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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
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891 rows × 12 columns

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" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", ".. ... ... ... \n", "886 887 0 2 \n", "887 888 1 1 \n", "888 889 0 3 \n", "889 890 1 1 \n", "890 891 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", ".. ... ... ... ... \n", "886 Montvila, Rev. Juozas male 27.0 0 \n", "887 Graham, Miss. Margaret Edith female 19.0 0 \n", "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", "889 Behr, Mr. Karl Howell male 26.0 0 \n", "890 Dooley, Mr. Patrick male 32.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S \n", ".. ... ... ... ... ... \n", "886 0 211536 13.0000 NaN S \n", "887 0 112053 30.0000 B42 S \n", "888 2 W./C. 6607 23.4500 NaN S \n", "889 0 111369 30.0000 C148 C \n", "890 0 370376 7.7500 NaN Q \n", "\n", "[891 rows x 12 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "# Load your dataset into a DataFrame\n", "df = pd.read_csv('Titanic-Dataset.csv')\n", "# Preview the first 5 rows\n", "df" ] }, { "cell_type": "code", "execution_count": 3, "id": "e4b67133-1f02-4e18-b562-a4aeb4ffee3e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
25%223.5000000.0000002.00000020.1250000.0000000.0000007.910400
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
75%668.5000001.0000003.00000038.0000001.0000000.00000031.000000
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200
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" ], "text/plain": [ " PassengerId Survived Pclass Age SibSp \\\n", "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", "\n", " Parch Fare \n", "count 891.000000 891.000000 \n", "mean 0.381594 32.204208 \n", "std 0.806057 49.693429 \n", "min 0.000000 0.000000 \n", "25% 0.000000 7.910400 \n", "50% 0.000000 14.454200 \n", "75% 0.000000 31.000000 \n", "max 6.000000 512.329200 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get descriptive statistics for numerical columns\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 4, "id": "fbfea5e8-813e-4dac-879c-18df5384f085", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PassengerId 0\n", "Survived 0\n", "Pclass 0\n", "Name 0\n", "Sex 0\n", "Age 177\n", "SibSp 0\n", "Parch 0\n", "Ticket 0\n", "Fare 0\n", "Cabin 687\n", "Embarked 2\n", "dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check for missing values\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 5, "id": "c2d567f0-0e28-4233-ade4-7d2dc337af96", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "891" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(df)" ] }, { "cell_type": "code", "execution_count": 6, "id": "6d19fc35-8f9f-428d-aeee-4788c3d06d64", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns with more than 30% missing values:\n", "Cabin 77.104377\n", "dtype: float64\n" ] } ], "source": [ "missing_percentage = df.isnull().mean() * 100\n", "# Filter columns with more than 30% (or 50%) missing values\n", "columns_with_missing_30 = missing_percentage[missing_percentage > 30]\n", "# Display the columns with more than 30% missing values\n", "print(\"Columns with more than 30% missing values:\")\n", "print(columns_with_missing_30)" ] }, { "cell_type": "code", "execution_count": 7, "id": "922a287a-b8ab-4056-8a93-55613f9e1603", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns dropped: ['Cabin']\n", "Remaining columns: Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n", " 'Parch', 'Ticket', 'Fare', 'Embarked'],\n", " dtype='object')\n" ] } ], "source": [ "missing_percentage = df.isnull().mean() * 100\n", "threshold = 30\n", "# Filter columns with more than the threshold percentage of missing