{ "cells": [ { "cell_type": "markdown", "id": "c8092e43-817e-4f02-a762-f356001218a4", "metadata": {}, "source": [ "\n", "# **Practical Machine Learning and Data Exploration**\n", "## Project **1** - **Titanic-Dataset**\n", "### Student: Sara Ismail\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "d55f5f92-175d-4682-a15d-cce3f0a3d9fe", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 4, "id": "938ede9f-c655-4ee1-879d-1df7254f8ce9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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
\n", "
" ], "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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load dataset\n", "df = pd.read_csv(r\"C:\\Users\\someO\\Desktop\\Master\\Practical_Machine_Learning_and_Data_Exploration/Titanic-Dataset.csv\")\n", "# Preview first 5 rows\n", "df.head()" ] }, { "cell_type": "markdown", "id": "7c076076-6a67-4ea8-88ce-091d933f4e46", "metadata": {}, "source": [ "# Identify Data Types of Features\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "7fff15dc-9732-4f93-b3de-9984aa2fa770", "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": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 6, "id": "43789411-da59-4878-9119-6009f15abdcc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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
\n", "
" ], "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": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "id": "5e01a0bd-7dd1-4554-85ea-b9a3b987fd19", "metadata": {}, "source": [ "# Handling missing values\n", "## Count of Missing Values" ] }, { "cell_type": "code", "execution_count": 8, "id": "83758cc7-91d7-414c-94d0-2cd55ed3cf66", "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": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "markdown", "id": "4e723fab-0e34-4360-a8d7-d01da80c6966", "metadata": {}, "source": [ "## Dropping Columns With >30% Missing Data" ] }, { "cell_type": "code", "execution_count": 9, "id": "dbd1691a-1a39-4a7a-be67-fd4d52a29b07", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns dropped: ['Cabin']\n", "---------------------------------\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\n", "columns_to_drop = missing_percentage[missing_percentage > threshold].index\n", "\n", "# Drop columns\n", "df = df.drop(columns=columns_to_drop)\n", "\n", "# Results\n", "print(f\"Columns dropped: {list(columns_to_drop)}\")\n", "print(f\"---------------------------------\")\n", "print(f\"Remaining columns: {df.columns}\")" ] }, { "cell_type": "markdown", "id": "bf6de515-f0cf-4056-804a-34cf14cf9c21", "metadata": {}, "source": [ "## Pre-processing on dataset" ] }, { "cell_type": "code", "execution_count": 10, "id": "9614447c-9bc2-4bff-93e9-0786d8b55854", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarked
0103108122.0105237.25002
1211190038.01059671.28330
2313353026.0006697.92502
3411272035.0104953.10002
450315135.0004728.05002
\n", "
" ], "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": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "label_encoder = LabelEncoder()\n", "\n", "for column in df.select_dtypes(include=['object']).columns:\n", " df[column] = label_encoder.fit_transform(df[column])\n", "\n", "df.head()" ] }, { "cell_type": "markdown", "id": "eb113e1b-cecb-4cd3-9272-2b98c6c6e51d", "metadata": {}, "source": [ "## Handling Nulls Based on Data Type" ] }, { "cell_type": "code", "execution_count": 12, "id": "674dbf3a-319f-4fc8-9b8d-b72a897565ae", "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": 12, "metadata": {}, "output_type": "execute_result" } ], "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", "\n", "# 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", "\n", "#Recount missing values\n", "df.isnull().sum()\n" ] }, { "cell_type": "markdown", "id": "042f6b51-a411-4d96-b3d3-b40acca65cf4", "metadata": {}, "source": [ "# Handling Qutliers" ] }, { "cell_type": "markdown", "id": "2a60efff-d59b-4388-b70a-be52dc0047e0", "metadata": {}, "source": [ "## Detect Outliers\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "89453d16-5698-478b-8f44-08d34e3bc48c", "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 130\n", "SibSp 46\n", "Parch 213\n", "Ticket 0\n", "Fare 146\n", "Embarked 0\n", "dtype: int64\n" ] } ], "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", "\n", "\n", "# Replace outliers with NaN for numiric feature\n", "for col in df.select_dtypes(include='number').columns:\n", " df[col] = replace_outliers_with_nan(df[col])\n", "\n", "# Recount missing values\n", "null_counts = df.isnull().sum()\n", "print(\"Sum of null values in each