{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "d7c317cb-32ec-45fb-a99f-ae01c92f3c61", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "id": "2d738dc1-3d5d-431c-99c4-3dbf44307f96", "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": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load your dataset into a DataFrame\n", "df = pd.read_csv('Titanic-Dataset.csv')\n", "# Preview the first 5 rows\n", "df.head()\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "7547610a-19cd-4479-9ecb-f80f5ee55aa5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PassengerId int64\n", "Survived int64\n", "Pclass int64\n", "Name object\n", "Sex object\n", "Age float64\n", "SibSp int64\n", "Parch int64\n", "Ticket object\n", "Fare float64\n", "Cabin object\n", "Embarked object\n", "dtype: object" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "c52ff2a8-d335-4837-8b56-2113c7625f96", "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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "f719a307-1728-4abc-b42c-c7479819156f", "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": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "ab76df23-82b8-4a79-9e0f-cc18841c8121", "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 = 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.columns}\")\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "2082e253-ab30-4b69-8e48-6c6a43fb2026", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarked
0103108122.0105237.25002
1211190038.01059671.28330
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" ], "text/plain": [ " PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket \\\n", "0 1 0 3 108 1 22.0 1 0 523 \n", "1 2 1 1 190 0 38.0 1 0 596 \n", "2 3 1 3 353 0 26.0 0 0 669 \n", "3 4 1 1 272 0 35.0 1 0 49 \n", "4 5 0 3 15 1 35.0 0 0 472 \n", "\n", " Fare Embarked \n", "0 7.2500 2 \n", "1 71.2833 0 \n", "2 7.9250 2 \n", "3 53.1000 2 \n", "4 8.0500 2 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "label_encoder = LabelEncoder()\n", "for column in df.select_dtypes(include=['object']).columns:\n", " df[column] = label_encoder.fit_transform(df[column])\n", "df.head()\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "0d926201-1445-47dd-8691-b2a83632752b", "metadata": {}, "outputs": [], "source": [ "# Fill missing values in columns with mode\n", "for column in df.select_dtypes(include=['float64']).columns:\n", " mean_value = df[column].mean() # Get the mode (most frequent value)\n", " df[column] = df[column].fillna(mean_value)\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "b27a5b66-2cb1-4c3e-a05a-546f9e38f442", "metadata": {}, "outputs": [], "source": [ "# Fill missing values in categorical columns with mode\n", "for column in df.select_dtypes(include=['object']).columns:\n", " mode_value = df[column].mode()[0] # Get the mode (most frequent value)\n", " df[column] = df[column].fillna(mode_value)\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "0db97993-2bdc-41a8-812d-2a6e8780618a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PassengerId 0\n", "Survived 0\n", "Pclass 0\n", "Name 0\n", "Sex 0\n", "Age 0\n", "SibSp 0\n", "Parch 0\n", "Ticket 0\n", "Fare 0\n", "Embarked 0\n", "dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 11, "id": "ea56157a-1abe-498d-945f-1d9ef35da70e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarked
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1211190038.0000001059671.28330
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....................................
88688702548127.0000000010113.00002
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891 rows × 11 columns

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" ], "text/plain": [ " PassengerId Survived Pclass Name Sex Age SibSp Parch \\\n", "0 1 0 3 108 1 22.000000 1 0 \n", "1 2 1 1 190 0 38.000000 1 0 \n", "2 3 1 3 353 0 26.000000 0 0 \n", "3 4 1 1 272 0 35.000000 1 0 \n", "4 5 0 3 15 1 35.000000 0 0 \n", ".. ... ... ... ... ... ... ... ... \n", "886 887 0 2 548 1 27.000000 0 0 \n", "887 888 1 1 303 0 19.000000 0 0 \n", "888 889 0 3 413 0 29.699118 1 2 \n", "889 890 1 1 81 1 26.000000 0 0 \n", "890 891 0 3 220 1 32.000000 0 0 \n", "\n", " Ticket Fare Embarked \n", "0 523 7.2500 2 \n", "1 596 71.2833 0 \n", "2 669 7.9250 2 \n", "3 49 53.1000 2 \n", "4 472 8.0500 2 \n", ".. ... ... ... \n", "886 101 13.0000 2 \n", "887 14 30.0000 2 \n", "888 675 23.4500 2 \n", "889 8 30.0000 0 \n", "890 466 7.7500 1 \n", "\n", "[891 rows x 11 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 12, "id": "2a859a46-5aee-425b-910a-6104e603b573", "metadata": {}, "outputs": [], "source": [ "def replace_outliers_with_nan(column):\n", " Q1 = column.quantile(0.25)\n", " Q3 = column.quantile(0.75)\n", " IQR = Q3 - Q1\n", " lower_bound = Q1 - 1.5 * IQR\n", " upper_bound = Q3 + 1.5 * IQR\n", " # Replace outliers with NaN\n", " return column.where((column >= lower_bound) & (column <= upper_bound), np.nan)\n", "# Replace outliers with NaN for each feature\n", "for col in df.columns:\n", " df[col] = replace_outliers_with_nan(df[col])\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "ea1d8bc9-cac9-4a46-be95-53acf3ad7b09", "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 66\n", "SibSp 46\n", "Parch 213\n", "Ticket 0\n", "Fare 116\n", "Embarked 0\n", "dtype: int64\n" ] } ], "source": [ "null_counts = df.isnull().sum()\n", "print(\"Sum of null values in each column:\")\n", "print(null_counts)" ] }, { "cell_type": "code", "execution_count": 16, "id": "208e6d12-8492-452b-bfbf-1c1ca800e032", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# 1. Distribution of Age\n", "plt.figure(figsize=(8, 6))\n", "sns.histplot(df['Age'], bins=15, kde=True, color='blue')\n", "plt.title('Age Distribution')\n", "plt.xlabel('Age')\n", "plt.ylabel('Frequency')\n", "plt.show() " ] }, { "cell_type": "code", "execution_count": 33, "id": "cf2f649d-2ea4-4aae-8278-769cb334b273", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_17944\\249984247.py:3: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.countplot(x='Sex', data=df, palette='Set2')\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 3. Countplot for Classification\n", "plt.figure(figsize=(8, 6))\n", "sns.countplot(x='Sex', data=df, palette='Set2')\n", "plt.title('Count by Sex')\n", "plt.xlabel('sex')\n", "plt.ylabel('Count')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 26, "id": "a9647600-eec2-468f-892e-b80be5967cd8", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 6))\n", "sns.scatterplot(x='Age', y='SibSp', data=df, hue='SibSp', palette='Set1')\n", "plt.title('Age vs SibSp')\n", "plt.xlabel('Age')\n", "plt.ylabel('SibSp')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 30, "id": "e19d2e92-75df-48f8-bbd0-6097d6ed87dd", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_17944\\564524713.py:2: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.boxplot(x='Embarked', y='Sex', data=df, palette='Set1')\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 6))\n", "sns.boxplot(x='Embarked', y='Sex', data=df, palette='Set1')\n", "plt.title('Embarked by Sex')\n", "plt.xlabel('Embarked')\n", "plt.ylabel('Sex')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "c8c90d3a-b441-4873-9ece-97a64a8561af", "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": "3.12.11" } }, "nbformat": 4, "nbformat_minor": 5 }