values\n", "columns_to_drop = missing_percentage[missing_percentage > threshold].index\n", "# Drop the columns\n", "df_cleaned = df.drop(columns=columns_to_drop)\n", "# Display the resulting dataframe after removing columns with too many missing valu\n", "print(f\"Columns dropped: {list(columns_to_drop)}\")\n", "print(f\"Remaining columns: {df_cleaned.columns}\")" ] }, { "cell_type": "code", "execution_count": 52, "id": "f9a5e428-ca55-43c1-b5c1-a539ade6e73b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Embarked\n", "0 S\n", "1 C\n", "2 S\n", "3 S\n", "4 S\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_19228\\2219690981.py:6: 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['Embarked'].fillna(data['Embarked'].mode()[0], inplace=True)\n" ] } ], "source": [ "data = pd.DataFrame({\n", "'Embarked': ['S', 'C', None, 'S', 'S']\n", "})\n", "\n", "# Mode imputation for categorical columns\n", "data['Embarked'].fillna(data['Embarked'].mode()[0], inplace=True)\n", "print(data)" ] }, { "cell_type": "code", "execution_count": 18, "id": "c4239483-22bd-4eca-8997-c26c66ac8df8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Age\n", "0 22.0\n", "1 38.0\n", "2 26.0\n", "3 35.0\n", "4 35.0\n", "5 31.2\n" ] } ], "source": [ "from sklearn.impute import KNNImputer\n", "# Example dataset\n", "data = pd.DataFrame({\n", "'Age': [22, 38, 26, 35, 35, None],\n", "})\n", "# KNN imputation (use n_neighbors=2, you can adjust this)\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": null, "id": "4aaab829-9142-421a-894e-f3cbc0664e61", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 41, "id": "2f61cc0a-567a-44a9-a549-2e876e23f526", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S " ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "df=pd.read_csv(\"Titanic-Dataset.csv\")\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 56, "id": "c141c7ed-27c2-482e-ba9d-84bf9f821fea", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedPassengerId_OutlierSurvived_OutlierPclass_OutlierAge_OutlierSibSp_OutlierParch_OutlierFare_Outlier
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85CFalseFalseFalseFalseFalseFalseTrue
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNSFalseFalseFalseFalseTrueTrueFalse
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNSFalseFalseFalseFalseFalseTrueFalse
101113Sandstrom, Miss. Marguerite Rutfemale4.011PP 954916.7000G6SFalseFalseFalseFalseFalseTrueFalse
131403Andersson, Mr. Anders Johanmale39.01534708231.2750NaNSFalseFalseFalseFalseFalseTrueFalse
............................................................
87187211Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female47.0111175152.5542D35SFalseFalseFalseFalseFalseTrueFalse
87988011Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)female56.0011176783.1583C50CFalseFalseFalseFalseFalseTrueTrue
88088112Shelley, Mrs. William (Imanita Parrish Hall)female25.00123043326.0000NaNSFalseFalseFalseFalseFalseTrueFalse
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250NaNQFalseFalseFalseFalseFalseTrueFalse
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNSFalseFalseFalseFalseFalseTrueFalse
\n", "

293 rows × 19 columns

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" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "1 2 1 1 \n", "7 8 0 3 \n", "8 9 1 3 \n", "10 11 1 3 \n", "13 14 0 3 \n", ".. ... ... ... \n", "871 872 1 1 \n", "879 880 1 1 \n", "880 881 1 2 \n", "885 886 0 3 \n", "888 889 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "7 Palsson, Master. Gosta Leonard male 2.0 3 \n", "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n", "10 Sandstrom, Miss. Marguerite Rut female 4.0 1 \n", "13 Andersson, Mr. Anders Johan male 39.0 1 \n", ".. ... ... ... ... \n", "871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1 \n", "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0 \n", "880 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0 \n", "885 Rice, Mrs. William (Margaret Norton) female 39.0 0 \n", "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", "\n", " Parch Ticket Fare Cabin Embarked PassengerId_Outlier \\\n", "1 0 PC 17599 71.2833 C85 C False \n", "7 1 349909 21.0750 NaN S False \n", "8 2 347742 11.1333 NaN S False \n", "10 1 PP 9549 16.7000 G6 S False \n", "13 5 347082 31.2750 NaN S False \n", ".. ... ... ... ... ... ... \n", "871 1 11751 52.5542 D35 S False \n", "879 1 11767 83.1583 C50 C False \n", "880 1 230433 26.0000 NaN S False \n", "885 5 382652 29.1250 NaN Q False \n", "888 2 W./C. 6607 23.4500 NaN S False \n", "\n", " Survived_Outlier Pclass_Outlier Age_Outlier SibSp_Outlier \\\n", "1 False False False False \n", "7 False False False True \n", "8 False False False False \n", "10 False False False False \n", "13 False False False False \n", ".. ... ... ... ... \n", "871 False False False False \n", "879 False False False False \n", "880 False False False False \n", "885 False False False False \n", "888 False False False False \n", "\n", " Parch_Outlier Fare_Outlier \n", "1 False True \n", "7 True False \n", "8 True False \n", "10 True False \n", "13 True False \n", ".. ... ... \n", "871 True False \n", "879 True True \n", "880 True False \n", "885 True False \n", "888 True False \n", "\n", "[293 rows x 19 columns]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Import necessary libraries\n", "import pandas as pd\n", "\n", "# Load the Titanic dataset\n", "file_path = 'Titanic-Dataset.csv'\n", "df = pd.read_csv(file_path)\n", "\n", "# Define the function to detect outliers using the IQR method\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", " # Return a boolean series indicating if the value is an outlier\n", " return (column < lower_bound) | (column > upper_bound)\n", "\n", "# Copy the original data to avoid modifying it directly\n", "data = df.copy()\n", "\n", "# Apply the IQR method to each numeric feature in the DataFrame\n", "for col in data.select_dtypes(include='number').columns:\n", " data[f'{col}_Outlier'] = detect_outliers_iqr(data[col])\n", "\n", "# Filter the DataFrame to display rows where any outlier flag is True\n", "outliers_only = data[data.filter(like='_Outlier').any(axis=1)]\n", "\n", "# Display the filtered DataFrame with only the outliers\n", "outliers_only\n" ] }, { "cell_type": "code", "execution_count": 64, "id": "b0c75ab9-9a78-4916-9b33-93f0e23f8b1f", "metadata": {}, "outputs": [ { "data": { "image/png": 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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=(12, 6))\n", "\n", "for i, col in enumerate(df.columns[:3]): # Only plotting the first 3 features\n", " plt.subplot(1, 3, i+1) # Create subplots for each feature\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": 69, "id": "cad0afdf-240b-499b-8570-57894b973a57", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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131403Andersson, Mr. Anders Johanmale39.01534708231.2750...0.827377FalseNaNFalse0.432793False5.732844True-0.018709False
161703Rice, Master. Eugenemale2.04138265229.1250...0.827377FalseNaNFalse3.154809True0.767630False-0.061999False
252613Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...female38.01534707731.3875...0.827377FalseNaNFalse0.432793False5.732844True-0.016444False
272801Fortune, Mr. Charles Alexandermale19.03219950263.0000...-1.566107FalseNaNFalse2.247470False2.008933False4.647001True
505103Panula, Master. Juha Niilomale7.041310129539.6875...0.827377FalseNaNFalse3.154809True0.767630False0.150674False
..................................................................