column:\")\n", "print(null_counts)\n" ] }, { "cell_type": "markdown", "id": "d7eeb524-1e25-4260-8d98-34d4f3664de9", "metadata": {}, "source": [ "# Re-handling missing values" ] }, { "cell_type": "code", "execution_count": 20, "id": "dc23cca2-aa92-4d68-b8ea-dbff7c4d62bd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns dropped: []\n", "---------------------------------\n", "Remaining columns: Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n", " 'Parch', 'Ticket', 'Fare', 'Embarked'],\n", " dtype='object')\n", "---------------------------------\n", "Count of Missing Values\n" ] }, { "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": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "## Dropping Columns With >30% Missing Data\n", "\n", "missing_percentage = df.isnull().mean() * 100\n", "threshold = 30\n", "# Filter columns\n", "columns_to_drop = missing_percentage[missing_percentage > threshold].index\n", "\n", "# Drop columns\n", "df = df.drop(columns=columns_to_drop)\n", "\n", "# Results\n", "print(f\"Columns dropped: {list(columns_to_drop)}\")\n", "print(f\"---------------------------------\")\n", "print(f\"Remaining columns: {df.columns}\")\n", "\n", "#------------------------------------------------------------------------------#\n", "\n", "## Handling Nulls Based on Data Type\n", "\n", "# 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", "\n", "# 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", "\n", "#Recount missing values\n", "print(f\"---------------------------------\")\n", "print(f\"Count of Missing Values\")\n", "df.isnull().sum()\n" ] }, { "cell_type": "markdown", "id": "54d70ba9-714e-40d5-bb07-eba0ccf74af0", "metadata": {}, "source": [ "# Data Visualization" ] }, { "cell_type": "code", "execution_count": null, "id": "92b5689e-6b60-483e-b656-e8bb42bb89f3", "metadata": {}, "outputs": [], "source": [ "from matplotlib import pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "id": "75e9c954-a96e-4d96-9c32-510f2be9c1af", "metadata": {}, "source": [ "## Number of Males and Females in Titanic Dataset" ] }, { "cell_type": "code", "execution_count": 39, "id": "cb6547c4-bdd2-4e13-bd6a-05c8aabf27f5", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 5))\n", "gender_counts = df['Sex'].value_counts()\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 Titanic Dataset')\n", "plt.xlabel('Gender')\n", "plt.ylabel('Count')\n", "plt.grid(axis='y')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "97f521d7-fde6-4f66-8fc7-7dc754aa3050", "metadata": {}, "source": [ "## Fare Distribution by Age" ] }, { "cell_type": "code", "execution_count": 45, "id": "1c93dc2f-a51d-4d2f-b9b1-830de6dc0d89", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x='Age', y='Fare', data=df)\n", "plt.title(\"Fare Distribution by Age\")\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "7f1d87af-3f33-4da0-b0d5-2e16c2075c61", "metadata": {}, "source": [ "## Siblings - Spouse" ] }, { "cell_type": "code", "execution_count": 44, "id": "10dad7a8-6fcf-40d7-b4f7-d200fbf92cfe", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['SibSp'].hist(bins=20, color='skyblue', edgecolor='black')\n", "plt.title(\" Siblings and Spouse Distribution\")\n", "plt.xlabel(\"SibSp\")\n", "plt.ylabel(\"Frequency\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "748fd7b6-e7ae-4712-bacd-d7020eec940d", "metadata": {}, "source": [ "## Survived Distribution" ] }, { "cell_type": "code", "execution_count": 36, "id": "7fbd0736-f436-4648-af3c-87e309016589", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "survived_counts = df['Survived'].value_counts()\n", "plt.figure(figsize=(8, 5))\n", "plt.pie(survived_counts, labels=['Not Survived', 'Survived'], autopct='%1.1f%%', colors=['darkred', 'skyblue'])\n", "plt.title('Survived Distribution')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "99b81ecb-6fba-4143-a174-998211a4cb22", "metadata": {}, "source": [ "## Age vs Survived" ] }, { "cell_type": "code", "execution_count": 38, "id": "1370f2c5-d5fe-41b7-a969-f0473b426ce4", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(df['Age'], df['Survived'], alpha=0.6)\n", "plt.xlabel(\"Age\")\n", "plt.ylabel(\"Survived\")\n", "plt.title(\"Age vs Survived\")\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "4d40e693-95f7-45dc-ac9c-7cafbc4ae71f", "metadata": {}, "source": [ "## Passenger Class Distribution" ] }, { "cell_type": "code", "execution_count": 40, "id": "9757a274-77c5-4d5f-92fe-46724ccbe39e", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x='Pclass', data=df)\n", "plt.title(\"Distribution of Pclass\")\n", "plt.show()" ] } ], "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 }