{ "cells": [ { "cell_type": "markdown", "id": "310c258e", "metadata": {}, "source": [ "# Machine Learning Activity: Feature Selection and Model Comparison\n", "\n", "In this notebook, you will train a **Random Forest classifier** using the **Diabetes** or **Titanic** dataset.\n", "You will analyze the effect of **feature selection** and compare **Random Forest, Logistic Regression, and Naïve Bayes classifiers**.\n", "\n", "## Objectives\n", "1. Train a **Random Forest classifier**.\n", "2. Investigate feature selection using:\n", " - **Manual feature importance analysis**.\n", " - **Principal Component Analysis (PCA)**.\n", "3. Train **Logistic Regression** and **Naïve Bayes** for comparison.\n", "4. Evaluate models using **accuracy, precision, recall, and F1-score**.\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "f77f9100", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 891 entries, 0 to 890\n", "Data columns (total 12 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 PassengerId 891 non-null int64 \n", " 1 Survived 891 non-null int64 \n", " 2 Pclass 891 non-null int64 \n", " 3 Name 891 non-null object \n", " 4 Sex 891 non-null object \n", " 5 Age 714 non-null float64\n", " 6 SibSp 891 non-null int64 \n", " 7 Parch 891 non-null int64 \n", " 8 Ticket 891 non-null object \n", " 9 Fare 891 non-null float64\n", " 10 Cabin 204 non-null object \n", " 11 Embarked 889 non-null object \n", "dtypes: float64(2), int64(5), object(5)\n", "memory usage: 83.7+ KB\n", "None\n" ] }, { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund\\t Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings\\t Mrs. John Bradley (Florence Briggs T...female38.010PC 1759971.2833C85C
2313Heikkinen\\t Miss Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle\\t Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen\\t 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\\t Mr. Owen Harris male 22.0 1 \n", "1 Cumings\\t Mrs. John Bradley (Florence Briggs T... female 38.0 1 \n", "2 Heikkinen\\t Miss Laina female 26.0 0 \n", "3 Futrelle\\t Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen\\t 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": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.decomposition import PCA\n", "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n", "\n", "# Load dataset (Uncomment the one you want to use)\n", "#df = pd.read_csv('diabetes.csv') # Diabetes dataset\n", "df_titanic= pd.read_csv('titanic.csv') # Titanic dataset\n", "\n", "# Display basic info\n", "print(df_titanic.info())\n", "\n", "df_titanic.head()\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "ef428d8d", "metadata": {}, "outputs": [], "source": [ "# # # Handle missing values (if any)\n", "# df.dropna(inplace=True)\n", "\n", "# # Encode categorical features (if using Titanic dataset)\n", "# if 'Sex' in df.columns:\n", "# df['Sex'] = LabelEncoder().fit_transform(df['Sex'])\n", "\n", "# # Define features and target\n", "# X = df.drop(columns=['Outcome']) if 'Outcome' in df.columns else df.drop(columns=['Survived'])\n", "# y = df['Outcome'] if 'Outcome' in df.columns else df['Survived']\n", "\n", "# # Split data\n", "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n" ] }, { "cell_type": "code", "execution_count": 69, "id": "f90a8350-7a25-4b22-824f-915de56cb775", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Before ============ ::\n", "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\n", "\n", "After ============ :\n", "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\n", "===============================\n", "\n", "Index: 