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85085103Andersson, Master. Sigvard Harald Eliasmale4.04234708231.2750...0.827377FalseNaNFalse3.154809True2.008933False-0.018709False
85885913Baclini, Mrs. Solomon (Latifa Qurban)female24.003266619.2583...0.827377FalseNaNFalse-0.474545False3.250237True-0.260662False
86386403Sage, Miss. Dorothy Edith \"Dolly\"femaleNaN82CA. 234369.5500...0.827377FalseNaNFalse6.784163True2.008933False0.751946False
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250...0.827377FalseNaNFalse-0.474545False5.732844True-0.061999False
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64 rows × 26 columns

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" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "13 14 0 3 \n", "16 17 0 3 \n", "25 26 1 3 \n", "27 28 0 1 \n", "50 51 0 3 \n", ".. ... ... ... \n", "846 847 0 3 \n", "850 851 0 3 \n", "858 859 1 3 \n", "863 864 0 3 \n", "885 886 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "13 Andersson, Mr. Anders Johan male 39.0 1 \n", "16 Rice, Master. Eugene male 2.0 4 \n", "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.0 1 \n", "27 Fortune, Mr. Charles Alexander male 19.0 3 \n", "50 Panula, Master. Juha Niilo male 7.0 4 \n", ".. ... ... ... ... \n", "846 Sage, Mr. Douglas Bullen male NaN 8 \n", "850 Andersson, Master. Sigvard Harald Elias male 4.0 4 \n", "858 Baclini, Mrs. Solomon (Latifa Qurban) female 24.0 0 \n", "863 Sage, Miss. Dorothy Edith \"Dolly\" female NaN 8 \n", "885 Rice, Mrs. William (Margaret Norton) female 39.0 0 \n", "\n", " Parch Ticket Fare ... Pclass_Z_Score Pclass_Outlier \\\n", "13 5 347082 31.2750 ... 0.827377 False \n", "16 1 382652 29.1250 ... 0.827377 False \n", "25 5 347077 31.3875 ... 0.827377 False \n", "27 2 19950 263.0000 ... -1.566107 False \n", "50 1 3101295 39.6875 ... 0.827377 False \n", ".. ... ... ... ... ... ... \n", "846 2 CA. 2343 69.5500 ... 0.827377 False \n", "850 2 347082 31.2750 ... 0.827377 False \n", "858 3 2666 19.2583 ... 0.827377 False \n", "863 2 CA. 2343 69.5500 ... 0.827377 False \n", "885 5 382652 29.1250 ... 0.827377 False \n", "\n", " Age_Z_Score Age_Outlier SibSp_Z_Score SibSp_Outlier Parch_Z_Score \\\n", "13 NaN False 0.432793 False 5.732844 \n", "16 NaN False 3.154809 True 0.767630 \n", "25 NaN False 0.432793 False 5.732844 \n", "27 NaN False 2.247470 False 2.008933 \n", "50 NaN False 3.154809 True 0.767630 \n", ".. ... ... ... ... ... \n", "846 NaN False 6.784163 True 2.008933 \n", "850 NaN False 3.154809 True 2.008933 \n", "858 NaN False -0.474545 False 3.250237 \n", "863 NaN False 6.784163 True 2.008933 \n", "885 NaN False -0.474545 False 5.732844 \n", "\n", " Parch_Outlier Fare_Z_Score Fare_Outlier \n", "13 True -0.018709 False \n", "16 False -0.061999 False \n", "25 True -0.016444 False \n", "27 False 4.647001 True \n", "50 False 0.150674 False \n", ".. ... ... ... \n", "846 False 0.751946 False \n", "850 False -0.018709 False \n", "858 True -0.260662 False \n", "863 False 0.751946 False \n", "885 True -0.061999 False \n", "\n", "[64 rows x 26 columns]" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Import necessary libraries\n", "import pandas as pd\n", "from scipy.stats import zscore\n", "\n", "# Load the Titanic dataset\n", "file_path = 'Titanic-Dataset.csv'\n", "df = pd.read_csv(file_path)\n", "\n", "# Copy the original data to avoid modifying it directly\n", "data = df.copy()\n", "\n", "# Define a threshold for identifying outliers (typically Z > 3 or Z < -3)\n", "z_threshold = 3\n", "\n", "# Apply the Z-score method to each numeric feature in the DataFrame\n", "for col in data.select_dtypes(include='number').columns:\n", " # Calculate the Z-scores\n", " data[f'{col}_Z_Score'] = zscore(data[col])\n", " # Flag as outlier if Z-score exceeds the threshold\n", " data[f'{col}_Outlier'] = data[f'{col}_Z_Score'].abs() > z_threshold\n", "\n", "# Filter the DataFrame to display rows where any outlier flag is True\n", "outliers_only = data[data.filter(like='_Outlier').any(axis=1)]\n", "\n", "# Display the filtered DataFrame with only the outliers\n", "outliers_only" ] }, { "cell_type": "code", "execution_count": 79, "id": "faf0ce72-9e7d-4508-a6f6-a559ebfb0ab1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.01.00.0A/5 211717.250NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.01.00.0PC 17599NaNC85C
