n", "SVM.fit(X_train_imputed, y_train)\n", "\n", "y_pred = SVM.predict(X_test_imputed)\n", "\n", "evaluation_results = evaluate_model('Support Vector Machine', y_test, y_pred)\n", "for key, value in evaluation_results.items():\n", " if key == 'Classification Report':\n", " print(value)\n", " else:\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 128, "id": "affab043-5b5d-42c8-a56a-2f609425d18a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Model Name: \n", "GaussianNB\n", "Accuracy: 0.7403\n", "Precision: 0.7436\n", "Recall: 0.7403\n", "F1 Score: 0.7417\n", " precision recall f1-score support\n", "\n", " 0 0.81 0.79 0.80 151\n", " 1 0.62 0.65 0.63 80\n", "\n", " accuracy 0.74 231\n", " macro avg 0.71 0.72 0.72 231\n", "weighted avg 0.74 0.74 0.74 231\n", "\n" ] } ], "source": [ "\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.naive_bayes import GaussianNB\n", "\n", "\n", "imputer = SimpleImputer(strategy='mean')\n", "\n", "X_train_imputed = imputer.fit_transform(X_train)\n", "X_test_imputed = imputer.transform(X_test)\n", "\n", "\n", "gnb = GaussianNB()\n", "gnb.fit(X_train_imputed, y_train)\n", "\n", "\n", "y_pred = gnb.predict(X_test_imputed)\n", "\n", "\n", "evaluation_results = evaluate_model('GaussianNB', y_test, y_pred)\n", "for key, value in evaluation_results.items():\n", " if key == 'Classification Report':\n", " print(value)\n", " else:\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")\n" ] }, { "cell_type": "code", "execution_count": 129, "id": "109ea17f-be44-4165-8a35-20b41a590bbd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "sklearn.neighbors._classification.KNeighborsClassifier" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" } ], "source": [ " KNeighborsClassifier\n" ] }, { "cell_type": "code", "execution_count": 138, "id": "7587f227-89fc-46db-84a8-d7d6fbbf7755", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Model Name: \n", "KNeighbors\n", "Accuracy: 0.7013\n", "Precision: 0.6862\n", "Recall: 0.7013\n", "F1 Score: 0.6772\n", " precision recall f1-score support\n", "\n", " 0 0.72 0.88 0.79 151\n", " 1 0.62 0.36 0.46 80\n", "\n", " accuracy 0.70 231\n", " macro avg 0.67 0.62 0.63 231\n", "weighted avg 0.69 0.70 0.68 231\n", "\n" ] } ], "source": [ "\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.pipeline import Pipeline\n", "\n", "\n", "knn_pipeline = Pipeline(steps=[\n", " (\"imputer\", SimpleImputer(strategy=\"mean\")), \n", " (\"knn\", KNeighborsClassifier(n_neighbors=2))\n", "])\n", "\n", "\n", "knn_pipeline.fit(X_train, y_train)\n", "\n", "\n", "y_pred = knn_pipeline.predict(X_test)\n", "\n", "\n", "evaluation_results = evaluate_model('KNeighbors', y_test, y_pred)\n", "\n", "\n", "for key, value in evaluation_results.items():\n", " if key == 'Classification Report':\n", " print(value)\n", " else:\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")\n" ] }, { "cell_type": "code", "execution_count": 140, "id": "1047717e-9f2e-4beb-a420-82e7cdb40784", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from sklearn.svm import SVR\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.tree import DecisionTreeRegressor\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.svm import SVR\n", "from sklearn.ensemble import GradientBoostingRegressor\n", "from sklearn.neighbors import KNeighborsRegressor\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 14, "id": "ca3d6210-604c-4d15-82ee-996281683223", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PregnantGlucoseDiastolic_BPSkin_FoldSerum_InsulinBMIDiabetes_PedigreeAgeClass
06148.072.035.0NaN33.60.627501
1185.066.029.0NaN26.60.351310
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" ], "text/plain": [ " Pregnant Glucose Diastolic_BP Skin_Fold Serum_Insulin BMI \\\n", "0 6 148.0 72.0 35.0 NaN 33.6 \n", "1 1 85.0 66.0 29.0 NaN 26.6 \n", "2 8 183.0 64.0 NaN NaN 23.3 \n", "3 1 89.0 66.0 23.0 94.0 28.1 \n", "4 0 137.0 40.0 35.0 168.0 43.1 \n", "\n", " Diabetes_Pedigree Age Class \n", "0 0.627 50 1 \n", "1 0.351 31 0 \n", "2 0.672 32 1 \n", "3 0.167 21 0 \n", "4 2.288 33 1 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "data = pd.read_csv(\"Diabetes Missing Data (1).csv\")\n", "data.head()\n" ] }, { "cell_type": "code", "execution_count": 143, "id": "355db11d-ab98-4db5-81d3-1ca86df24592", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PregnantGlucoseDiastolic_BPSkin_FoldSerum_InsulinBMIDiabetes_PedigreeAgeClass
count768.000000763.000000733.000000541.000000394.000000757.000000768.000000768.000000768.000000
mean3.845052121.68676372.40518429.153420155.54822332.4574640.47187633.2408850.348958
std3.36957830.53564112.38215810.476982118.7758556.9249880.33132911.7602320.476951
min0.00000044.00000024.0000007.00000014.00000018.2000000.07800021.0000000.000000
25%1.00000099.00000064.00000022.00000076.25000027.5000000.24375024.0000000.000000
50%3.000000117.00000072.00000029.000000125.00000032.3000000.37250029.0000000.000000
75%6.000000141.00000080.00000036.000000190.00000036.6000000.62625041.0000001.000000
max17.000000199.000000122.00000099.000000846.00000067.1000002.42000081.0000001.000000
\n", "
" ], "text/plain": [ " Pregnant Glucose Diastolic_BP Skin_Fold Serum_Insulin \\\n", "count 768.000000 763.000000 733.000000 541.000000 394.000000 \n", "mean 3.845052 121.686763 72.405184 29.153420 155.548223 \n", "std 3.369578 30.535641 12.382158 10.476982 118.775855 \n", "min 0.000000 44.000000 24.000000 7.000000 14.000000 \n", "25% 1.000000 99.000000 64.000000 22.000000 76.250000 \n", "50% 3.000000 117.000000 72.000000 29.000000 125.000000 \n", "75% 6.000000 141.000000 80.000000 36.000000 190.000000 \n", "max 17.000000 199.000000 122.000000 99.000000 846.000000 \n", "\n", " BMI Diabetes_Pedigree Age Class \n", "count 757.000000 768.000000 768.000000 768.000000 \n", "mean 32.457464 0.471876 33.240885 0.348958 \n", "std 6.924988 0.331329 11.760232 0.476951 \n", "min 18.200000 0.078000 21.000000 0.000000 \n", "25% 27.500000 0.243750 24.000000 0.000000 \n", "50% 32.300000 0.372500 29.000000 0.000000 \n", "75% 36.600000 0.626250 41.000000 1.000000 \n", "max 67.100000 2.420000 81.000000 1.000000 " ] }, "execution_count": 143, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe()\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "891d5217-ae20-4d92-8260-aa621c192ead", "metadata": {}, "outputs": [], "source": [ "X = data.drop(['Age','Class'], axis=1)\n", "y = data['Age']" ] }, { "cell_type": "code", "execution_count": 147, "id": "093b7f78-ca36-4ba7-a375-c06557ce4340", "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", "X = scaler.fit_transform(X)" ] }, { "cell_type": "code", "execution_count": 148, "id": "35d3f9ea-e9a1-4615-8edf-7e156a176f0f", "metadata": {}, "outputs": [], "source": [ "\n", "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.25,random_state=32)\n" ] }, { "cell_type": "code", "execution_count": 153, "id": "29aa4884-6a6f-4707-b70a-07bcb8d008f9", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", "def evaluate_model(model_name, y_true, y_pred):\n", " \n", " \n", "\n", " mae = mean_absolute_error(y_true, y_pred)\n", " mse = mean_squared_error(y_true, y_pred)\n", " rmse = np.sqrt(mse)\n", " r2 = r2_score(y_true, y_pred)\n", " \n", " results = {\n", " \"Mean Absolute Error (MAE)\": mae,\n", " \"Mean Squared Error (MSE)\": mse,\n", " \"Root Mean Squared Error (RMSE)\": rmse,\n", " \"R^2 Score\": r2\n", " }\n", " \n", " \n", " plt.figure(figsize=(8, 6))\n", " plt.scatter(y_true, y_pred, alpha=0.6, color='blue', label='Predicted vs Actual')\n", " plt.plot([min(y_true), max(y_true)], [min(y_true), max(y_true)], 'r--', label='')\n", " plt.xlabel('True Values')\n", " plt.ylabel('Predicted Values')\n", " plt.title(f'Regression Model: {model_name} - True vs Predicted Values')\n", " plt.text(0.05, 0.9, f'R^2 Score: {r2:.2f}', transform=plt.gca().transAxes, fontsize=12)\n", " plt.legend()\n", " plt.grid(True)\n", " plt.show()\n", " \n", " return results" ] }, { "cell_type": "code", "execution_count": 157, "id": "0b3266a3-ec8b-4d4e-a40e-06ebfc2972c3", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'Mean Absolute Error (MAE)': 0.29583333333333334,\n", " 'Mean Squared Error (MSE)': 0.15180520833333333,\n", " 'Root Mean Squared Error (RMSE)': np.float64(0.38962187866357473),\n", " 'R^2 Score': 0.3406212796041004}" ] }, "execution_count": 157, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "rf_regressor = RandomForestRegressor(n_estimators=100, random_state=42)\n", "\n", "rf_regressor.fit(X_train, y_train)\n", "\n", "y_pred = rf_regressor.predict(X_test)\n", "\n", "evaluation_results = evaluate_model('RandomForestRegressor', y_test, y_pred)\n", "\n", "evaluation_results" ] }, { "cell_type": "code", "execution_count": 160, "id": "32b61c2d-801e-4318-92ba-f5f797100ca7", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Mean Absolute Error (MAE): 0.2604\n", "Mean Squared Error (MSE): 0.2604\n", "Root Mean Squared Error (RMSE): 0.5103\n", "R^2 Score: -0.1311\n" ] } ], "source": [ "\n", "\n", "DT = DecisionTreeRegressor(random_state=42) \n", "\n", "DT.fit(X_train, y_train)\n", "\n", "y_pred = DT.predict(X_test)\n", "\n", "evaluation_results = evaluate_model('Decision Tree Regressor', y_test, y_pred)\n", "\n", "for key, value in evaluation_results.items():\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")\n" ] }, { "cell_type": "code", "execution_count": 173, "id": "3c4cf837-7593-4c37-831e-b3f6b88de7c5", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Mean Absolute Error (MAE): 0.2947\n", "Mean Squared Error (MSE): 0.1762\n", "Root Mean Squared Error (RMSE): 0.4198\n", "R^2 Score: 0.2345\n" ] } ], "source": [ "from sklearn.pipeline import Pipeline\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.svm import SVR\n", "\n", "svr = Pipeline([\n", " ('imputer', SimpleImputer(strategy='mean')), \n", " ('model', SVR())\n", "])\n", "\n", "svr.fit(X_train, y_train)\n", "\n", "y_pred = svr.predict(X_test)\n", "\n", "evaluation_results = evaluate_model('Support Vector Regressor', y_test, y_pred)\n", "\n", "for key, value in evaluation_results.items():\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")\n" ] }, { "cell_type": "code", "execution_count": 177, "id": "b75c13ae-5aa6-4dd8-816e-ac99fe4a08d3", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Mean Absolute Error (MAE): 0.3077\n", "Mean Squared Error (MSE): 0.1570\n", "Root Mean Squared Error (RMSE): 0.3963\n", "R^2 Score: 0.3179\n" ] } ], "source": [ "from sklearn.pipeline import Pipeline\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.ensemble import GradientBoostingRegressor\n", "\n", "gbr = Pipeline([\n", " ('imputer', SimpleImputer(strategy='mean')), \n", " ('model', GradientBoostingRegressor(random_state=42))\n", "])\n", "\n", "gbr.fit(X_train, y_train)\n", "\n", "y_pred = gbr.predict(X_test)\n", "\n", "evaluation_results = evaluate_model('Gradient Boosting Regressor', y_test, y_pred)\n", "\n", "for key, value in evaluation_results.items():\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")" ] }, { "cell_type": "code", "execution_count": 186, "id": "f690532e-55ef-4439-b0e5-cec9af5a4f78", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Mean Absolute Error (MAE): 0.2958\n", "Mean Squared Error (MSE): 0.1717\n", "Root Mean Squared Error (RMSE): 0.4143\n", "R^2 Score: 0.2544\n" ] } ], "source": [ "from sklearn.impute import SimpleImputer\n", "from sklearn.neighbors import KNeighborsRegressor\n", "from sklearn.pipeline import Pipeline\n", "\n", "knn_pipeline = Pipeline([\n", " ('imputer', SimpleImputer(strategy='mean')), \n", " ('model', KNeighborsRegressor(n_neighbors=5)) \n", "])\n", "\n", "\n", "knn_pipeline.fit(X_train, y_train)\n", "\n", "\n", "y_pred = knn_pipeline.predict(X_test)\n", "\n", "\n", "evaluation_results = evaluate_model('KNeighborsRegressor', y_test, y_pred)\n", "\n", "for key, value in evaluation_results.items():\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")" ] }, { "cell_type": "code", "execution_count": 188, "id": "c817387e-5620-4c53-a71a-b3613d614b12", "metadata": {}, "outputs": [], "source": [ " import pandas as pd\n", " import numpy as np\n", " from sklearn.model_selection import train_test_split, GridSearchCV\n", " from sklearn.preprocessing import StandardScaler, LabelEncoder\n", " from sklearn.linear_model import LogisticRegression\n", " from sklearn.tree import DecisionTreeClassifier\n", " from sklearn.ensemble import RandomForestClassifier\n", " from sklearn.svm import SVC\n", " from sklearn.naive_bayes import GaussianNB\n", " from sklearn.neighbors import KNeighborsClassifier\n", " from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score\n", " import matplotlib.pyplot as plt\n", " import seaborn as sns\n", " from sklearn.model_selection import GridSearchCV\n", " from sklearn.linear_model import LogisticRegression\n", " from sklearn.metrics import classification_report\n", "\n" ] }, { "cell_type": "code", "execution_count": 194, "id": "fd578ad1-4bb5-4129-9686-c91c2dd7b391", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PregnantGlucoseDiastolic_BPSkin_FoldSerum_InsulinBMIDiabetes_PedigreeAgeClass
06148.072.035.0NaN33.60.627501
1185.066.029.0NaN26.60.351310
28183.064.0NaNNaN23.30.672321
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\n", "
" ], "text/plain": [ " Pregnant Glucose Diastolic_BP Skin_Fold Serum_Insulin BMI \\\n", "0 6 148.0 72.0 35.0 NaN 33.6 \n", "1 1 85.0 66.0 29.0 NaN 26.6 \n", "2 8 183.0 64.0 NaN NaN 23.3 \n", "3 1 89.0 66.0 23.0 94.0 28.1 \n", "4 0 137.0 40.0 35.0 168.0 43.1 \n", "\n", " Diabetes_Pedigree Age Class \n", "0 0.627 50 1 \n", "1 0.351 31 0 \n", "2 0.672 32 1 \n", "3 0.167 21 0 \n", "4 2.288 33 1 " ] }, "execution_count": 194, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv('Diabetes Missing Data (1).csv')\n", "data.head()\n" ] }, { "cell_type": "code", "execution_count": 196, "id": "106dc036-b171-4d10-8375-4898320849cb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Class\n", "0 500\n", "1 268\n", "Name: count, dtype: int64" ] }, "execution_count": 196, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Class'].value_counts()" ] }, { "cell_type": "code", "execution_count": 197, "id": "5209a459-ab2c-43b9-b2b7-da96bc88b89e", "metadata": {}, "outputs": [], "source": [ "label_encoders = {}\n", "for column in data.select_dtypes(include=['object']).columns:\n", " le = LabelEncoder()\n", " data[column] = le.fit_transform(data[column])\n", " label_encoders[column] = le" ] }, { "cell_type": "code", "execution_count": 198, "id": "fc49f38b-2e4b-4216-b51c-52bb3be63096", "metadata": {}, "outputs": [], "source": [ "X = data.drop('Class', axis=1).values # Replace 'classification' with you\n", "y = data['Class'].values" ] }, { "cell_type": "code", "execution_count": 202, "id": "1ab473a4-abc5-49aa-a23f-dcaae22af8ea", "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.30, random_state=12)\n" ] }, { "cell_type": "code", "execution_count": 210, "id": "80a8f8f8-88d2-4710-99fd-31779395532b", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.metrics import (\n", " accuracy_score,\n", " precision_score,\n", " recall_score,\n", " f1_score,\n", " confusion_matrix,\n", " classification_report\n", ")\n", "\n", "def evaluate_model(model_name, y_true, y_pred):\n", "\n", " accuracy = accuracy_score(y_true, y_pred)\n", " precision = precision_score(y_true, y_pred, average='weighted')\n", " recall = recall_score(y_true, y_pred, average='weighted')\n", " f1 = f1_score(y_true, y_pred, average='weighted')\n", " cm = confusion_matrix(y_true, y_pred)\n", " report = classification_report(y_true, y_pred)\n", "\n", " \n", " metrics = {\n", " 'Model Name': model_name,\n", " 'Accuracy': accuracy,\n", " 'Precision': precision,\n", " 'Recall': recall,\n", " 'F1 Score': f1,\n", " 'Classification Report': report\n", " }\n", "\n", " \n", " plt.figure(figsize=(4, 4))\n", " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False,\n", " xticklabels=np.unique(y_true), yticklabels=np.unique(y_true))\n", " plt.title(f'Confusion Matrix for {model_name}')\n", " plt.xlabel('Predicted Label')\n", " plt.ylabel('True Label')\n", " plt.show()\n", "\n", " return metrics\n" ] }, { "cell_type": "code", "execution_count": 222, "id": "6c8a0d7e-8d46-4ee9-9294-610263c28b6a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Model Name: \n", "RandomForestClassifier\n", "Accuracy: 0.7835\n", "Precision: 0.7803\n", "Recall: 0.7835\n", "F1 Score: 0.7777\n", " precision recall f1-score support\n", "\n", " 0 0.80 0.88 0.84 147\n", " 1 0.75 0.61 0.67 84\n", "\n", " accuracy 0.78 231\n", " macro avg 0.77 0.75 0.75 231\n", "weighted avg 0.78 0.78 0.78 231\n", "\n", "\n", "Best hyperparameters found by GridSearchCV:\n", "{'max_depth': 10, 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 100}\n" ] } ], "source": [ "\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "rf_classifier = RandomForestClassifier(random_state=42)\n", "\n", "\n", "param_grid = {\n", " 'n_estimators': [100, 200, 300],\n", " 'max_depth': [None, 10, 20, 30],\n", " 'min_samples_split': [2, 5, 10],\n", " 'min_samples_leaf': [1, 2, 4]\n", "}\n", "\n", "\n", "grid_search = GridSearchCV(estimator=rf_classifier,param_grid=param_grid,cv=5,scoring='accuracy',n_jobs=-1)\n", "\n", "grid_search.fit(X_train, y_train)\n", "\n", "best_rf_classifier = grid_search.best_estimator_\n", "\n", "y_pred = best_rf_classifier.predict(X_test)\n", "\n", "\n", "evaluation_results = evaluate_model('RandomForestClassifier', y_test, y_pred)\n", "\n", "\n", "for key, value in evaluation_results.items():\n", " if key == 'Classification Report':\n", " print(value) \n", " else:\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")\n", "\n", "\n", "print(\"\\nBest hyperparameters found by GridSearchCV:\")\n", "print(grid_search.best_params_)\n" ] }, { "cell_type": "code", "execution_count": 228, "id": "f25f6993-e8f8-4891-a017-b12bfc902974", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\alaam\\anaconda3\\envs\\Alaa\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Model Name: \n", "LogisticRegression (with Imputer)\n", "Accuracy: 0.7792\n", "Precision: 0.7780\n", "Recall: 0.7792\n", "F1 Score: 0.7697\n", " precision recall f1-score support\n", "\n", " 0 0.78 0.90 0.84 147\n", " 1 0.77 0.56 0.65 84\n", "\n", " accuracy 0.78 231\n", " macro avg 0.78 0.73 0.74 231\n", "weighted avg 0.78 0.78 0.77 231\n", "\n", "\n", "Best hyperparameters found by GridSearchCV:\n", "{'classifier__C': 1, 'classifier__max_iter': 100, 'classifier__solver': 'lbfgs'}\n" ] } ], "source": [ "from sklearn.impute import SimpleImputer\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "\n", "\n", "pipeline = Pipeline([\n", " ('imputer', SimpleImputer(strategy='mean')), # ممكن تختاري median أو most_frequent حسب نوع البيانات\n", " ('classifier', LogisticRegression(random_state=42))\n", "])\n", "\n", "\n", "param_grid = {\n", " 'classifier__C': [0.01, 0.1, 1, 10],\n", " 'classifier__solver': ['liblinear', 'lbfgs'],\n", " 'classifier__max_iter': [100, 200, 300]\n", "}\n", "\n", "grid_search = GridSearchCV(\n", " estimator=pipeline,\n", " param_grid=param_grid,\n", " cv=5,\n", " scoring='accuracy',\n", " n_jobs=-1\n", ")\n", "\n", "\n", "grid_search.fit(X_train, y_train)\n", "\n", "best_model = grid_search.best_estimator_\n", "\n", "y_pred = best_model.predict(X_test)\n", "\n", "evaluation_results = evaluate_model('LogisticRegression (with Imputer)', y_test, y_pred)\n", "\n", "for key, value in evaluation_results.items():\n", " if key == 'Classification Report':\n", " print(value)\n", " else:\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")\n", "\n", "print(\"\\nBest hyperparameters found by GridSearchCV:\")\n", "print(grid_search.best_params_)\n" ] }, { "cell_type": "code", "execution_count": 229, "id": "e7f28e79-8376-490c-a2f3-48bf34519b0e", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Model Name: \n", "DecisionTreeClassifier\n", "Accuracy: 0.7056\n", "Precision: 0.6985\n", "Recall: 0.7056\n", "F1 Score: 0.7001\n", " precision recall f1-score support\n", "\n", " 0 0.75 0.81 0.78 147\n", " 1 0.61 0.52 0.56 84\n", "\n", " accuracy 0.71 231\n", " macro avg 0.68 0.67 0.67 231\n", "weighted avg 0.70 0.71 0.70 231\n", "\n", "\n", "Best hyperparameters found by GridSearchCV:\n", "{'criterion': 'gini', 'max_depth': 10, 'max_features': None, 'min_samples_leaf': 1, 'min_samples_split': 10}\n" ] } ], "source": [ "\n", "\n", "\n", "DT = DecisionTreeClassifier(random_state=42)\n", "\n", "\n", "param_grid = {\n", " 'criterion': ['gini', 'entropy'], # The function to measure the qual\n", " 'max_depth': [None, 10, 20, 30], # The maximum depth of the tree\n", " 'min_samples_split': [2, 5, 10], # The minimum number of samples r\n", " 'min_samples_leaf': [1, 2, 4], # The minimum number of samples r\n", " 'max_features': [None, 'sqrt', 'log2'] # The number of features to consid\n", " }\n", "\n", "grid_search = GridSearchCV(estimator=DT, param_grid=param_grid, cv=5, scoring='accuracy',n_jobs=1)\n", "\n", "grid_search.fit(X_train, y_train)\n", "\n", "best_DT = grid_search.best_estimator_\n", "\n", "\n", "y_pred = best_DT.predict(X_test)\n", "\n", "evaluation_results = evaluate_model('DecisionTreeClassifier', y_test, y_pred)\n", "\n", "for key, value in evaluation_results.items():\n", " if key == 'Classification Report':\n", " print(value) \n", " else:\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")\n", "\n", " \n", "print(\"\\nBest hyperparameters found by GridSearchCV:\")\n", "print(grid_search.best_params_)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "5ac3ab3a-d798-4764-9986-185014079e63", "metadata": {}, "outputs": [], "source": [ "\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.svm import SVC\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "\n", "pipeline = Pipeline([\n", " ('imputer', SimpleImputer(strategy='mean')), \n", " ('classifier', SVC(random_state=42))\n", "])\n", "\n", "\n", "param_grid = {\n", " 'classifier__C': [0.01, 0.1, 1, 10], \n", " 'classifier__kernel': ['linear', 'rbf', 'poly'], \n", " 'classifier__gamma': ['scale', 'auto'], \n", " 'classifier__degree': [3, 4, 5], \n", " 'classifier__class_weight': [None, 'balanced'] \n", "}\n", "\n", "\n", "grid_search = GridSearchCV(\n", " estimator=pipeline,\n", " param_grid=param_grid,\n", " cv=5,\n", " scoring='accuracy',\n", " n_jobs=-1\n", ")\n", "\n", "\n", "grid_search.fit(X_train, y_train)\n", "\n", "\n", "best_SVM = grid_search.best_estimator_\n", "\n", "\n", "y_pred = best_SVM.predict(X_test)\n", "\n", "\n", "evaluation_results = evaluate_model('Support Vector Machine', y_test, y_pred)\n", "\n", "for key, value in evaluation_results.items():\n", " if key == 'Classification Report':\n", " print(value)\n", " else:\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")\n", "\n", "print(\"\\nBest hyperparameters found by GridSearchCV:\")\n", "print(grid_search.best_params_)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "cd631a72-8356-4d4f-accb-5a0d4f83a7ae", "metadata": {}, "outputs": [], "source": [ "\n", "\n", "\n", "\n", "gnb = GaussianNB()\n", "param_grid = {\n", " 'var_smoothing': [1e-9, 1e-8, 1e-7, 1e-6] # Smoothing parameter\n", "\n", "}\n", "grid_search = GridSearchCV(\n", " estimator=gnb, \n", " param_grid=param_grid, \n", " cv=5, \n", " scoring= 'accuracy',\n", " n_jobs=-1 )\n", "\n", "grid_search.fit(X_train, y_train)\n", "\n", "best_gnb = grid_search.best_estimator_\n", "\n", "y_pred = best_gnb.predict(X_test)\n", "\n", "evaluation_results = evaluate_model('Gaussian Naive Bayes', y_test, y_pred)\n", "\n", "for key, value in evaluation_results.items():\n", " if key == 'Classification Report':\n", " print(value) \n", " else:\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")\n", "\n", "print(\"\\nBest hyperparameters found by GridSearchCV:\")\n", "print(grid_search.best_params_)" ] }, { "cell_type": "code", "execution_count": null, "id": "4696702d-f6b3-43a9-abc9-61abf48db0bc", "metadata": {}, "outputs": [], "source": [ "from sklearn.pipeline import Pipeline\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "\n", "knn_pipeline = Pipeline([\n", " ('imputer', SimpleImputer(strategy='mean')), \n", " ('classifier', KNeighborsClassifier()) \n", "])\n", "\n", "\n", "param_grid = {\n", " 'classifier__n_neighbors': [1, 3, 5, 7, 9],\n", " 'classifier__weights': ['uniform', 'distance'],\n", " 'classifier__metric': ['euclidean', 'manhattan'],\n", " 'classifier__algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']\n", "}\n", "\n", "\n", "grid_search = GridSearchCV(\n", " estimator=knn_pipeline,\n", " param_grid=param_grid,\n", " cv=5,\n", " scoring='accuracy',\n", " n_jobs=-1\n", ")\n", "\n", "\n", "grid_search.fit(X_train, y_train)\n", "\n", "best_knn = grid_search.best_estimator_\n", "\n", "y_pred = best_knn.predict(X_test)\n", "\n", "evaluation_results = evaluate_model('K-Nearest Neighbors', y_test, y_pred)\n", "\n", "for key, value in evaluation_results.items():\n", " if key == 'Classification Report':\n", " print(value)\n", " else:\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")\n", "\n", "print(\"\\nBest hyperparameters found by GridSearchCV:\")\n", "print(grid_search.best_params_)\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "8d76f8f0-897e-4dd7-9d63-9ebd292bc2e6", "metadata": {}, "outputs": [], "source": [ " import pandas as pd\n", " import numpy as np\n", " from sklearn.linear_model import LinearRegression\n", " from sklearn.tree import DecisionTreeRegressor\n", " from sklearn.ensemble import RandomForestRegressor\n", " from sklearn.svm import SVR\n", " from sklearn.ensemble import GradientBoostingRegressor\n", " from sklearn.neighbors import KNeighborsRegressor\n", " import seaborn as sns\n", " from sklearn.ensemble import RandomForestRegressor\n", " from sklearn.model_selection import GridSearchCV, train_test_split\n", " from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n" ] }, { "cell_type": "code", "execution_count": 18, "id": "6bbe25c2-ca0e-4d70-9549-31e7587a5728", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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40137.040.035.0168.043.12.288331
\n", "
" ], "text/plain": [ " Pregnant Glucose Diastolic_BP Skin_Fold Serum_Insulin BMI \\\n", "0 6 148.0 72.0 35.0 NaN 33.6 \n", "1 1 85.0 66.0 29.0 NaN 26.6 \n", "2 8 183.0 64.0 NaN NaN 23.3 \n", "3 1 89.0 66.0 23.0 94.0 28.1 \n", "4 0 137.0 40.0 35.0 168.0 43.1 \n", "\n", " Diabetes_Pedigree Age Class \n", "0 0.627 50 1 \n", "1 0.351 31 0 \n", "2 0.672 32 1 \n", "3 0.167 21 0 \n", "4 2.288 33 1 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(\"Diabetes Missing Data (1).csv\")\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 19, "id": "9e59d6cb-51b6-45d9-b41a-e7178b618bae", "metadata": {}, "outputs": [], "source": [ "X = data.drop(['Age','Class'], axis=1)\n", "y = data['Age']" ] }, { "cell_type": "code", "execution_count": 20, "id": "a1e9977b-0132-459c-b09c-982c01c4e363", "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", "X = scaler.fit_transform(X)" ] }, { "cell_type": "code", "execution_count": 21, "id": "d5c9ab3d-79ad-4673-99c1-b774eb3540e4", "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.25, random_state=42)" ] }, { "cell_type": "code", "execution_count": 28, "id": "8899d113-341d-4aac-bba7-2896229cd832", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", "\n", "def evaluate_model(model_name, y_true, y_pred):\n", " \n", " mae = mean_absolute_error(y_true, y_pred)\n", " mse = mean_squared_error(y_true, y_pred)\n", " rmse = np.sqrt(mse)\n", " r2 = r2_score(y_true, y_pred)\n", "\n", " \n", " results = {\n", " \"Model\": model_name,\n", " \"Mean Absolute Error (MAE)\": mae,\n", " \"Mean Squared Error (MSE)\": mse,\n", " \"Root Mean Squared Error (RMSE)\": rmse,\n", " \"R^2 Score\": r2\n", " }\n", "\n", " return results\n" ] }, { "cell_type": "code", "execution_count": 31, "id": "8be41b69-b80f-497d-b422-1f164a73b04f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 108 candidates, totalling 540 fits\n", "Best Parameters: {'max_depth': 20, 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 100}\n", "Best CV RMSE: -8.862009995321378\n", "\n", "Evaluation Results on Test Set:\n", "Model:RandomForestRegressor\n", "Mean Absolute Error (MAE): 6.8020\n", "Mean Squared Error (MSE): 93.9262\n", "Root Mean Squared Error (RMSE): 9.6916\n", "R^2 Score: 0.3928\n" ] } ], "source": [ "\n", "rf_regressor = RandomForestRegressor()\n", "\n", "param_grid = {\n", " 'n_estimators': [100, 200, 300],\n", " 'max_depth': [None, 10, 20, 30],\n", " 'min_samples_split': [2, 5, 10],\n", " 'min_samples_leaf': [1, 2, 4],}\n", "\n", "\n", "grid_search = GridSearchCV(\n", " estimator=rf_regressor,\n", " param_grid=param_grid,\n", " cv=5, \n", " scoring='neg_root_mean_squared_error', \n", " verbose=2,\n", " n_jobs=-1 \n", " )\n", "\n", "grid_search.fit(X_train, y_train)\n", "\n", "print(\"Best Parameters:\", grid_search.best_params_)\n", "print(\"Best CV RMSE:\", grid_search.best_score_)\n", "\n", "best_rf = grid_search.best_estimator_\n", "\n", "y_pred = best_rf.predict(X_test)\n", "evaluation_results = evaluate_model('RandomForestRegressor', y_test, y_pred)\n", "\n", " \n", "print(\"\\nEvaluation Results on Test Set:\")\n", "for key, value in evaluation_results.items():\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}:{value}\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 33, "id": "b4a61676-0fd0-4e11-aad2-c5d1ff938a35", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n", "Best Parameters: {'max_depth': 10, 'min_samples_leaf': 4, 'min_samples_split': 10}\n", "Best CV RMSE: -10.713869017110008\n", "\n", "Evaluation Results on Test Set:\n", "Model:Decision Tree Regressor\n", "Mean Absolute Error (MAE): 8.5255\n", "Mean Squared Error (MSE): 142.1751\n", "Root Mean Squared Error (RMSE): 11.9237\n", "R^2 Score: 0.0809\n" ] } ], "source": [ "\n", "\n", "DT = DecisionTreeRegressor(random_state=42)\n", " \n", "param_grid = {\n", " 'max_depth': [None, 10, 20, 30],\n", " 'min_samples_split': [2, 5, 10],\n", " 'min_samples_leaf': [1, 2, 4],\n", " }\n", " \n", "grid_search = GridSearchCV(\n", " estimator=DT,\n", " param_grid=param_grid,\n", " cv=5, \n", " scoring='neg_root_mean_squared_error', \n", " verbose=2,\n", " n_jobs=-1 \n", " )\n", " \n", "grid_search.fit(X_train, y_train)\n", " \n", "print(\"Best Parameters:\", grid_search.best_params_)\n", "print(\"Best CV RMSE:\", grid_search.best_score_)\n", " \n", "best_DT = grid_search.best_estimator_\n", " \n", "y_pred = best_DT.predict(X_test)\n", " \n", "evaluation_results = evaluate_model('Decision Tree Regressor', y_test, y_pred)\n", "\n", "print(\"\\nEvaluation Results on Test Set:\")\n", " \n", "for key, value in evaluation_results.items():\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}:{value}\")" ] }, { "cell_type": "code", "execution_count": 36, "id": "e7c572db-0721-40b1-b960-90ac63e34a0d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 128 candidates, totalling 640 fits\n", "Best Parameters: {'model__C': 10, 'model__epsilon': 1, 'model__gamma': 'auto', 'model__kernel': 'rbf'}\n", "Best CV RMSE Score: -9.055753087446103\n", "\n", "Evaluation Results on Test Set:\n", "Model: Support Vector Regressor\n", "Mean Absolute Error (MAE): 6.6195\n", "Mean Squared Error (MSE): 104.6086\n", "Root Mean Squared Error (RMSE): 10.2278\n", "R^2 Score: 0.3238\n" ] } ], "source": [ "from sklearn.pipeline import Pipeline\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.svm import SVR\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "# Pipeline مع المعالجة المسبقة والنموذج\n", "svr_pipeline = Pipeline([\n", " ('imputer', SimpleImputer(strategy='mean')), \n", " ('model', SVR())\n", "])\n", "\n", "# شبكة القيم الممكنة\n", "param_grid = {\n", " 'model__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n", " 'model__C': [0.1, 1, 10, 100],\n", " 'model__epsilon': [0.1, 0.2, 0.5, 1],\n", " 'model__gamma': ['scale', 'auto']\n", "}\n", "\n", "# Grid Search\n", "grid_search = GridSearchCV(\n", " estimator=svr_pipeline,\n", " param_grid=param_grid,\n", " cv=5, \n", " scoring='neg_root_mean_squared_error', \n", " verbose=2,\n", " n_jobs=-1 \n", ")\n", "\n", "grid_search.fit(X_train, y_train)\n", "\n", "print(\"Best Parameters:\", grid_search.best_params_)\n", "print(\"Best CV RMSE Score:\", grid_search.best_score_)\n", "\n", "best_SVM = grid_search.best_estimator_\n", "\n", "# التوقع والتقييم\n", "y_pred = best_SVM.predict(X_test)\n", "evaluation_results = evaluate_model('Support Vector Regressor', y_test, y_pred)\n", "\n", "print(\"\\nEvaluation Results on Test Set:\")\n", "for key, value in evaluation_results.items():\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: {value}\")\n" ] }, { "cell_type": "code", "execution_count": 38, "id": "5e795840-d4b7-4d81-9896-2d1e019b7c15", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 243 candidates, totalling 1215 fits\n", "Best Parameters: {'model__learning_rate': 0.01, 'model__max_depth': 3, 'model__min_samples_leaf': 4, 'model__min_samples_split': 2, 'model__n_estimators': 300}\n", "Best CV RMSE Score: -8.946669592495825\n", "\n", "Evaluation Results on Test Set:\n", "Model: Gradient Boosting Regressor\n", "Mean Absolute Error (MAE): 6.9007\n", "Mean Squared Error (MSE): 95.8485\n", "Root Mean Squared Error (RMSE): 9.7902\n", "R^2 Score: 0.3804\n" ] } ], "source": [ "\n", "\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.ensemble import GradientBoostingRegressor\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "\n", "gbr_pipeline = Pipeline([\n", " ('imputer', SimpleImputer(strategy='mean')), \n", " ('model', GradientBoostingRegressor(random_state=42))\n", "])\n", "\n", "\n", "param_grid = {\n", " 'model__n_estimators': [100, 200, 300],\n", " 'model__learning_rate': [0.01, 0.1, 0.2],\n", " 'model__max_depth': [3, 5, 7],\n", " 'model__min_samples_split': [2, 5, 10],\n", " 'model__min_samples_leaf': [1, 2, 4],\n", "}\n", "\n", "\n", "grid_search = GridSearchCV(\n", " estimator=gbr_pipeline,\n", " param_grid=param_grid,\n", " cv=5,\n", " scoring='neg_root_mean_squared_error',\n", " verbose=2,\n", " n_jobs=-1\n", ")\n", "\n", "grid_search.fit(X_train, y_train)\n", "\n", "print(\"Best Parameters:\", grid_search.best_params_)\n", "print(\"Best CV RMSE Score:\", grid_search.best_score_)\n", "\n", "best_GBR = grid_search.best_estimator_\n", "\n", "\n", "y_pred = best_GBR.predict(X_test)\n", "\n", "evaluation_results = evaluate_model('Gradient Boosting Regressor', y_test, y_pred)\n", "print(\"\\nEvaluation Results on Test Set:\")\n", "for key, value in evaluation_results.items():\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: {value}\")\n" ] }, { "cell_type": "code", "execution_count": 41, "id": "8982a678-4d9b-49e2-af3f-0543cce00c80", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n", "Best Parameters: {'model__metric': 'euclidean', 'model__n_neighbors': 10, 'model__weights': 'distance'}\n", "Best CV RMSE Score: -8.958207906532385\n", "\n", "Evaluation Results on Test Set:\n", "Model: KNeighborsRegressor\n", "Mean Absolute Error (MAE): 7.3632\n", "Mean Squared Error (MSE): 108.4766\n", "Root Mean Squared Error (RMSE): 10.4152\n", "R^2 Score: 0.2987\n" ] } ], "source": [ "\n", "\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.neighbors import KNeighborsRegressor\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "\n", "knn_pipeline = Pipeline([\n", " ('imputer', SimpleImputer(strategy='mean')), \n", " ('model', KNeighborsRegressor())\n", "])\n", "\n", "\n", "param_grid = {\n", " 'model__n_neighbors': [3, 5, 7, 10],\n", " 'model__weights': ['uniform', 'distance'],\n", " 'model__metric': ['euclidean', 'manhattan', 'minkowski'],\n", "}\n", "\n", "\n", "grid_search = GridSearchCV(\n", " estimator=knn_pipeline,\n", " param_grid=param_grid,\n", " cv=5,\n", " scoring='neg_root_mean_squared_error',\n", " verbose=2,\n", " n_jobs=-1\n", ")\n", "\n", "grid_search.fit(X_train, y_train)\n", "\n", "print(\"Best Parameters:\", grid_search.best_params_)\n", "print(\"Best CV RMSE Score:\", grid_search.best_score_)\n", "\n", "best_knn = grid_search.best_estimator_\n", "y_pred = best_knn.predict(X_test)\n", "\n", "evaluation_results = evaluate_model('KNeighborsRegressor', y_test, y_pred)\n", "print(\"\\nEvaluation Results on Test Set:\")\n", "for key, value in evaluation_results.items():\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: {value}\")\n" ] }, { "cell_type": "code", "execution_count": 43, "id": "6b2596f3-55c6-452f-a9c6-1db29ee52f2d", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from sklearn.datasets import load_iris\n", "from sklearn.feature_selection import SelectKBest, chi2, RFE\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.model_selection import train_test_split, GridSearchCV\n", "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.metrics import (\n", " confusion_matrix,\n", " accuracy_score,\n", " precision_score,\n", " recall_score,\n", " f1_score,\n", " classification_report\n", " )\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n" ] }, { "cell_type": "code", "execution_count": 44, "id": "c9701938-361e-4747-bba1-b05644d1b7e0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PregnantGlucoseDiastolic_BPSkin_FoldSerum_InsulinBMIDiabetes_PedigreeAgeClass
06148.072.035.0NaN33.60.627501
1185.066.029.0NaN26.60.351310
28183.064.0NaNNaN23.30.672321
3189.066.023.094.028.10.167210
40137.040.035.0168.043.12.288331
\n", "
" ], "text/plain": [ " Pregnant Glucose Diastolic_BP Skin_Fold Serum_Insulin BMI \\\n", "0 6 148.0 72.0 35.0 NaN 33.6 \n", "1 1 85.0 66.0 29.0 NaN 26.6 \n", "2 8 183.0 64.0 NaN NaN 23.3 \n", "3 1 89.0 66.0 23.0 94.0 28.1 \n", "4 0 137.0 40.0 35.0 168.0 43.1 \n", "\n", " Diabetes_Pedigree Age Class \n", "0 0.627 50 1 \n", "1 0.351 31 0 \n", "2 0.672 32 1 \n", "3 0.167 21 0 \n", "4 2.288 33 1 " ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv('Diabetes Missing Data (1).csv')\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 45, "id": "1d3c5dd2-b317-4037-a846-19f408d6eae5", "metadata": {}, "outputs": [], "source": [ "X = data.drop('Class', axis=1)\n", "y = data['Class']" ] }, { "cell_type": "code", "execution_count": 46, "id": "fae1367f-ff16-47ef-8b56-0bb7fd61deb4", "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, \n", "test_size=0.2, random_state=42)" ] }, { "cell_type": "code", "execution_count": 48, "id": "80250290-6a5e-4bef-bee1-490219da4b6e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Feature Score\n", "4 Serum_Insulin 1227.250623\n", "1 Glucose 1090.456123\n", "7 Age 194.164018\n", "5 BMI 92.792413\n", "0 Pregnant 77.452968\n", "3 Skin_Fold 67.137484\n", "2 Diastolic_BP 31.708800\n", "6 Diabetes_Pedigree 3.541524\n" ] } ], "source": [ "\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.feature_selection import SelectKBest, chi2\n", "import pandas as pd\n", "\n", "imputer = SimpleImputer(strategy='mean')\n", "X_train_imputed = imputer.fit_transform(X_train)\n", "\n", "select_feature = SelectKBest(chi2, k=8).fit(X_train_imputed, y_train)\n", "\n", "\n", "feature_scores = pd.DataFrame({\n", " 'Feature': X.columns,\n", " 'Score': select_feature.scores_\n", "})\n", "\n", "print(feature_scores.sort_values(by=\"Score\", ascending=False))\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "2212834d-cc31-4032-9538-de9b6ce2b73b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Evaluation Results on Test Set:\n", "Model Name: RandomForestClassifier\n", "Accuracy: 0.9561\n", "Precision: 0.9561\n", "Recall: 0.9561\n", "F1 Score: 0.9560\n", "\n", "Best hyperparameters found by GridSearchCV:\n", "{'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n" ] } ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.model_selection import GridSearchCV, train_test_split\n", "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n", "from sklearn.feature_selection import SelectKBest, f_classif\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.datasets import load_breast_cancer # مثال على بيانات جاهزة\n", "\n", "\n", "data = load_breast_cancer()\n", "X = data.data\n", "y = data.target\n", "\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", "\n", "\n", "imputer = SimpleImputer(strategy='mean')\n", "X_train_imputed = imputer.fit_transform(X_train)\n", "X_test_imputed = imputer.transform(X_test)\n", "\n", "\n", "selector = SelectKBest(score_func=f_classif, k=8)\n", "X_train_selected = selector.fit_transform(X_train_imputed, y_train)\n", "X_test_selected = selector.transform(X_test_imputed)\n", "\n", "\n", "def evaluate_model(model_name, y_true, y_pred):\n", " accuracy = accuracy_score(y_true, y_pred)\n", " precision = precision_score(y_true, y_pred, average='weighted')\n", " recall = recall_score(y_true, y_pred, average='weighted')\n", " f1 = f1_score(y_true, y_pred, average='weighted')\n", " return {\n", " 'Model Name': model_name,\n", " 'Accuracy': accuracy,\n", " 'Precision': precision,\n", " 'Recall': recall,\n", " 'F1 Score': f1,\n", " }\n", "\n", "\n", "rf_classifier = RandomForestClassifier(random_state=42)\n", "\n", "param_grid = {\n", " 'n_estimators': [100, 200],\n", " 'max_depth': [None, 10],\n", " 'min_samples_split': [2, 5],\n", " 'min_samples_leaf': [1, 2]\n", "}\n", "\n", "# GridSearchCV\n", "grid_search = GridSearchCV(\n", " estimator=rf_classifier,\n", " param_grid=param_grid,\n", " cv=5,\n", " scoring='accuracy',\n", " n_jobs=-1\n", ")\n", "\n", "\n", "grid_search.fit(X_train_selected, y_train)\n", "\n", "best_rf_classifier = grid_search.best_estimator_\n", "\n", "y_pred = best_rf_classifier.predict(X_test_selected)\n", "\n", "\n", "evaluation_results = evaluate_model('RandomForestClassifier', y_test, y_pred)\n", "\n", "\n", "print(\"\\nEvaluation Results on Test Set:\")\n", "for key, value in evaluation_results.items():\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: {value}\")\n", "\n", "\n", "print(\"\\nBest hyperparameters found by GridSearchCV:\")\n", "print(grid_search.best_params_)" ] }, { "cell_type": "code", "execution_count": 17, "id": "4c628ee7-329e-46b3-9a7d-a5670bc64f08", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Evaluation Results on Test Set:\n", "Model Name: RandomForestClassifier\n", "Accuracy: 0.9649\n", "Precision: 0.9652\n", "Recall: 0.9649\n", "F1 Score: 0.9647\n", "\n", "Best hyperparameters found by GridSearchCV:\n", "{'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n" ] } ], "source": [ "\n", "\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.model_selection import GridSearchCV, train_test_split\n", "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n", "from sklearn.datasets import load_breast_cancer \n", "\n", "\n", "data = load_breast_cancer()\n", "X = data.data\n", "y = data.target\n", "\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", "\n", "rf_classifier = RandomForestClassifier(random_state=42)\n", "\n", "\n", "param_grid = {\n", " 'n_estimators': [100, 200, 300],\n", " 'max_depth': [None, 10, 20, 30],\n", " 'min_samples_split': [2, 5, 10],\n", " 'min_samples_leaf': [1, 2, 4]\n", "}\n", "\n", "# GridSearchCV\n", "grid_search = GridSearchCV(\n", " estimator=rf_classifier,\n", " param_grid=param_grid,\n", " cv=5,\n", " scoring='accuracy',\n", " n_jobs=-1\n", ")\n", "\n", "\n", "grid_search.fit(X_train, y_train)\n", "\n", "\n", "best_rf_classifier = grid_search.best_estimator_\n", "\n", "\n", "y_pred = best_rf_classifier.predict(X_test)\n", "\n", "\n", "def evaluate_model(model_name, y_true, y_pred):\n", " return {\n", " 'Model Name': model_name,\n", " 'Accuracy': accuracy_score(y_true, y_pred),\n", " 'Precision': precision_score(y_true, y_pred, average='weighted'),\n", " 'Recall': recall_score(y_true, y_pred, average='weighted'),\n", " 'F1 Score': f1_score(y_true, y_pred, average='weighted'),\n", " }\n", "\n", "\n", "evaluation_results = evaluate_model('RandomForestClassifier', y_test, y_pred)\n", "\n", "\n", "print(\"\\nEvaluation Results on Test Set:\")\n", "for key, value in evaluation_results.items():\n", " print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: {value}\")\n", "\n", "\n", "print(\"\\nBest hyperparameters found by GridSearchCV:\")\n", "print(grid_search.best_params_)" ] }, { "cell_type": "code", "execution_count": 21, "id": "9c27b856-8a49-43e9-ba8d-b2b7e0daf39b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Selected Features (RFE): [ 2 6 7 20 21 22 23 27]\n" ] } ], "source": [ "\n", "\n", "from sklearn.feature_selection import RFE\n", "\n", "\n", "clf_rf_2 = RandomForestClassifier(random_state=43) \n", "\n", "rfe_selector = RFE(estimator=clf_rf_2, n_features_to_select=8)\n", "\n", "X_train_selected_rfe = rfe_selector.fit_transform(X_train, y_train)\n", " \n", "X_test_selected_rfe = rfe_selector.transform(X_test)\n", "\n", "# Get the selected feature indices\n", "\n", "selected_features = rfe_selector.get_support(indices=True)\n", "\n", "print(\"Selected Features (RFE):\", selected_features)" ] }, { "cell_type": "code", "execution_count": 33, "id": "0f6b4871-c348-490f-90a7-23320c7212f4", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'numpy.ndarray' object has no attribute 'columns'", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[33]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mensemble\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m RandomForestClassifier\n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m# نحفظ أسماء الأعمدة\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m feature_names = X_train.columns \n\u001b[32m 7\u001b[39m \u001b[38;5;66;03m# تطبيق RFE\u001b[39;00m\n\u001b[32m 8\u001b[39m selector = RFE(estimator=RandomForestClassifier(random_state=\u001b[32m42\u001b[39m), n_features_to_select=\u001b[32m8\u001b[39m)\n", "\u001b[31mAttributeError\u001b[39m: 'numpy.ndarray' object has no attribute 'columns'" ] } ], "source": [ "from sklearn.feature_selection import RFE\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "\n", "feature_names = X_train.columns \n", "\n", "\n", "selector = RFE(estimator=RandomForestClassifier(random_state=42), n_features_to_select=8)\n", "X_train_selected_rfe = selector.fit_transform(X_train, y_train)\n", "X_test_selected_rfe = selector.transform(X_test)\n", "\n", "\n", "selected_features = feature_names[selector.support_]\n", "print(\"✅ Selected features by RFE:\", selected_features.tolist())\n", "\n", "\n", "rf_classifier = RandomForestClassifier(random_state=42)\n", "\n", "param_grid = {\n", " 'n_estimators': [100, 200, 300],\n", " 'max_depth': [None, 10, 20, 30],\n", " 'min_samples_split': [2, 5, 10],\n", " 'min_samples_leaf': [1, 2, 4]\n", "}\n", "\n", "grid_search = GridSearchCV(\n", " estimator=rf_classifier,\n", " param_grid=param_grid,\n", " cv=5,\n", " scoring='accuracy',\n", " n_jobs=-1\n", ")\n", "\n", "grid_search.fit(X_train_selected_rfe, y_train)\n", "\n", "best_rf_classifier = grid_search.best_estimator_\n", "\n", "y_pred = best_rf_classifier.predict(X_test_selected_rfe)\n", "\n", "\n", "evaluation_results = evaluate_model('RandomForestClassifier', y_test, y_pred)\n", "\n", "print(\"\\nEvaluation Results on Test Set:\")\n", "for key, value in evaluation_results.items():\n", " if isinstance(value, float):\n", " print(f\"{key}: {value:.4f}\")\n", " else:\n", " print(f\"{key}: {value}\")\n", "\n", "print(\"\\nBest hyperparameters found by GridSearchCV:\")\n", "print(grid_search.best_params_)\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "3f50cbdf-1050-4f98-9d6d-984bc453bbd3", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'numpy.ndarray' object has no attribute 'columns'", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[22]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m 6\u001b[39m X_train_selected_rfe = selector.fit_transform(X_train, y_train)\n\u001b[32m 7\u001b[39m X_test_selected_rfe = selector.transform(X_test)\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m selected_features = X.columns[selector.support_]\n\u001b[32m 11\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m Selected features by RFE:\u001b[39m\u001b[33m\"\u001b[39m, selected_features.tolist())\n\u001b[32m 16\u001b[39m rf_classifier = RandomForestClassifier(random_state=\u001b[32m42\u001b[39m)\n", "\u001b[31mAttributeError\u001b[39m: 'numpy.ndarray' object has no attribute 'columns'" ] } ], "source": [ "\n", "\n", "\n", "from sklearn.feature_selection import RFE\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "\n", "selector = RFE(estimator=RandomForestClassifier(random_state=42), n_features_to_select=8)\n", "X_train_selected_rfe = selector.fit_transform(X_train, y_train)\n", "X_test_selected_rfe = selector.transform(X_test)\n", "\n", "\n", "selected_features = X.columns[selector.support_]\n", "print(\" Selected features by RFE:\", selected_features.tolist())\n", "\n", "\n", "\n", "\n", "rf_classifier = RandomForestClassifier(random_state=42)\n", "\n", "param_grid = {\n", " 'n_estimators': [100, 200, 300],\n", " 'max_depth': [None, 10, 20, 30],\n", " 'min_samples_split': [2, 5, 10],\n", " 'min_samples_leaf': [1, 2, 4]\n", " }\n", "\n", "\n", "grid_search = GridSearchCV(estimator=rf_classifier, \n", "param_grid=param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n", "\n", "\n", "grid_search.fit(X_train_selected_rfe, y_train)\n", "\n", "\n", "best_rf_classifier = grid_search.best_estimator_\n", "\n", "\n", "y_pred = best_rf_classifier.predict(X_test_selected_rfe)\n", "\n", "\n", "evaluation_results = evaluate_model('RandomForestClassifier', y_test, \n", "y_pred)\n", "\n", " \n", "for key, value in evaluation_results.items():\n", " print(f\"{key}: {value:.4f}\")\n", "\n", " if isinstance(value, float):\n", " print(f\"{key}: {value:.4f}\")\n", "\n", " else:\n", " print(f\"{key}: {value}\")\n", "\n", "\n", " \n", "print(\"\\nBest hyperparameters found by GridSearchCV:\")\n", "\n", "print(grid_search.best_params_)\n" ] }, { "cell_type": "code", "execution_count": 74, "id": "e2c429ad-425a-4b63-a238-773ce5654e75", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.svm import SVR\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.tree import DecisionTreeRegressor\n", "from sklearn.model_selection import GridSearchCV, train_test_split\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", "from sklearn.preprocessing import StandardScaler\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 75, "id": "7f5aeb3f-29e6-47ac-b258-6fcec813fd3d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PregnantGlucoseDiastolic_BPSkin_FoldSerum_InsulinBMIDiabetes_PedigreeAgeClass
06148.072.035.0NaN33.60.627501
1185.066.029.0NaN26.60.351310
28183.064.0NaNNaN23.30.672321
3189.066.023.094.028.10.167210
40137.040.035.0168.043.12.288331
\n", "
" ], "text/plain": [ " Pregnant Glucose Diastolic_BP Skin_Fold Serum_Insulin BMI \\\n", "0 6 148.0 72.0 35.0 NaN 33.6 \n", "1 1 85.0 66.0 29.0 NaN 26.6 \n", "2 8 183.0 64.0 NaN NaN 23.3 \n", "3 1 89.0 66.0 23.0 94.0 28.1 \n", "4 0 137.0 40.0 35.0 168.0 43.1 \n", "\n", " Diabetes_Pedigree Age Class \n", "0 0.627 50 1 \n", "1 0.351 31 0 \n", "2 0.672 32 1 \n", "3 0.167 21 0 \n", "4 2.288 33 1 " ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv('Diabetes Missing Data (1).csv')\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 76, "id": "1784af3e-8fd9-4c47-a22d-83dff0f59643", "metadata": {}, "outputs": [], "source": [ "X = data.drop('Serum_Insulin', axis=1)\n", "y = data['Serum_Insulin']" ] }, { "cell_type": "code", "execution_count": 77, "id": "8c0a9e17-1fab-4d13-9332-c8990224716b", "metadata": {}, "outputs": [], "source": [ " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n" ] }, { "cell_type": "code", "execution_count": 25, "id": "91fc14bf-0b1b-4798-8463-5d1c8077a415", "metadata": { "scrolled": true }, "outputs": [ { "ename": "AttributeError", "evalue": "'numpy.ndarray' object has no attribute 'isna'", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[25]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mensemble\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m RandomForestRegressor\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m mask = ~y_train.isna()\n\u001b[32m 5\u001b[39m X_train_clean = X_train[mask]\n\u001b[32m 6\u001b[39m y_train_clean = y_train[mask]\n", "\u001b[31mAttributeError\u001b[39m: 'numpy.ndarray' object has no attribute 'isna'" ] } ], "source": [ "\n", "from sklearn.ensemble import RandomForestRegressor\n", "import numpy as np\n", "\n", "mask = ~y_train.isna()\n", "X_train_clean = X_train[mask]\n", "y_train_clean = y_train[mask]\n", "\n", "model = RandomForestRegressor(random_state=42)\n", "model.fit(X_train_clean, y_train_clean)\n", "\n", "feature_importances = model.feature_importances_\n", "\n", "\n", "selected_features = X.columns[np.argsort(feature_importances)[-10:]]\n", "\n", "print(f\"Selected features using tree-based importance: {selected_features.tolist()}\")\n" ] }, { "cell_type": "code", "execution_count": 26, "id": "724a923f-4d6c-41f4-943a-2f6a1775011f", "metadata": {}, "outputs": [], "source": [ "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", "\n", "def evaluate_model(model_name, y_true, y_pred):\n", " mae = mean_absolute_error(y_true, y_pred)\n", " mse = mean_squared_error(y_true, y_pred)\n", " rmse = np.sqrt(mse)\n", " r2 = r2_score(y_true, y_pred)\n", "\n", " results = {\n", " \"Model\": model_name,\n", " \"Mean Absolute Error (MAE)\": mae,\n", " \"Mean Squared Error (MSE)\": mse,\n", " \"Root Mean Squared Error (RMSE)\": rmse,\n", " \"R^2 Score\": r2\n", " }\n", "\n", " return results\n", " " ] }, { "cell_type": "code", "execution_count": 28, "id": "a3fd62ed-6147-4f71-9070-0124834e22c0", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'DecisionTreeRegressor' is not defined", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[28]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m DT = DecisionTreeRegressor(random_state=\u001b[32m42\u001b[39m)\n\u001b[32m 4\u001b[39m param_grid = {\n\u001b[32m 5\u001b[39m \u001b[33m'\u001b[39m\u001b[33mmax_depth\u001b[39m\u001b[33m'\u001b[39m: [\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[32m10\u001b[39m, \u001b[32m20\u001b[39m, \u001b[32m30\u001b[39m],\n\u001b[32m 6\u001b[39m \u001b[33m'\u001b[39m\u001b[33mmin_samples_split\u001b[39m\u001b[33m'\u001b[39m: [\u001b[32m2\u001b[39m, \u001b[32m5\u001b[39m, \u001b[32m10\u001b[39m],\n\u001b[32m 7\u001b[39m \u001b[33m'\u001b[39m\u001b[33mmin_samples_leaf\u001b[39m\u001b[33m'\u001b[39m: [\u001b[32m1\u001b[39m, \u001b[32m2\u001b[39m, \u001b[32m4\u001b[39m],\n\u001b[32m 8\u001b[39m }\n\u001b[32m 11\u001b[39m grid_search = GridSearchCV(\n\u001b[32m 12\u001b[39m estimator=DT,\n\u001b[32m 13\u001b[39m param_grid=param_grid,\n\u001b[32m (...)\u001b[39m\u001b[32m 17\u001b[39m n_jobs=-\u001b[32m1\u001b[39m \n\u001b[32m 18\u001b[39m )\n", "\u001b[31mNameError\u001b[39m: name 'DecisionTreeRegressor' is not defined" ] } ], "source": [ "\n", "\n", "\n", "\n", "DT = DecisionTreeRegressor(random_state=42)\n", "\n", " \n", "param_grid = {\n", " 'max_depth': [None, 10, 20, 30],\n", " 'min_samples_split': [2, 5, 10],\n", " 'min_samples_leaf': [1, 2, 4],\n", " }\n", "\n", " \n", "grid_search = GridSearchCV(\n", " estimator=DT,\n", " param_grid=param_grid,\n", " cv=5, \n", " scoring='neg_root_mean_squared_error', \n", " verbose=2,\n", " n_jobs=-1 \n", " )\n", "\n", "\n", "grid_search.fit(X_train_selected, y_train)\n", "\n", " \n", "print(\"Best Parameters:\", grid_search.best_params_)\n", " \n", "print(\"Best CV RMSE:\", grid_search.best_score_)\n", "\n", "\n", "best_DT = grid_search.best_estimator_\n", "\n", "\n", "y_pred = best_DT.predict(X_test_selected)\n", "\n", "evaluation_results = evaluate_model('Decision Tree Regressor', y_test, y_pred)\n", "\n", "\n", "print(\"\\nEvaluation Results on Test Set:\")\n", "\n", "for key, value in evaluation_results.items():\n", " if isinstance(value, float):\n", " print(f\"{key}: {value:.4f}\")\n", " else:\n", " print(f\"{key}:\\n{value}\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 32, "id": "eefe8067-4d4f-46c1-b52e-209dc2afa5e7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 108 candidates, totalling 540 fits\n", "Best Parameters: {'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 100}\n", "\n", "Evaluation Results on Test Set:\n", "Model: RandomForestRegressor\n", "Mean Absolute Error (MAE): 0.0690\n", "Mean Squared Error (MSE): 0.0399\n", "Root Mean Squared Error (RMSE): 0.1997\n", "R^2 Score: 0.8303\n" ] } ], "source": [ "rf_regressor = RandomForestRegressor()\n", "\n", "# Define the parameter grid\n", "param_grid = {\n", " 'n_estimators': [100, 200, 300],\n", " 'max_depth': [None, 10, 20, 30],\n", " 'min_samples_split': [2, 5, 10],\n", " 'min_samples_leaf': [1, 2, 4],\n", "}\n", "\n", "# Initialize GridSearchCV\n", "grid_search = GridSearchCV(\n", " estimator=rf_regressor,\n", " param_grid=param_grid,\n", " cv=5, # 5-fold cross-validation\n", " scoring='neg_root_mean_squared_error',\n", " verbose=2,\n", " n_jobs=-1\n", ")\n", "\n", "# Fit the grid search to the data\n", "grid_search.fit(X_train_selected, y_train)\n", "\n", "print(\"Best Parameters:\", grid_search.best_params_)\n", "\n", "# Best model\n", "best_rf = grid_search.best_estimator_\n", "\n", "# Predictions\n", "y_pred = best_rf.predict(X_test_selected)\n", "\n", "# Evaluation\n", "evaluation_results = evaluate_model('RandomForestRegressor', y_test, y_pred)\n", "\n", "print(\"\\nEvaluation Results on Test Set:\")\n", "\n", "for key, value in evaluation_results.items():\n", " if isinstance(value, float):\n", " print(f\"{key}: {value:.4f}\")\n", " else:\n", " print(f\"{key}: {value}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "acc3d35e-e91c-4127-959b-4a11c07ceb39", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 5 }