712 entries, 0 to 890\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 PassengerId 712 non-null int64 \n", " 1 Survived 712 non-null int64 \n", " 2 Pclass 712 non-null int64 \n", " 3 Name 712 non-null object \n", " 4 Sex 712 non-null int64 \n", " 5 Age 712 non-null float64\n", " 6 SibSp 712 non-null int64 \n", " 7 Parch 712 non-null int64 \n", " 8 Ticket 712 non-null object \n", " 9 Fare 712 non-null float64\n", " 10 Embarked 712 non-null float64\n", "dtypes: float64(3), int64(6), object(2)\n", "memory usage: 66.8+ KB\n", "None\n", "===============================\n", "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n", " 'Parch', 'Ticket', 'Fare', 'Embarked'],\n", " dtype='object')\n" ] } ], "source": [ "## Check for missing \n", "print(\"Before ============ ::\")\n", "print(df_titanic.isna().sum())\n", "\n", "# Delete the column \"Cabin\", which has 687 out of 890 missing.\n", "df_titanic.drop(\"Cabin\", axis=1, inplace=True)\n", "\n", "## Delete the rows with missing values for \"Embarked\"\n", "df_titanic.dropna(subset=[\"Embarked\"], axis=0, how=\"all\", inplace=True)\n", "\n", "\n", "## Delete the rows with missing values for \"Age\"\n", "df_titanic.dropna(subset=[\"Age\"], axis=0, how=\"all\", inplace=True)\n", "\n", "\n", "print(\"\\nAfter ============ :\")\n", "print(df_titanic.isna().sum())\n", "\n", "print(\"===============================\")\n", "\n", "print(df_titanic.info())\n", "\n", "print(\"===============================\")\n", "print(df_titanic.columns)" ] }, { "cell_type": "code", "execution_count": 97, "id": "d5c02e35-efdc-48fb-b731-2d8964dce815", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2. 0. 1.]\n" ] }, { "data": { "text/html": [ "
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0103Braund\\t Mr. Owen Harris022.010A/5 211717.25002.0
1211Cumings\\t Mrs. John Bradley (Florence Briggs T...138.010PC 1759971.28330.0
2313Heikkinen\\t Miss Laina126.000STON/O2. 31012827.92502.0
3411Futrelle\\t Mrs. Jacques Heath (Lily May Peel)135.01011380353.10002.0
4503Allen\\t Mr. William Henry035.0003734508.05002.0
....................................
88588603Rice\\t Mrs. William (Margaret Norton)139.00538265229.12501.0
88688702Montvila\\t Rev. Juozas027.00021153613.00002.0
88788811Graham\\t Miss Margaret Edith119.00011205330.00002.0
88989011Behr\\t Mr. Karl Howell026.00011136930.00000.0
89089103Dooley\\t Mr. Patrick032.0003703767.75001.0
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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", "885 886 0 3 \n", "886 887 0 2 \n", "887 888 1 1 \n", "889 890 1 1 \n", "890 891 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund\\t Mr. Owen Harris 0 22.0 1 \n", "1 Cumings\\t Mrs. John Bradley (Florence Briggs T... 1 38.0 1 \n", "2 Heikkinen\\t Miss Laina 1 26.0 0 \n", "3 Futrelle\\t Mrs. Jacques Heath (Lily May Peel) 1 35.0 1 \n", "4 Allen\\t Mr. William Henry 0 35.0 0 \n", ".. ... ... ... ... \n", "885 Rice\\t Mrs. William (Margaret Norton) 1 39.0 0 \n", "886 Montvila\\t Rev. Juozas 0 27.0 0 \n", "887 Graham\\t Miss Margaret Edith 1 19.0 0 \n", "889 Behr\\t Mr. Karl Howell 0 26.0 0 \n", "890 Dooley\\t Mr. Patrick 0 32.0 0 \n", "\n", " Parch Ticket Fare Embarked \n", "0 0 A/5 21171 7.2500 2.0 \n", "1 0 PC 17599 71.2833 0.0 \n", "2 0 STON/O2. 3101282 7.9250 2.0 \n", "3 0 113803 53.1000 2.0 \n", "4 0 373450 8.0500 2.0 \n", ".. ... ... ... ... \n", "885 5 382652 29.1250 1.0 \n", "886 0 211536 13.0000 2.0 \n", "887 0 112053 30.0000 2.0 \n", "889 0 111369 30.0000 0.0 \n", "890 0 370376 7.7500 1.0 \n", "\n", "[712 rows x 11 columns]" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Replace 'male' with 0 and 'female' with 1\n", "df_titanic['Sex'] = df_titanic['Sex'].replace({'male': 0, 'female': 1})\n", "\n", "# Get all the unique values in the Embark column.\n", "print(df_titanic['Embarked'].unique())\n", "\n", "# Replace the values in Embark with numeric ones\n", "df_titanic['Embarked'] = df_titanic['Embarked'].replace({'C': 0, 'Q': 1, 'S': 2})\n", "df_titanic" ] }, { "cell_type": "code", "execution_count": 14, "id": "803835ae-24cb-4cde-b2fa-4110eff3c7fc", "metadata": {}, "outputs": [], "source": [ "# split the data into train and test using the SciKitLearn package\n", "\n", "# specify the target variables (dependent/predicted): \"y\"\n", "y = df_titanic['Survived']\n", "\n", "# Drop the 'y' column and unwanted columns i.e. the predictors (a.k.a. independent) variables : \"x\"\n", "X = df_titanic.drop(['Survived','PassengerId', 'Name', 'Ticket'],axis=1)\n", "\n", "\n", "# split the data into train and test using the SciKitLearn package \"train_test_split\"\n", "X_train, X_test, y_train, y_test = train_test_split(X, y , test_size=0.25, random_state=104) " ] }, { "cell_type": "code", "execution_count": 30, "id": "36a74543-e4a8-452f-8def-2c15aa81d8ee", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PclassSexAgeSibSpParchFareEmbarked
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" ], "text/plain": [ " Pclass Sex Age SibSp Parch Fare Embarked\n", "421 3 0 21.00 0 0 7.7333 1\n", "78 2 0 0.83 0 2 29.0000 2\n", "860 3 0 41.00 2 0 14.1083 2\n", "607 1 0 27.00 0 0 30.5000 2\n", "804 3 0 27.00 0 0 6.9750 2\n", ".. ... ... ... ... ... ... ...\n", "209 1 0 40.00 0 0 31.0000 0\n", "819 3 0 10.00 3 2 27.9000 2\n", "315 3 1 26.00 0 0 7.8542 2\n", "883 2 0 28.00 0 0 10.5000 2\n", "91 3 0 20.00 0 0 7.8542 2\n", "\n", "[534 rows x 7 columns]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train" ] }, { "cell_type": "code", "execution_count": 32, "id": "fba7f053-b147-4edb-a642-f14258d609b7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "421 0\n", "78 1\n", "860 0\n", "607 1\n", "804 1\n", " ..\n", "209 1\n", "819 0\n", "315 1\n", "883 0\n", "91 0\n", "Name: Survived, Length: 534, dtype: int64" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train" ] }, { "cell_type": "code", "execution_count": 46, "id": "282e91c1-9453-426b-a89d-aa0b5a00c388", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "The accuracy score for RandomForest: 0.8089887640449438\n", "\n", "============== Metric ===============================\n", "\n", " precision recall f1-score support\n", "\n", " 0 0.88 0.82 0.85 114\n", " 1 0.71 0.80 0.75 64\n", "\n", " accuracy 0.81 178\n", " macro avg 0.79 0.81 0.80 178\n", "weighted avg 0.82 0.81 0.81 178\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix\n", "\n", "\n", "# make a copy of the algorithm\n", "RF_clf = RandomForestClassifier(random_state=42)\n", "\n", "# fits a model using training data\n", "RF_clf.fit(X_train, y_train)\n", "\n", "\n", "# check with test data\n", "y_pred = RF_clf.predict(X_test)\n", "\n", "# Print the accuracy score\n", "print('\\nThe accuracy score for RandomForest: ', accuracy_score(y_test, y_pred))\n", "\n", "print(\"\\n============== Metric ===============================\\n\")\n", "# Print the evaluation metrics\n", "print(classification_report(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 48, "id": "0b428f41-6e0f-4829-bfa6-af20d34ecf78", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.10698919, 0.22239287, 0.28488012, 0.05249737, 0.04421786,\n", " 