2313Heikkinen, Miss. Lainafemale26.00.00.0STON/O2. 31012827.925NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01.00.011380353.100C123S
4503Allen, Mr. William Henrymale35.00.00.03734508.050NaNS
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" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1.0 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1.0 \n", "2 Heikkinen, Miss. Laina female 26.0 0.0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1.0 \n", "4 Allen, Mr. William Henry male 35.0 0.0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0.0 A/5 21171 7.250 NaN S \n", "1 0.0 PC 17599 NaN C85 C \n", "2 0.0 STON/O2. 3101282 7.925 NaN S \n", "3 0.0 113803 53.100 C123 S \n", "4 0.0 373450 8.050 NaN S " ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "# Load the Titanic dataset\n", "file_path = 'Titanic-Dataset.csv'\n", "df = pd.read_csv(file_path)\n", "\n", "# Copy the original data to avoid modifying it directly\n", "data = df.copy()\n", "\n", "# Define the function to replace outliers with NaN based on the IQR method\n", "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", " # Replace outliers with NaN\n", " return column.where((column >= lower_bound) & (column <= upper_bound), np.nan)\n", "\n", "# Apply the function to each numeric column in the DataFrame\n", "for col in data.select_dtypes(include='number').columns:\n", " data[col] = replace_outliers_with_nan(data[col])\n", "\n", "# Display the resulting DataFrame\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 80, "id": "76b563bc-b7b5-410d-bbdb-7236f94b147a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sum of null values in each column:\n", "PassengerId 0\n", "Survived 0\n", "Pclass 0\n", "Name 0\n", "Sex 0\n", "Age 188\n", "SibSp 46\n", "Parch 213\n", "Ticket 0\n", "Fare 116\n", "Cabin 687\n", "Embarked 2\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": 83, "id": "93fcc68a-57e8-4c92-b2e5-c54570581f0c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PassengerId 0\n", "Survived 0\n", "Pclass 0\n", "Age 0\n", "SibSp 0\n", "Parch 0\n", "Fare 0\n", "dtype: int64\n" ] } ], "source": [ "import pandas as pd\n", "from sklearn.impute import KNNImputer\n", "\n", "# Load the Titanic dataset\n", "file_path = 'Titanic-Dataset.csv'\n", "data = pd.read_csv(file_path)\n", "\n", "# Initialize the KNNImputer with the desired number of neighbors\n", "imputer = KNNImputer(n_neighbors=2)\n", "\n", "# Apply the KNN imputer to the data and keep the columns\n", "data_imputed_knn = pd.DataFrame(imputer.fit_transform(data.select_dtypes(include='number')), columns=data.select_dtypes(include='number').columns)\n", "\n", "# Check for remaining missing values in the imputed data\n", "print(data_imputed_knn.isnull().sum())" ] }, { "cell_type": "code", "execution_count": 96, "id": "a8f968ae-a52d-4bb4-bb0d-b043ea8bf2d1", "metadata": {}, "outputs": [], "source": [ "df=pd.read_csv(\"Titanic-Dataset.csv\")" ] }, { "cell_type": "code", "execution_count": 101, "id": "b6fbc50e-d3d2-46a3-a358-b44b3a55ac50", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original Data:\n", "891\n", "\n", "Cleaned Data (without outliers):\n", "598\n" ] } ], "source": [ "# Import necessary libraries\n", "import pandas as pd\n", "\n", "# Load the Titanic dataset\n", "file_path = 'Titanic-Dataset.csv'\n", "df = pd.read_csv(file_path)\n", "\n", "# Make a copy of the original data\n", "data = df.copy()\n", "\n", "# Define the function to remove outliers using the IQR method for numeric