{ "cells": [ { "cell_type": "markdown", "id": "50b46a36-4330-4868-a1d9-ba8db4209eea", "metadata": {}, "source": [ "# Preprocessing \n", "\n", "In this notebook, we prepare the UCI Student Performance dataset for Machine Learning.\n", "We will:\n", "- Identify numerical and categorical features\n", "- Encode categorical variables\n", "- Normalize numeric data \n", "- Create target variables for regression & classification\n", "- Split the data into train/test sets\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "a324a8c7-2ff3-4189-b6e8-d1b7ad7d6705", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "985258ee-a301-482d-ba56-0e4f75876261", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(\"../data/raw/student_performance_full.csv\",sep=\";\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "500db2ef-ea72-4cbc-9d83-da0af17064e2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " school sex age address famsize Pstatus Medu Fedu Mjob Fjob ... \\\n", "0 GP F 18 U GT3 A 4 4 at_home teacher ... \n", "1 GP F 17 U GT3 T 1 1 at_home other ... \n", "2 GP F 15 U LE3 T 1 1 at_home other ... \n", "3 GP F 15 U GT3 T 4 2 health services ... \n", "4 GP F 16 U GT3 T 3 3 other other ... \n", "\n", " famrel freetime goout Dalc Walc health absences G1 G2 G3 \n", "0 4 3 4 1 1 3 4 0 11 11 \n", "1 5 3 3 1 1 3 2 9 11 11 \n", "2 4 3 2 2 3 3 6 12 13 12 \n", "3 3 2 2 1 1 5 0 14 14 14 \n", "4 4 3 2 1 2 5 0 11 13 13 \n", "\n", "[5 rows x 33 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "id": "8038a066-c814-43fe-a6ef-f33654c1d284", "metadata": {}, "source": [ "Identify numerical and categorical features" ] }, { "cell_type": "code", "execution_count": 5, "id": "789e4891-9bdb-4b35-afeb-dcef4a600457", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(Index(['age', 'Medu', 'Fedu', 'traveltime', 'studytime', 'failures', 'famrel',\n", " 'freetime', 'goout', 'Dalc', 'Walc', 'health', 'absences', 'G1', 'G2',\n", " 'G3'],\n", " dtype='object'),\n", " Index(['school', 'sex', 'address', 'famsize', 'Pstatus', 'Mjob', 'Fjob',\n", " 'reason', 'guardian', 'schoolsup', 'famsup', 'paid', 'activities',\n", " 'nursery', 'higher', 'internet', 'romantic'],\n", " dtype='object'))" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numeric_cols = df.select_dtypes(include=['int64', 'float64']).columns\n", "categorical_cols = df.select_dtypes(include=['object']).columns\n", "numeric_cols, categorical_cols" ] }, { "cell_type": "markdown", "id": "9ddb34a2-26da-4b60-96b2-bafc14e966a2", "metadata": {}, "source": [ "Explore Unique Values of Categorical Columns" ] }, { "cell_type": "code", "execution_count": 6, "id": "0feda451-928d-4e67-a68d-5f5f171f2587", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "school : ['GP' 'MS']\n", "sex : ['F' 'M']\n", "address : ['U' 'R']\n", "famsize : ['GT3' 'LE3']\n", "Pstatus : ['A' 'T']\n", "Mjob : ['at_home' 'health' 'other' 'services' 'teacher']\n", "Fjob : ['teacher' 'other' 'services' 'health' 'at_home']\n", "reason : ['course' 'other' 'home' 'reputation']\n", "guardian : ['mother' 'father' 'other']\n", "schoolsup : ['yes' 'no']\n", "famsup : ['no' 'yes']\n", "paid : ['no' 'yes']\n", "activities : ['no' 'yes']\n", "nursery : ['yes' 'no']\n", "higher : ['yes' 'no']\n", "internet : ['no' 'yes']\n", "romantic : ['no' 'yes']\n" ] } ], "source": [ "for col in categorical_cols:\n", " print(col, \":\", df[col].unique())\n" ] }, { "cell_type": "markdown", "id": "2fdfe90f-fb25-42a7-9443-672099faf3d2", "metadata": {}, "source": [ "One-Hot Encoding" ] }, { "cell_type": "code", "execution_count": 7, "id": "1c1a2a4f-a8f9-489a-ad7c-e56d695c632e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " age Medu Fedu traveltime studytime failures famrel freetime goout \\\n", "0 18 4 4 2 2 0 4 3 4 \n", "1 17 1 1 1 2 0 5 3 3 \n", "2 15 1 1 1 2 0 4 3 2 \n", "3 15 4 2 1 3 0 3 2 2 \n", "4 16 3 3 1 2 0 4 3 2 \n", "\n", " Dalc ... guardian_mother guardian_other schoolsup_yes famsup_yes \\\n", "0 1 ... True False True False \n", "1 1 ... False False False True \n", "2 2 ... True False True False \n", "3 1 ... True False False True \n", "4 1 ... False False False True \n", "\n", " paid_yes activities_yes nursery_yes higher_yes internet_yes \\\n", "0 False False True True False \n", "1 False False False True True \n", "2 False False True True True \n", "3 False True True True True \n", "4 False False True True False \n", "\n", " romantic_yes \n", "0 False \n", "1 False \n", "2 False \n", "3 True \n", "4 False \n", "\n", "[5 rows x 42 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_encoded = pd.get_dummies(df, columns=categorical_cols, drop_first=True)\n", "df_encoded.head()\n" ] }, { "cell_type": "markdown", "id": "eaadae83-76fe-4e49-9b42-170b1d6fe255", "metadata": {}, "source": [ "Normalize Numeric Features" ] }, { "cell_type": "code", "execution_count": 9, "id": "5e2c0b1e-bdca-459b-b2a7-de9d36e14f52", "metadata": {}, "outputs": [], "source": [ "scaler = StandardScaler()\n", "df_scaled = df_encoded.copy()\n", "\n", "num_features = df_scaled.select_dtypes(include=['int64', 'float64']).columns\n", "df_scaled[num_features] = scaler.fit_transform(df_scaled[num_features])\n" ] }, { "cell_type": "markdown", "id": "d76079b2-7ee1-497f-9a55-8a9eb5699374", "metadata": {}, "source": [ "Create the Regression Target" ] }, { "cell_type": "code", "execution_count": 10, "id": "20d7b91d-dd80-4138-8707-da21a91cd4ca", "metadata": {}, "outputs": [], "source": [ "target_reg = \"G3\"\n", "X_reg = df_scaled.drop(columns=[\"G3\"])\n", "y_reg = df_scaled[\"G3\"]\n" ] }, { "cell_type": "markdown", "id": "2a161f4c-f000-469a-9709-ba69aebbfab4", "metadata": {}, "source": [ "Create Classification Target (risk / medium / good)" ] }, { "cell_type": "markdown", "id": "a459e55b-dd5d-464b-9450-69cbedab1c52", "metadata": {}, "source": [ "We convert the final grade (G3) into 3 categories:\n", "\n", "- 0–9 → risk\n", "- 10–14 → medium\n", "- 15–20 → good\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "9e823344-4084-46f0-b730-8d480c302e29", "metadata": {}, "outputs": [], "source": [ "def categorize_grade(g):\n", " if g < 10:\n", " return \"risk\"\n", " elif g < 15:\n", " return \"medium\"\n", " else:\n", " return \"good\"\n", "\n", "df_encoded[\"G3_category\"] = df[\"G3\"].apply(categorize_grade)\n", "\n", "X_clf = df_encoded.drop(columns=[\"G3\", \"G3_category\"])\n", "y_clf = df_encoded[\"G3_category\"]\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "9a63651e-12fe-44f1-baf3-2dd9c990953e", "metadata": {}, "outputs": [], "source": [ "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(\n", " X_reg, y_reg, test_size=0.2, random_state=42\n", ")\n", "\n", "X_train_clf, X_test_clf, y_train_clf, y_test_clf = train_test_split(\n", " X_clf, y_clf, test_size=0.2, random_state=42\n", ")\n" ] } ], "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 }