{ "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", " # Boolean DataFrame indicating if a value is an outlier\n", " outliers = pd.DataFrame(False, index=df.index, columns=df.columns)\n", " \n", " # Iterate through each numeric column\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", " # Mark True in outliers DataFrame where abs(z_score) > threshold\n", " outliers[column] = abs(z_scores) > threshold\n", " \n", " # Filter the rows that have any True in the outliers DataFrame (indicating an o\n", " rows_to_keep = ~outliers.any(axis=1)\n", " \n", " # Return the DataFrame with outlier rows removed\n", " return data[rows_to_keep]\n", " # Example usage:\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
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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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", 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" ] }, "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": {}, "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": null, "id": "770ddb44-c162-4562-88b8-2241e0116f91", "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 }