columns only\n", "def remove_outliers_iqr(data):\n", " # Select only numeric columns for outlier detection\n", " numeric_data = data.select_dtypes(include='number')\n", " \n", " # Calculate Q1 (25th percentile) and Q3 (75th percentile) for each numeric feature\n", " Q1 = numeric_data.quantile(0.25)\n", " Q3 = numeric_data.quantile(0.75)\n", " IQR = Q3 - Q1\n", " \n", " # Define lower and upper bounds\n", " lower_bound = Q1 - 1.5 * IQR\n", " upper_bound = Q3 + 1.5 * IQR\n", " \n", " # Filter the dataset to remove rows with outliers in any numeric column\n", " data_cleaned = data[~((numeric_data < lower_bound) | (numeric_data > upper_bound)).any(axis=1)]\n", " \n", " return data_cleaned\n", "\n", "# Apply the function to remove outliers\n", "cleaned_data = remove_outliers_iqr(data)\n", "\n", "# Display the number of rows in the original and cleaned datasets\n", "print(\"Original Data:\")\n", "print(len(data))\n", "\n", "print(\"\\nCleaned Data (without outliers):\")\n", "print(len(cleaned_data))\n" ] }, { "cell_type": "code", "execution_count": 87, "id": "32b7ecc3-5314-4914-ab6f-e5803d49bed9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original Data:\n", "891\n", "\n", "Cleaned Data (without outliers):\n", "825\n" ] } ], "source": [ "data = df.copy() # Create a copy to avoid modifying the original dataframe\n", "def remove_outliers_zscore(data, threshold=3):\n", " # Boolean DataFrame indicating if a value is an outlier\n", " outliers = pd.DataFrame(False, index=df.index, columns=df.columns)\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", " # 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", " \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": 102, "id": "86586150-3227-40be-a35c-1fdca8011d00", "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": 103, "id": "284e6881-183a-467b-909b-581a46db6371", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S " ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('Titanic-Dataset.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 107, "id": "345f3690-1c46-4ee8-86c4-8e4534cb242c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
25%223.5000000.0000002.00000020.1250000.0000000.0000007.910400
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
75%668.5000001.0000003.00000038.0000001.0000000.00000031.000000
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200
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" ], "text/plain": [ " PassengerId Survived Pclass Age SibSp \\\n", "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", "\n", " Parch Fare \n", "count 891.000000 891.000000 \n", "mean 0.381594 32.204208 \n", "std 0.806057 49.693429 \n", "min 0.000000 0.000000 \n", "25% 0.000000 7.910400 \n", "50% 0.000000 14.454200 \n", "75% 0.000000 31.000000 \n", "max 6.000000 512.329200 " ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 123, "id": "0a89e1b9-7b65-40e2-9415-aecfb1024a90", "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "# Load the Titanic dataset\n", "file_path = 'Titanic-Dataset.csv'\n", "\n", "df = pd.read_csv(file_path)\n", "\n", "# Generate the correlation heatmap\n", "plt.figure(figsize=(12, 10))\n", "sns.heatmap(numeric_df.corr(), annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n", "plt.title('Correlation Heatmap')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 125, "id": "97665ea1-bb5e-4954-a73d-83ee87b225a6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of males: 342\n", "Number of females: 549\n" ] } ], "source": [ "# Check the gender column\n", "gender_counts = df['Survived'].value_counts() # 'sex' is typically coded as 0 (female)\n", "# Display the counts\n", "print(\"Number of males:\", gender_counts[1]) # Assuming 1 is for male\n", "print(\"Number of females:\", gender_counts[0]) # Assuming 0 is for female" ] }, { "cell_type": "code", "execution_count": 129, "id": "14117f02-8c5c-4447-9443-130d4bd07fe2", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a bar chart\n", "plt.figure(figsize=(8, 5))\n", "gender_counts.plot(kind='bar', color=['pink', 'blue'])\n", "plt.xticks([0, 1], ['Female (1)', 'Male (0)'], rotation=0)\n", "plt.title('Number of Males and Females in Titanic-Dataset.csv')\n", "plt.xlabel('Gender')\n", "plt.ylabel('Count')\n", "plt.grid(axis='y')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 136, "id": "2602060c-2511-4a70-94da-08f6f2a3e8c8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sex female male\n", "Survived \n", "0 81 468\n", "1 233 109\n" ] } ], "source": [ "# Group by target (heart disease presence) and sex, then count\n", "sex_class_counts = df.groupby(['Survived', 'Sex']).size().unstack(fill_value=0)\n", "# Display the result\n", "print(sex_class_counts)" ] }, { "cell_type": "code", "execution_count": 139, "id": "658a60ea-167d-42a0-805f-e215d55503cc", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a bar chart\n", "sex_class_counts.plot(kind='bar', color=['lightcoral', 'lightblue'])\n", "plt.title('Number of Males and Females Based on Survived Class')\n", "plt.xlabel('Survived Class (0 = No, 1 = Yes)')\n", "plt.ylabel('Count')\n", "plt.xticks(rotation=0)\n", "plt.legend(title='Sex', labels=['Female (1)', 'Male (0)'])\n", "plt.grid(axis='y')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 162, "id": "30467b1c-8850-4265-81bd-2b219b55d3e2", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Pclass_counts = df['Pclass'].value_counts()\n", "# Create a pie chart\n", "plt.figure(figsize=(8, 5))\n", "plt.pie(cp_counts, labels=['Type 0', 'Type 1','Type 2'], autopct='%1.1f%%')\n", "plt.title('Distribution of Chest Pain Types (Pclass)')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 167, "id": "a3ecfca8-5788-44a9-ba0c-e6bfe3cef40e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create the box plot\n", "plt.figure(figsize=(8, 6))\n", "df.boxplot(column='Age', by='Sex', grid=False)\n", "plt.title('Age Distribution of Survived Passengers')\n", "plt.suptitle('')\n", "plt.xlabel('Number of Survived Passengers')\n", "plt.ylabel('Age')\n", "plt.xticks([1, 2], ['No Survived (0)', 'Survived (1)'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 173, "id": "95d642e3-014e-4a68-b229-979de4e79cd3", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 6))\n", "plt.scatter(df['Pclass'], df['Age'], alpha=0.8)\n", "plt.title('Pclass vs. Age')\n", "plt.xlabel('Pclass')\n", "plt.ylabel('Age')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 175, "id": "332f93a2-35a1-45c9-948d-d5d0d08e1940", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 6))\n", "plt.hist(df['Age'], bins=20, color='skyblue', edgecolor='black')\n", "plt.title('Age Distribution in Titanic Dataset')\n", "plt.xlabel('Age')\n", "plt.ylabel('Frequency')\n", "plt.grid(axis='y')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 182, "id": "ab5c8afe-bb77-4e5e-9e86-6a05d4ab7bbf", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "age_grouped = df.groupby('Age')['SibSp'].mean().reset_index()\n", "# Create a line chart\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(age_grouped['Age'], age_grouped['SibSp'], marker='o', linestyle='-', color='b')\n", "plt.title('Average SibSp Levels by Age')\n", "plt.xlabel('Age')\n", "plt.ylabel('Average SibSp Level')\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 183, "id": "f2fba3b3-3023-4022-962e-60c5b0abb67f", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (1241551015.py, line 1)", "output_type": "error", "traceback": [ "\u001b[1;36m Cell \u001b[1;32mIn[183], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m jupyter nbconvert --to html your_notebook.ipynb\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" ] } ], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "849c3f9a-b331-4f0a-aba4-dd0103d08c33", "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": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }