{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "8a492400-1dbe-4dd0-89e4-3d9f2361c095", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 31, "id": "1136028d-8f8e-4e24-a417-7485647ffc4a", "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": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from matplotlib import pyplot as plt \n", "import seaborn as sns\n", "import pandas as pd\n", "df=pd.read_csv('Titanic-Dataset.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "id": "e9c69e75-8c20-486a-ac16-b80984d4ab56", "metadata": {}, "outputs": [], "source": [ "#check Missing value" ] }, { "cell_type": "code", "execution_count": 9, "id": "6a617ff2-9859-4906-95d9-bf03f04e157a", "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": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ " df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": null, "id": "fb6b2c35-78a4-4a0e-8f8a-21afa13dee27", "metadata": {}, "outputs": [], "source": [ "# Handle Missing value" ] }, { "cell_type": "code", "execution_count": 13, "id": "8f98172a-a745-4093-838c-a267386fc9fb", "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 # Calculate the percentage of missing values\n", "\n", "# Filter columns with more than 30% (or 50%) missing values\n", "columns_with_missing_30 = missing_percentage[missing_percentage > 30]\n", "\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)\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "f27ea843-14f8-4c1e-a655-1ba8cb53b532", "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": null, "id": "13b8c0bd-e243-4336-9abe-a418402cc3c3", "metadata": {}, "outputs": [], "source": [ "######################################################################" ] }, { "cell_type": "code", "execution_count": null, "id": "05f50ab2-f979-4640-9fba-6ef231778255", "metadata": {}, "outputs": [], "source": [ "#Handling Outliers" ] }, { "cell_type": "code", "execution_count": 19, "id": "90a29457-3a18-42eb-a220-16afbcaf7919", "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
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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": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "# Detect Outliers\n", "\n", "\n", "data = df.copy()\n", "\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", "# Apply the IQR method to each numeric feature in the DataFrame\n", "for col in data.select_dtypes(include=['number']).columns: # Only select numeric 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", "# Display the filtered DataFrame with outliers\n", "outliers_only\n" ] }, { "cell_type": "code", "execution_count": 21, "id": "38a01f27-e7c5-46f3-aff5-5152686c595b", "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": [ "import numpy as np\n", "\n", "data = df.copy() # Removed indentation here\n", "\n", "def replace_outliers_with_nan(column):\n", " # Check if the column is numeric\n", " if np.issubdtype(column.dtype, np.number):\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", " else:\n", " # If not numeric, return the column unchanged\n", " return column\n", "\n", "# Replace outliers with NaN for each feature\n", "for col in data.columns:\n", " data[col] = replace_outliers_with_nan(data[col])\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": null, "id": "8a36df48-91f2-4cdc-8d46-f3315d3ffe02", "metadata": {}, "outputs": [], "source": [ "# Now Handle missing value" ] }, { "cell_type": "code", "execution_count": 27, "id": "ae162a7f-6dc5-4702-899f-e81cf98dc2ca", "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 = data.isnull().mean() * 100 # Calculate the percentage of missing values\n", "\n", "# Filter columns with more than 30% (or 50%) missing values\n", "columns_with_missing_30 = missing_percentage[missing_percentage > 30]\n", "\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": 29, "id": "aaeb7a3b-a539-4b98-aac2-c29bfc9481c1", "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": [ "\n", "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.drop(columns=columns_to_drop, inplace=True) # Removed the extra brackets around columns_to_drop\n", "# Display the resulting dataframe after removing columns with too many missing values\n", "print(f\"Columns dropped: {list(columns_to_drop)}\")\n", "print(f\"Remaining columns: {df.columns}\") # Changed df_cleaned to df since inplace=True modifies df directly" ] }, { "cell_type": "code", "execution_count": null, "id": "744eb515-0dbc-4231-8681-2986890e8b95", "metadata": {}, "outputs": [], "source": [ "##############################################################################" ] }, { "cell_type": "code", "execution_count": null, "id": "a8bc38b9-c17e-4451-8051-df0d1b31bef5", "metadata": {}, "outputs": [], "source": [ "# Visualization" ] }, { "cell_type": "code", "execution_count": 37, "id": "ae90a8d9-d8e0-408b-9b52-2b38e747f962", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of males: 314\n", "Number of females: 577\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_127/4253619690.py:4: 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]) # Assuming 1 is for male\n", "/tmp/ipykernel_127/4253619690.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 females:\", gender_counts[0]) # Assuming 0 is for female\n" ] } ], "source": [ "# Check the gender column\n", "gender_counts = df['Sex'].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": 39, "id": "402f1ef4-c30f-4557-b9bf-0882b20c1bbb", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", " # Create a bar chart\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 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": "4b7ec2ef-153b-4ed2-adbb-3a7c268f7699", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "anaconda-ai-2024.04-py310", "language": "python", "name": "conda-env-anaconda-ai-2024.04-py310-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 5 }