0.26067288, 0.02834971])" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RF_clf.feature_importances_" ] }, { "cell_type": "code", "execution_count": 50, "id": "14bd53fe-a3e8-473c-a1e4-270ab71a0aee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked'], dtype='object')" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.columns" ] }, { "cell_type": "code", "execution_count": 52, "id": "f432df54-595b-4dca-adda-0667e2de7602", "metadata": {}, "outputs": [], "source": [ "# Reduce unimportant features\n", "# split the data into train and test using the SciKitLearn package\n", "\n", "# specify the target variables (dependent/predicted): \"y\"\n", "y = df_titanic['Survived']\n", "\n", "# Drop the 'y' column and unwanted columns i.e. the predictors (a.k.a. independent) variables : \"x\"\n", "X = df_titanic.drop(['Survived','PassengerId', 'Name', 'Ticket', 'Embarked', 'Parch'],axis=1)\n", "\n", "\n", "# split the data into train and test using the SciKitLearn package \"train_test_split\"\n", "X_train, X_test, y_train, y_test = train_test_split(X, y , test_size=0.25, random_state=104) " ] }, { "cell_type": "code", "execution_count": null, "id": "5ff2ce9d-1f1d-4189-ab66-e164bd040cda", "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "le = LabelEncoder()\n", "X_train['your_column'] = le.fit_transform(X_train['your_column'])" ] }, { "cell_type": "code", "execution_count": 77, "id": "e27b100a-c6fa-4b69-a7df-8b6a5794d5dd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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668 rows × 6 columns

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" ], "text/plain": [ " Pclass Sex Age SibSp Fare Cabin\n", "888 3 1 NaN 1 23.4500 NaN\n", "394 3 1 24.0 0 16.7000 G6\n", "84 2 1 17.0 0 10.5000 NaN\n", "689 1 1 15.0 0 211.3375 B5\n", "862 1 1 48.0 0 25.9292 D17\n", ".. ... ... ... ... ... ...\n", "654 3 1 18.0 0 6.7500 NaN\n", "251 3 1 29.0 1 10.4625 G6\n", "729 3 1 25.0 1 7.9250 NaN\n", "705 2 0 39.0 0 26.0000 NaN\n", "69 3 0 26.0 2 8.6625 NaN\n", "\n", "[668 rows x 6 columns]" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train" ] }, { "cell_type": "code", "execution_count": 95, "id": "0ed0f9ae-2177-454d-aeee-1e7048b4fb0d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "The accuracy score for RandomForest: 0.7937219730941704\n", "\n", "============== Metric ===============================\n", "\n", " precision recall f1-score support\n", "\n", " 0 0.83 0.86 0.84 145\n", " 1 0.72 0.67 0.69 78\n", "\n", " accuracy 0.79 223\n", " macro avg 0.78 0.76 0.77 223\n", "weighted avg 0.79 0.79 0.79 223\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix\n", "\n", "\n", "# make a copy of the algorithm\n", "RF_clf = RandomForestClassifier(random_state=42)\n", "# fits a model using training data\n", "RF_clf.fit(X_train, y_train)\n", "\n", "X_train, X_test = X_train.align(X_test, join='left', axis=1, fill_value=0)\n", "# check with test data\n", "y_pred = RF_clf.predict(X_test)\n", "\n", "# Print the accuracy score\n", "print('\\nThe accuracy score for RandomForest: ', accuracy_score(y_test, y_pred))\n", "\n", "print(\"\\n============== Metric ===============================\\n\")\n", "# Print the evaluation metrics\n", "print(classification_report(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": null, "id": "27e04ac0-7e8d-476c-b849-7122292228b5", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:base] *", "language": "python", "name": "conda-base-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": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }