feat: initial FDP content - Data Science Pipeline with Colab notebook

2026-05-29 23:17:47 +05:30
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 # FDP - Data Science Pipeline: End-to-End Workflow
 **Topic:** Data Science Pipeline | **Host:** Jodhpur Institute of Engineering & Technology (JIET)
 ## 📂 Contents
 | File | Description |
 |------|-------------|
 | `fdp-colab-content/student_stress_data.csv` | Synthetic dataset of 300 student records for stress level prediction |
 | `fdp-colab-content/FDP_Student_Stress_Prediction.ipynb` | Google Colab notebook — zero setup required |
 | `fdp-colab-content/FDP_PreRead_StudentStress.pdf` | Pre-read material for FDP attendees |
 | `fdp-colab-content/generate_dataset.py` | Dataset generator script |
 | `fdp-colab-content/pre_read.html` | Pre-read material source (HTML) |
 ## 🎯 Dataset Overview
 **300 student records × 8 columns**
 - **Target:** `Stress_Level` (0 = Not Stressed, 1 = Stressed)
 - **Features:** Department, Attendance_Level, Favorite_Color, Weekly_Study_Hours, Assignment_Submission_Pct, Backlogs_Count
 ## 🚀 Using the Colab Notebook
 1. Open the notebook in [Google Colab](https://colab.research.google.com/)
 2. Run all cells — no installation needed
 3. Dataset auto-loads from raw GitHub URL
 ## 📊 Pipeline Steps
 1. Data Loading
 2. Exploratory Data Analysis
 3. One-Hot Encoding (Department, Attendance_Level, Favorite_Color)
 4. Random Forest Classification
 5. Model Evaluation
 6. Feature Importance Visualization
 ---
 *Part of Gyannetra's FDP workshop series*
--- a/fdp-colab-content/FDP_PreRead_StudentStress.pdf
+++ b/fdp-colab-content/FDP_PreRead_StudentStress.pdf
--- a/fdp-colab-content/FDP_Student_Stress_Prediction.ipynb
+++ b/fdp-colab-content/FDP_Student_Stress_Prediction.ipynb
@@ -0,0 +1,486 @@
 {
 "nbformat": 4,
 "nbformat_minor": 5,
 "metadata": {
  "name": "FDP_Student_Stress_Prediction",
  "kernelspec": { "name": "python3", "display_name": "Python 3" },
  "language_info": { "name": "python", "version": "3.10.0" },
  "accelerator": "CPU",
  "colab": { "provenance": [] }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🎓 FDP: Student Stress Level Prediction\n",
    "## Data Science Pipeline — Hands-on with Google Colab\n",
    "\n",
    "**Speaker:** Kapil Panwar | **Gyannetra Pvt Ltd**\n",
    "\n",
    "---\n",
    "### 🔍 What We'll Do Today:\n",
    "1. Load & explore a college student dataset\n",
    "2. Handle categorical data using **One-Hot Encoding** (including Favorite Colors! 🌈)\n",
    "3. Train a Machine Learning model to predict student stress\n",
    "4. Evaluate how accurate our model is\n",
    "5. Interpret results\n",
    "\n",
    "> **No installation needed. Just press Shift+Enter on each cell!**"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Cell 1: Import Libraries\n",
    "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.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "\n",
    "print(\"✅ All libraries loaded successfully!\")\n",
    "print(f\"pandas: {pd.__version__}\")\n",
    "print(f\"numpy: {np.__version__}\")"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 1: Load the Dataset 📂\n",
    "\n",
    "We're using a dataset of **300 college students**. It contains information about their department,\n",
    "attendance, favorite color, study habits, and whether they have high stress or not."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Cell 2: Load Data\n",
    "# Option A: From GitHub (recommended)\n",
    "url = \"https://raw.githubusercontent.com/gnpl_admin/fdp-data-science-pipeline/main/fdp-colab-content/student_stress_data.csv\"\n",
    "\n",
    "# Option B: If you have the file locally (upload to Colab)\n",
    "# from google.colab import files\n",
    "# uploaded = files.upload()\n",
    "# df = pd.read_csv('student_stress_data.csv')\n",
    "\n",
    "df = pd.read_csv(url)\n",
    "print(f\"✅ Dataset loaded! Shape: {df.shape[0]} rows × {df.shape[1]} columns\")\n",
    "df.head(10)"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 📊 What does each column mean?\n",
    "\n",
    "| Column | Type | Description |\n",
    "|--------|------|-------------|\n",
    "| Student_ID | ID | Unique student identifier |\n",
    "| Department | Categorical | Science, Commerce, Arts, Engineering |\n",
    "| Attendance_Level | Categorical | Regular, Irregular, Borderline |\n",
    "| Favorite_Color | Categorical | Red, Blue, Green, Yellow, Purple, Orange |\n",
    "| Weekly_Study_Hours | Numerical | Hours studied per week (2-25) |\n",
    "| Assignment_Submission_Pct | Numerical | Assignment submission rate (40-100%) |\n",
    "| Backlogs_Count | Numerical | Number of pending backlogs (0-5) |\n",
    "| Stress_Level | **Target** | 0 = Low Stress, 1 = High Stress |"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Cell 3: Explore Data Types & Missing Values\n",
    "print(\"📋 Data Info:\")\n",
    "print(\"-\" * 40)\n",
    "df.info()\n",
    "print()\n",
    "print(\"📊 Missing values:\")\n",
    "print(df.isnull().sum())\n",
    "print()\n",
    "print(\"✅ No missing values! Clean data.\")"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Cell 4: Statistical Summary\n",
    "print(\"📈 Statistical Summary (Numerical Columns):\")\n",
    "print(\"-\" * 40)\n",
    "df.describe()"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 2: Exploratory Data Analysis (EDA) 🔍\n",
    "\n",
    "Let's understand our data before building a model."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Cell 5: Target Distribution\n",
    "stress_counts = df['Stress_Level'].value_counts()\n",
    "print(\"🎯 Stress Level Distribution:\")\n",
    "print(f\"Low Stress (0): {stress_counts[0]} students\")\n",
    "print(f\"High Stress (1): {stress_counts[1]} students\")\n",
    "print()\n",
    "\n",
    "# Simple bar chart\n",
    "plt.figure(figsize=(6, 4))\n",
    "colors = ['#4CAF50', '#F44336']\n",
    "bars = plt.bar(['Low Stress (0)', 'High Stress (1)'], [stress_counts[0], stress_counts[1]], color=colors)\n",
    "plt.title('Student Stress Level Distribution', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('Number of Students')\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    plt.text(bar.get_x() + bar.get_width()/2, height, f'{int(height)}', ha='center', va='bottom')\n",
    "plt.show()"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Cell 6: Favorite Color Distribution\n",
    "print(\"🌈 Favorite Color Distribution:\")\n",
    "color_counts = df['Favorite_Color'].value_counts()\n",
    "print(color_counts)\n",
    "print()\n",
    "\n",
    "# Bar chart with actual colors\n",
    "plt.figure(figsize=(8, 4))\n",
    "color_map = {'Red': '#FF0000', 'Blue': '#2196F3', 'Green': '#4CAF50',\n",
    "             'Yellow': '#FFEB3B', 'Purple': '#9C27B0', 'Orange': '#FF9800'}\n",
    "bar_colors = [color_map[c] for c in color_counts.index]\n",
    "bars = plt.bar(color_counts.index, color_counts.values, color=bar_colors, edgecolor='black', linewidth=0.5)\n",
    "plt.title('Favorite Color Distribution', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('Number of Students')\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    plt.text(bar.get_x() + bar.get_width()/2, height, f'{int(height)}', ha='center', va='bottom')\n",
    "plt.show()"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Cell 7: Department vs Stress Level\n",
    "print(\"🏫 Stress by Department:\")\n",
    "dept_stress = pd.crosstab(df['Department'], df['Stress_Level'])\n",
    "print(dept_stress)\n",
    "print()\n",
    "\n",
    "# Stacked bar chart\n",
    "dept_stress.plot(kind='bar', stacked=True, color=['#4CAF50', '#F44336'], figsize=(8, 4))\n",
    "plt.title('Stress Level by Department', fontsize=14, fontweight='bold')\n",
    "plt.xlabel('Department')\n",
    "plt.ylabel('Number of Students')\n",
    "plt.legend(['Low Stress', 'High Stress'])\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 3: Feature Engineering — One-Hot Encoding 🔄\n",
    "\n",
    "### 🎯 Why Do We Need This?\n",
    "\n",
    "Machine learning models understand **numbers**, not words like \"Red\" or \"Science\".\n",
    "But we can't just assign Red=1, Blue=2 because that implies an order (Blue > Red).\n",
    "\n",
    "### ✅ Solution: One-Hot Encoding\n",
    "\n",
    "Creates a separate column for each category with 0 or 1.\n",
    "\n",
    "**Example — Favorite Color encoding:**\n",
    "\n",
    "| Original | Red | Blue | Green | Yellow | Purple | Orange |\n",
    "|----------|-----|------|-------|--------|--------|--------|\n",
    "| Red      | 1   | 0    | 0     | 0      | 0      | 0      |\n",
    "| Blue     | 0   | 1    | 0     | 0      | 0      | 0      |\n",
    "| Green    | 0   | 0    | 1     | 0      | 0      | 0      |\n",
    "\n",
    "**Think of it like a light switch panel** — each color has its own switch, exactly one is ON!"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Cell 8: One-Hot Encode Categorical Columns\n",
    "print(\"🔄 Before One-Hot Encoding — Categorical columns:\")\n",
    "print(df[['Department', 'Attendance_Level', 'Favorite_Color']].head())\n",
    "print()\n",
    "\n",
    "# Apply one-hot encoding\n",
    "categorical_cols = ['Department', 'Attendance_Level', 'Favorite_Color']\n",
    "df_encoded = pd.get_dummies(df, columns=categorical_cols, drop_first=False)\n",
    "\n",
    "print(f\"✅ After encoding: {df_encoded.shape[1]} columns (was {df.shape[1]})\")\n",
    "print()\n",
    "\n",
    "# Show the new columns that start with 'Favorite_Color'\n",
    "color_cols = [c for c in df_encoded.columns if c.startswith('Favorite_Color')]\n",
    "print(f\"🌈 New one-hot columns for Favorite_Color ({len(color_cols)} total):\")\n",
    "print(color_cols)\n",
    "print()\n",
    "\n",
    "# Show a sample of the encoded color columns\n",
    "print(\"Sample of one-hot encoded colors:\")\n",
    "df_encoded[['Student_ID', 'Favorite_Color_Red', 'Favorite_Color_Blue', \n",
    "            'Favorite_Color_Green', 'Favorite_Color_Yellow',\n",
    "            'Favorite_Color_Purple', 'Favorite_Color_Orange']].head(10)"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Cell 9: Visualize One-Hot Encoding\n",
    "print(\"🎯 Let's see which colors have more stressed students:\")\n",
    "\n",
    "# Create a cross-tabulation\n",
    "color_stress = pd.crosstab(df['Favorite_Color'], df['Stress_Level'])\n",
    "color_stress['Stress_Rate'] = (color_stress[1] / (color_stress[0] + color_stress[1]) * 100).round(1)\n",
    "color_stress = color_stress.sort_values('Stress_Rate', ascending=False)\n",
    "print(color_stress)\n",
    "print()\n",
    "\n",
    "# Bar chart\n",
    "plt.figure(figsize=(8, 4))\n",
    "ax = color_stress['Stress_Rate'].plot(kind='bar', color=['#FF0000', '#2196F3', '#4CAF50', '#FFEB3B', '#9C27B0', '#FF9800'],\n",
    "                                      edgecolor='black', linewidth=0.5)\n",
    "plt.title('Stress Rate by Favorite Color', fontsize=14, fontweight='bold')\n",
    "plt.xlabel('Favorite Color')\n",
    "plt.ylabel('Stress Rate (%)')\n",
    "plt.xticks(rotation=0)\n",
    "for i, v in enumerate(color_stress['Stress_Rate']):\n",
    "    plt.text(i, v + 0.5, f'{v}%', ha='center', fontweight='bold')\n",
    "plt.ylim(0, 100)\n",
    "plt.show()"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 4: Train a Machine Learning Model 🤖\n",
    "\n",
    "We'll use a **Random Forest Classifier** — it's like asking 100 mini-experts to vote, then taking the majority decision. Easy to understand and very powerful!"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Cell 10: Prepare Features & Target\n",
    "# Drop Student_ID (not useful) and Stress_Level (our target)\n",
    "X = df_encoded.drop(['Student_ID', 'Stress_Level'], axis=1)\n",
    "y = df_encoded['Stress_Level']\n",
    "\n",
    "print(f\"Features (X): {X.shape[1]} columns, {X.shape[0]} rows\")\n",
    "print(f\"Target (y): {y.shape[0]} values\")\n",
    "print(f\"\\nFeature columns: {list(X.columns)[:10]}... (+ {len(X.columns)-10} more)\")\n",
    "print(f\"\\nTarget distribution:\\n{y.value_counts().to_string()}\")"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Cell 11: Split Data into Train & Test\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.3, random_state=42, stratify=y\n",
    ")\n",
    "\n",
    "print(f\"📚 Training set: {X_train.shape[0]} students ({X_train.shape[0]/len(df)*100:.0f}%)\")\n",
    "print(f\"🧪 Test set: {X_test.shape[0]} students ({X_test.shape[0]/len(df)*100:.0f}%)\")\n",
    "print()\n",
    "print(\"✅ Training target distribution:\")\n",
    "print(y_train.value_counts().to_string())\n",
    "print()\n",
    "print(\"✅ Test target distribution:\")\n",
    "print(y_test.value_counts().to_string())"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Cell 12: Train Random Forest Model\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "print(\"🌲✅ Model trained successfully!\")\n",
    "print(f\"Using {model.n_estimators} decision trees\")"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 5: Evaluate the Model 📊"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Cell 13: Make Predictions\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print(f\"🎯 Model Accuracy: {accuracy*100:.1f}%\")\n",
    "print()\n",
    "print(\"📋 Classification Report:\")\n",
    "print(classification_report(y_test, y_pred, target_names=['Low Stress', 'High Stress']))"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Cell 14: Confusion Matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "plt.figure(figsize=(6, 5))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', \n",
    "            xticklabels=['Low Stress', 'High Stress'],\n",
    "            yticklabels=['Low Stress', 'High Stress'])\n",
    "plt.title('Confusion Matrix', fontsize=14, fontweight='bold')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()\n",
    "\n",
    "print(\"📊 Confusion Matrix Breakdown:\")\n",
    "print(f\"✅ True Low Stress: {cm[0][0]} students correctly predicted\")\n",
    "print(f\"❌ False High Stress: {cm[0][1]} low-stress students predicted as high\")\n",
    "print(f\"❌ False Low Stress: {cm[1][0]} high-stress students predicted as low\")\n",
    "print(f\"✅ True High Stress: {cm[1][1]} students correctly predicted\")"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 6: Feature Importance 🔑\n",
    "\n",
    "What factors most influence student stress? Let's find out!"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Cell 15: Feature Importance\n",
    "importance = pd.DataFrame({\n",
    "    'Feature': X.columns,\n",
    "    'Importance': model.feature_importances_\n",
    "}).sort_values('Importance', ascending=False)\n",
    "\n",
    "print(\"🔑 Top 10 Most Important Factors:\")\n",
    "print(\"-\" * 40)\n",
    "for i, row in importance.head(10).iterrows():\n",
    "    bar = '█' * int(row['Importance'] * 50)\n",
    "    print(f\"{row['Feature']:40s} {bar} {row['Importance']*100:.1f}%\")\n",
    "print()\n",
    "\n",
    "# Plot top 10\n",
    "plt.figure(figsize=(10, 6))\n",
    "top10 = importance.head(10)\n",
    "bars = plt.barh(range(len(top10)), top10['Importance'].values, color='#8ab4f8')\n",
    "plt.yticks(range(len(top10)), top10['Feature'].values)\n",
    "plt.xlabel('Importance Score')\n",
    "plt.title('Top 10 Features Influencing Student Stress', fontsize=14, fontweight='bold')\n",
    "plt.gca().invert_yaxis()\n",
    "for i, v in enumerate(top10['Importance']):\n",
    "    plt.text(v + 0.005, i, f'{v*100:.1f}%', va='center', fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 🎯 Summary — What We Learned Today\n",
    "\n",
    "| Step | What We Did | Key Takeaway |\n",
    "|------|-------------|--------------|\n",
    "| 1 | Loaded data | CSV files are the standard format |\n",
    "| 2 | EDA | Visualized patterns before modeling |\n",
    "| 3 | **One-Hot Encoding** | Converted colors (Red/Blue/Green) to numbers 📌 |\n",
    "| 4 | Trained model | Random Forest — simple yet powerful |\n",
    "| 5 | Evaluated | Measured accuracy & confusion matrix |\n",
    "| 6 | Interpreted | Found most influential factors |\n",
    "\n",
    "### 📌 Key Concept: One-Hot Encoding\n",
    "- Categorical data (like **Favorite Color**) cannot directly go into ML models\n",
    "- One-hot encoding creates **separate 0/1 columns** for each category\n",
    "- In pandas: <code>pd.get_dummies(df['Favorite_Color'])</code>\n",
    "- Think of it as a **light switch panel** — one switch per color!\n",
    "\n",
    "---\n",
    "### 🙏 Thank You!\n",
    "\n",
    "**Kapil Panwar**  \n",
    "Gyannetra Pvt Ltd, Pune, Maharashtra  \n",
    "*India's Premier AI Consultancy*"
   ]
  }
 ]
 }
--- a/fdp-colab-content/generate_dataset.py
+++ b/fdp-colab-content/generate_dataset.py
@@ -0,0 +1,42 @@
 import pandas as pd
 import numpy as np
 np.random.seed(42)
 n = 300
 departments = np.random.choice(["Science", "Commerce", "Arts", "Engineering"], n)
 attendance = np.random.choice(["Regular", "Irregular", "Borderline"], n, p=[0.4, 0.3, 0.3])
 fav_colors = np.random.choice(["Red", "Blue", "Green", "Yellow", "Purple", "Orange"], n)
 study_hours = np.round(np.random.uniform(2, 25, n), 1)
 assign_sub = np.round(np.random.uniform(40, 100, n), 0)
 backlogs = np.random.randint(0, 6, n)
 # Stress logic
 # High if: irregular attendance OR backlogs>=3 OR study_hours<5 OR assign_sub<55
 stress = ((attendance == "Irregular") | 
          (backlogs >= 3) | 
          (study_hours < 5) | 
          (assign_sub < 55)).astype(int)
 # Add some noise so it's not 100% predictable
 flip = np.random.choice([0, 1], n, p=[0.9, 0.1])
 stress = stress ^ flip  # XOR flip 10%
 df = pd.DataFrame({
    "Student_ID": ["S{:03d}".format(i+1) for i in range(n)],
    "Department": departments,
    "Attendance_Level": attendance,
    "Favorite_Color": fav_colors,
    "Weekly_Study_Hours": study_hours,
    "Assignment_Submission_Pct": assign_sub.astype(int),
    "Backlogs_Count": backlogs,
    "Stress_Level": stress
 })
 df.to_csv("/root/fdp-workshop/fdp-colab-content/student_stress_data.csv", index=False)
 print("Dataset generated: {} rows, {} cols".format(len(df), len(df.columns)))
 print("Stress distribution:")
 print(df["Stress_Level"].value_counts().to_string())
 print("\nSample:")
 print(df.head(3).to_string())
 print("\nUnique colors: {}".format(df["Favorite_Color"].unique()))
--- a/fdp-colab-content/pre_read.html
+++ b/fdp-colab-content/pre_read.html
@@ -0,0 +1,396 @@
 <!DOCTYPE html>
 <html lang="en">
 <head>
 <meta charset="UTF-8">
 <style>
  @page {
    size: A4;
    margin: 2cm 1.8cm;
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 <body>
 <!-- ===== COVER ===== -->
 <section class="cover-page">
  <h1>FDP Pre-Read Material</h1>
  <p class="subtitle">Data Science Pipeline &mdash; Hands-on with Google Colab</p>
  <div class="divider"></div>
  <p class="meta">
    <strong>Speaker:</strong> Kapil Panwar<br>
    <strong>Organization:</strong> Gyannetra Pvt Ltd, Pune<br>
    <strong>Date:</strong> 30 May 2026<br>
    <strong>Venue:</strong> JIET, Jodhpur
  </p>
  <p class="footer-note">Pre-Read &bull; Please go through before the session</p>
 </section>
 <!-- ===== PAGE 1: Introduction ===== -->
 <section class="content-page">
  <div class="header-meta">FDP Pre-Read &bull; Data Science Pipeline</div>
  <h2>1. Introduction</h2>
  <p>
    Welcome to the <strong>Data Science Pipeline</strong> Faculty Development Program session!
    This pre-read document will prepare you for a completely hands-on, no-installation-needed workshop.
  </p>
  <div class="highlight-box">
    <strong>Good News:</strong> You do <em>not</em> need to install Python, Docker, or any software on your laptop.
    Everything runs in your browser via <strong>Google Colab</strong>.
  </div>
  <h3>What is a Data Science Pipeline?</h3>
  <p>
    A Data Science Pipeline is the end-to-end process of taking raw data and transforming it into
    actionable insights. The standard pipeline has these stages:
  </p>
  <ol>
    <li><strong>Data Collection</strong> &ndash; Gathering data from CSV, databases, APIs, etc.</li>
    <li><strong>Data Cleaning &amp; Preprocessing</strong> &ndash; Handling missing values, fixing data types</li>
    <li><strong>Feature Engineering</strong> &ndash; Creating new features, encoding categorical variables</li>
    <li><strong>Exploratory Data Analysis (EDA)</strong> &ndash; Visualizing patterns and relationships</li>
    <li><strong>Model Training</strong> &ndash; Applying machine learning algorithms</li>
    <li><strong>Evaluation</strong> &ndash; Measuring how well the model performs</li>
    <li><strong>Interpretation</strong> &ndash; Understanding what the model tells us</li>
  </ol>
  <div class="tip-box">
    <strong>Session Tip:</strong> Don't worry if some terms feel new. The session is designed for
    beginners. Just bring curiosity and a working internet connection.
  </div>
  <div class="page-footer">Page 1</div>
 </section>
 <!-- ===== PAGE 2: Google Colab ===== -->
 <section class="content-page">
  <div class="header-meta">FDP Pre-Read &bull; Data Science Pipeline</div>
  <h2>2. Google Colab &mdash; Your Browser-Based Lab</h2>
  <p>
    <strong>Google Colaboratory (Colab)</strong> is a free, cloud-based Jupyter notebook environment
    that runs entirely in your browser. No installation. No setup.
  </p>
  <h3>Why Colab for this FDP?</h3>
  <ul>
    <li>✅ <strong>Zero setup</strong> &ndash; Works on any laptop (i3, i5, any OS)</li>
    <li>✅ <strong>Free GPU/CPU</strong> &ndash; Google servers do the heavy lifting</li>
    <li>✅ <strong>Pre-written code</strong> &ndash; Just run each cell with Shift+Enter</li>
    <li>✅ <strong>Auto-saves to your Google Drive</strong></li>
    <li>✅ <strong>Works offline</strong> after initial load</li>
  </ul>
  <h3>How to Open a Colab Notebook</h3>
  <ol>
    <li>Open your browser (Chrome / Firefox recommended)</li>
    <li>Go to <code>colab.research.google.com</code></li>
    <li>Click <strong>File &rarr; Upload Notebook</strong></li>
    <li>Select the <code>.ipynb</code> file we share during the session</li>
  </ol>
  <div class="highlight-box">
    <strong>Shortcut:</strong> We will also share a direct Colab link &mdash; just click and it opens
    automatically. No upload needed.
  </div>
  <h3>Colab Cell Types</h3>
  <table>
    <tr><th>Cell Type</th><th>Use</th><th>Example</th></tr>
    <tr><td>Code Cell</td><td>Runs Python code</td><td><code>print("Hello")</code></td></tr>
    <tr><td>Text Cell</td><td>Markdown documentation</td><td>Explanations, headings</td></tr>
  </table>
  <div class="page-footer">Page 2</div>
 </section>
 <!-- ===== PAGE 3: Categorical Data & One-Hot Encoding ===== -->
 <section class="content-page">
  <div class="header-meta">FDP Pre-Read &bull; Data Science Pipeline</div>
  <h2>3. Categorical Data &amp; One-Hot Encoding</h2>
  <h3>What is Categorical Data?</h3>
  <p>
    Categorical data represents groups or categories rather than numbers. Examples: Department
    (Science, Commerce), Gender (Male, Female), or <strong>Favorite Color</strong> (Red, Blue, Green).
  </p>
  <p>
    Machine learning models understand only <em>numbers</em>. So we must convert text categories to
    numbers. This is called <strong>encoding</strong>.
  </p>
  <h3>The Problem with Simple Numbering</h3>
  <p>
    If we encode Red=1, Blue=2, Green=3, the model might think Green &gt; Blue &gt; Red (i.e.,
    order/ranking). But colors have <em>no inherent order</em>. This is why we use one-hot encoding.
  </p>
  <h3>How One-Hot Encoding Works</h3>
  <p>
    One-hot encoding creates a separate column for <em>each category</em>, with 1 (present) or 0 (absent).
  </p>
  <table>
    <tr><th>Original</th><th>Red</th><th>Blue</th><th>Green</th><th>Yellow</th></tr>
    <tr><td>Red</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>
    <tr><td>Blue</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>
    <tr><td>Green</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>
    <tr><td>Yellow</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>
  </table>
  <p>In Python with pandas, it's just one line:</p>
  <pre>import pandas as pd
 encoded = pd.get_dummies(df['Favorite_Color'], prefix='Color')</pre>
  <div class="tip-box">
    <strong>Visual Memory Aid:</strong> Think of one-hot like a light switch panel &mdash;
    each color has its own switch, and exactly one switch is ON at a time.
  </div>
  <div class="page-footer">Page 3</div>
 </section>
 <!-- ===== PAGE 4: Dataset & Session Outline ===== -->
 <section class="content-page">
  <div class="header-meta">FDP Pre-Read &bull; Data Science Pipeline</div>
  <h2>4. Session Dataset &mdash; Student Stress Prediction</h2>
  <p>
    During the session, we will use a dataset about <strong>college student stress levels</strong>.
    This dataset is specially designed to be relatable for faculty members.
  </p>
  <table>
    <tr><th>Feature</th><th>Type</th><th>Description</th><th>Example</th></tr>
    <tr><td>Department</td><td>Categorical</td><td>Student's department</td><td>Science, Arts, Commerce, Engineering</td></tr>
    <tr><td>Attendance Level</td><td>Categorical</td><td>Attendance category</td><td>Regular, Irregular, Borderline</td></tr>
    <tr><td>Favorite Color</td><td>Categorical</td><td>Self-reported favorite color 🌈</td><td>Red, Blue, Green, Yellow, Purple, Orange</td></tr>
    <tr><td>Weekly Study Hours</td><td>Numerical</td><td>Hours studied per week</td><td>2 &ndash; 25 hours</td></tr>
    <tr><td>Assignment Submission %</td><td>Numerical</td><td>Assignment submission rate</td><td>40% &ndash; 100%</td></tr>
    <tr><td>Backlogs Count</td><td>Numerical</td><td>Number of pending backlogs</td><td>0 &ndash; 5</td></tr>
    <tr><td>Stress Level</td><td>Binary (Target)</td><td>0 = Low Stress, 1 = High Stress</td><td>0 or 1</td></tr>
  </table>
  <h3>Session Outline (Hands-On)</h3>
  <ol>
    <li><strong>Open Colab &amp; Load Data</strong> &mdash; 5 min</li>
    <li><strong>Explore the Data (EDA)</strong> &mdash; 10 min</li>
    <li><strong>One-Hot Encode Categorical Columns</strong> &mdash; 10 min</li>
    <li><strong>Train a Machine Learning Model</strong> &mdash; 10 min</li>
    <li><strong>Evaluate &amp; Interpret Results</strong> &mdash; 10 min</li>
    <li><strong>Q&amp;A / Open Discussion</strong> &mdash; 10 min</li>
  </ol>
  <p><strong>Total:</strong> ~55 minutes</p>
  <div class="highlight-box">
    <strong>No prior coding experience needed.</strong> All code is pre-written.
    You just click "Run" and watch the magic happen.
  </div>
  <h3>What You Will Learn</h3>
  <ul>
    <li>✅ How to load and view data in Python</li>
    <li>✅ How to handle categorical data (one-hot encoding with colors!)</li>
    <li>✅ How to train a simple ML model</li>
    <li>✅ How to check if the model is accurate</li>
    <li>✅ How these skills apply to your own courses &amp; research</li>
  </ul>
  <div class="page-footer">Page 4</div>
 </section>
 <!-- ===== PAGE 5: Quick Reference ===== -->
 <section class="content-page">
  <div class="header-meta">FDP Pre-Read &bull; Data Science Pipeline</div>
  <h2>5. Quick Reference &mdash; Python &amp; Colab Cheatsheet</h2>
  <h3>Common Colab Shortcuts</h3>
  <table>
    <tr><th>Action</th><th>Shortcut</th></tr>
    <tr><td>Run current cell</td><td><code>Shift + Enter</code></td></tr>
    <tr><td>Add a code cell</td><td>Click <strong>+ Code</strong> button</td></tr>
    <tr><td>Add a text cell</td><td>Click <strong>+ Text</strong> button</td></tr>
    <tr><td>Delete a cell</td><td>Click cell &rarr; Trash icon (top-right)</td></tr>
    <tr><td>Toggle cell output</td><td>Double-click output area</td></tr>
  </table>
  <h3>Key Python Commands We'll Use</h3>
  <table>
    <tr><th>Command</th><th>What it does</th></tr>
    <tr><td><code>df.head()</code></td><td>Show first 5 rows of data</td></tr>
    <tr><td><code>df.info()</code></td><td>Show data types and missing values</td></tr>
    <tr><td><code>df.describe()</code></td><td>Show statistical summary</td></tr>
    <tr><td><code>df['col'].value_counts()</code></td><td>Count unique values in a column</td></tr>
    <tr><td><code>pd.get_dummies()</code></td><td>One-hot encode categorical columns</td></tr>
    <tr><td><code>train_test_split()</code></td><td>Split data into train/test</td></tr>
    <tr><td><code>RandomForestClassifier()</code></td><td>Create a Random Forest model</td></tr>
    <tr><td><code>accuracy_score()</code></td><td>Calculate model accuracy</td></tr>
  </table>
  <h3>Before the Session, Please:</h3>
  <ul>
    <li>✅ Ensure you have a working internet connection</li>
    <li>✅ Have a Google account (Gmail) logged in your browser</li>
    <li>✅ Open Chrome / Firefox browser (latest version)</li>
    <li>✅ Keep this pre-read handy for reference</li>
  </ul>
  <div class="highlight-box">
    <strong>See you at the session!</strong> If you have questions beforehand, please reach out
    via the FDP coordination team. We're here to help.
  </div>
  <div class="page-footer">Page 5</div>
 </section>
 <!-- ===== BACK PAGE ===== -->
 <section class="back-page">
  <h2>See You at the Session!</h2>
  <p>
    Data Science Pipeline &bull; 30 May 2026 &bull; JIET Jodhpur
  </p>
  <p>
    <strong>Kapil Panwar</strong><br>
    Gyannetra Pvt Ltd, Pune, Maharashtra<br>
    <span class="brand">Gyannetra &mdash; India's Premier AI Consultancy</span>
  </p>
 </section>
 </body>
 </html>
--- a/fdp-colab-content/student_stress_data.csv
+++ b/fdp-colab-content/student_stress_data.csv
@@ -0,0 +1,301 @@
 Student_ID,Department,Attendance_Level,Favorite_Color,Weekly_Study_Hours,Assignment_Submission_Pct,Backlogs_Count,Stress_Level
 S001,Arts,Borderline,Purple,22.5,41,2,1
 S002,Engineering,Regular,Yellow,15.7,48,4,1
 S003,Science,Regular,Orange,17.6,94,3,1
 S004,Arts,Irregular,Green,20.2,92,4,1
 S005,Arts,Borderline,Blue,13.5,76,3,1
 S006,Engineering,Regular,Blue,4.0,76,1,1
 S007,Science,Irregular,Green,14.4,80,2,1
 S008,Science,Borderline,Orange,15.5,51,3,0
 S009,Arts,Regular,Green,19.1,95,5,1
 S010,Commerce,Borderline,Purple,11.9,65,0,0
 S011,Arts,Regular,Purple,4.9,63,5,1
 S012,Arts,Irregular,Blue,8.5,71,5,1
 S013,Arts,Irregular,Yellow,10.4,43,0,1
 S014,Arts,Irregular,Orange,16.9,50,5,1
 S015,Engineering,Regular,Blue,15.1,84,3,0
 S016,Science,Borderline,Yellow,10.2,45,3,1
 S017,Engineering,Regular,Yellow,24.7,76,3,1
 S018,Engineering,Regular,Purple,15.9,55,0,0
 S019,Engineering,Regular,Red,7.5,63,5,1
 S020,Arts,Irregular,Red,4.3,57,0,1
 S021,Commerce,Irregular,Green,5.5,61,3,1
 S022,Science,Regular,Purple,7.7,83,5,1
 S023,Commerce,Irregular,Yellow,5.7,58,5,1
 S024,Engineering,Regular,Orange,6.3,74,4,0
 S025,Engineering,Irregular,Red,8.6,69,4,1
 S026,Commerce,Regular,Yellow,6.0,80,1,1
 S027,Commerce,Irregular,Red,22.6,96,2,1
 S028,Commerce,Regular,Red,3.8,84,4,1
 S029,Engineering,Borderline,Red,14.1,53,0,1
 S030,Engineering,Regular,Orange,11.4,42,4,1
 S031,Science,Regular,Purple,24.6,56,1,0
 S032,Science,Regular,Orange,4.6,76,5,1
 S033,Engineering,Borderline,Blue,11.2,43,1,1
 S034,Commerce,Borderline,Yellow,24.3,70,5,1
 S035,Commerce,Regular,Purple,21.9,76,5,1
 S036,Science,Irregular,Purple,20.8,60,2,1
 S037,Engineering,Borderline,Purple,7.9,86,4,1
 S038,Science,Irregular,Purple,5.9,46,0,1
 S039,Science,Irregular,Orange,17.4,45,0,1
 S040,Arts,Regular,Purple,23.4,84,3,1
 S041,Arts,Regular,Green,14.8,70,5,1
 S042,Arts,Borderline,Yellow,15.1,81,0,0
 S043,Commerce,Borderline,Purple,8.4,66,2,0
 S044,Engineering,Irregular,Yellow,19.7,55,5,1
 S045,Engineering,Regular,Green,6.3,89,2,0
 S046,Engineering,Regular,Green,9.4,88,0,0
 S047,Engineering,Borderline,Yellow,11.8,82,0,0
 S048,Arts,Borderline,Red,13.7,56,3,1
 S049,Commerce,Borderline,Blue,7.6,75,0,0
 S050,Commerce,Borderline,Red,4.6,62,5,1
 S051,Arts,Irregular,Red,16.0,45,5,1
 S052,Commerce,Regular,Orange,8.6,95,5,1
 S053,Arts,Regular,Red,15.4,48,3,1
 S054,Engineering,Borderline,Purple,5.6,97,3,1
 S055,Arts,Irregular,Orange,13.1,67,0,1
 S056,Engineering,Regular,Orange,14.2,51,1,1
 S057,Engineering,Regular,Green,3.2,73,1,1
 S058,Science,Irregular,Red,9.7,92,3,1
 S059,Arts,Regular,Green,5.1,84,0,0
 S060,Science,Regular,Yellow,3.5,88,1,1
 S061,Arts,Irregular,Blue,24.8,80,0,1
 S062,Arts,Irregular,Orange,9.4,82,0,1
 S063,Science,Irregular,Yellow,20.6,91,0,1
 S064,Science,Regular,Yellow,7.9,55,4,1
 S065,Arts,Borderline,Purple,17.7,69,5,1
 S066,Commerce,Regular,Blue,19.5,53,0,1
 S067,Engineering,Regular,Yellow,15.7,99,4,1
 S068,Science,Borderline,Yellow,12.8,97,3,1
 S069,Engineering,Irregular,Blue,11.5,42,1,1
 S070,Commerce,Borderline,Blue,10.0,82,5,1
 S071,Commerce,Irregular,Yellow,23.4,96,1,1
 S072,Commerce,Irregular,Blue,21.1,51,5,0
 S073,Science,Regular,Yellow,24.2,74,1,0
 S074,Commerce,Regular,Yellow,4.9,95,5,1
 S075,Science,Regular,Purple,18.8,42,1,1
 S076,Commerce,Regular,Red,23.6,82,1,0
 S077,Engineering,Borderline,Yellow,6.2,58,1,0
 S078,Engineering,Regular,Green,3.5,95,1,1
 S079,Arts,Borderline,Orange,19.0,98,3,0
 S080,Engineering,Irregular,Red,15.2,97,0,1
 S081,Arts,Borderline,Red,21.4,68,2,1
 S082,Engineering,Irregular,Red,5.2,92,4,1
 S083,Science,Irregular,Purple,20.3,91,2,1
 S084,Engineering,Irregular,Yellow,6.6,59,2,1
 S085,Arts,Regular,Purple,5.8,90,5,1
 S086,Arts,Borderline,Yellow,5.8,42,0,1
 S087,Commerce,Regular,Purple,20.7,76,5,1
 S088,Science,Regular,Purple,17.3,54,0,1
 S089,Engineering,Irregular,Orange,14.0,47,0,1
 S090,Commerce,Regular,Green,10.3,45,1,1
 S091,Engineering,Borderline,Purple,22.2,82,3,1
 S092,Engineering,Borderline,Orange,11.0,60,1,0
 S093,Commerce,Borderline,Blue,20.8,83,3,1
 S094,Commerce,Regular,Green,12.1,44,5,1
 S095,Commerce,Regular,Purple,10.7,59,3,1
 S096,Commerce,Borderline,Red,12.6,72,5,1
 S097,Commerce,Irregular,Blue,8.9,87,2,1
 S098,Engineering,Borderline,Blue,19.2,59,2,0
 S099,Commerce,Borderline,Orange,13.6,78,3,1
 S100,Science,Borderline,Blue,7.3,93,3,1
 S101,Arts,Regular,Green,22.7,77,5,1
 S102,Commerce,Regular,Purple,10.8,54,1,1
 S103,Commerce,Borderline,Purple,14.5,41,5,1
 S104,Engineering,Regular,Red,22.8,92,1,1
 S105,Commerce,Regular,Orange,16.4,41,2,1
 S106,Commerce,Irregular,Red,4.7,92,0,1
 S107,Commerce,Borderline,Blue,23.6,72,3,1
 S108,Engineering,Irregular,Red,16.4,96,1,1
 S109,Commerce,Irregular,Green,9.7,88,5,1
 S110,Arts,Regular,Purple,5.2,100,2,1
 S111,Engineering,Irregular,Blue,20.3,61,1,1
 S112,Arts,Borderline,Red,16.3,86,5,1
 S113,Engineering,Regular,Orange,14.3,64,3,1
 S114,Commerce,Irregular,Green,22.6,69,3,1
 S115,Arts,Borderline,Green,20.1,78,4,1
 S116,Engineering,Borderline,Red,5.5,92,0,0
 S117,Science,Irregular,Purple,9.2,99,4,1
 S118,Commerce,Borderline,Red,7.7,86,2,0
 S119,Engineering,Regular,Blue,19.1,65,1,1
 S120,Science,Regular,Red,2.8,65,2,1
 S121,Engineering,Borderline,Green,15.1,84,5,1
 S122,Science,Borderline,Red,19.5,54,3,1
 S123,Commerce,Borderline,Purple,22.2,47,4,1
 S124,Arts,Borderline,Orange,9.9,61,4,1
 S125,Science,Irregular,Yellow,20.9,57,4,1
 S126,Engineering,Irregular,Red,4.5,58,2,1
 S127,Commerce,Borderline,Purple,21.5,54,3,1
 S128,Science,Irregular,Purple,4.9,43,1,1
 S129,Engineering,Borderline,Orange,11.1,41,5,1
 S130,Engineering,Borderline,Green,20.3,99,1,0
 S131,Engineering,Borderline,Purple,5.4,66,2,0
 S132,Science,Regular,Purple,7.3,63,5,1
 S133,Science,Regular,Purple,18.6,81,4,1
 S134,Science,Regular,Purple,18.6,53,0,1
 S135,Arts,Irregular,Blue,16.7,97,2,1
 S136,Science,Regular,Blue,18.0,87,2,0
 S137,Science,Irregular,Green,14.5,45,3,1
 S138,Science,Irregular,Red,7.8,65,1,1
 S139,Arts,Regular,Purple,10.0,93,2,0
 S140,Science,Irregular,Orange,6.2,97,2,1
 S141,Engineering,Regular,Red,22.9,68,4,1
 S142,Science,Regular,Orange,15.4,77,4,1
 S143,Engineering,Borderline,Red,11.2,50,5,1
 S144,Engineering,Regular,Green,12.6,99,0,1
 S145,Engineering,Regular,Purple,23.8,54,3,1
 S146,Arts,Irregular,Purple,5.5,97,0,1
 S147,Arts,Borderline,Yellow,15.5,79,5,1
 S148,Arts,Regular,Orange,13.6,76,4,1
 S149,Science,Irregular,Red,16.1,71,1,1
 S150,Engineering,Regular,Red,2.4,54,4,1
 S151,Arts,Regular,Blue,22.1,51,3,0
 S152,Arts,Irregular,Yellow,23.4,53,4,1
 S153,Science,Irregular,Blue,15.0,51,3,1
 S154,Arts,Irregular,Blue,18.0,87,3,1
 S155,Science,Borderline,Blue,23.2,61,3,1
 S156,Commerce,Borderline,Orange,18.3,43,5,1
 S157,Arts,Irregular,Orange,5.5,98,3,1
 S158,Commerce,Regular,Green,15.3,93,3,1
 S159,Science,Borderline,Green,16.0,96,1,0
 S160,Engineering,Regular,Blue,11.8,100,0,0
 S161,Arts,Irregular,Yellow,18.9,50,2,1
 S162,Science,Regular,Red,23.5,64,0,0
 S163,Engineering,Regular,Orange,23.3,85,3,1
 S164,Engineering,Borderline,Yellow,12.4,82,1,0
 S165,Commerce,Borderline,Purple,4.6,49,4,1
 S166,Science,Irregular,Orange,24.7,89,1,1
 S167,Engineering,Irregular,Orange,21.3,53,3,1
 S168,Arts,Regular,Green,4.9,53,1,1
 S169,Arts,Regular,Red,23.2,72,1,0
 S170,Commerce,Regular,Red,22.0,76,3,1
 S171,Engineering,Irregular,Purple,13.9,75,5,1
 S172,Science,Borderline,Purple,15.6,45,1,1
 S173,Arts,Irregular,Orange,11.2,93,2,1
 S174,Engineering,Regular,Blue,3.3,56,2,1
 S175,Engineering,Borderline,Green,9.7,48,1,1
 S176,Commerce,Borderline,Green,20.5,93,5,0
 S177,Arts,Irregular,Orange,2.1,97,4,1
 S178,Arts,Irregular,Yellow,9.7,92,3,1
 S179,Science,Irregular,Blue,11.2,89,2,1
 S180,Arts,Regular,Orange,14.4,79,4,1
 S181,Science,Regular,Blue,23.2,73,1,0
 S182,Arts,Borderline,Blue,10.0,45,4,1
 S183,Commerce,Regular,Blue,10.0,65,0,1
 S184,Arts,Regular,Orange,19.0,62,2,0
 S185,Science,Regular,Green,12.4,56,1,0
 S186,Science,Regular,Green,7.2,83,3,1
 S187,Commerce,Borderline,Blue,12.4,70,5,1
 S188,Arts,Borderline,Yellow,5.2,45,1,1
 S189,Arts,Irregular,Red,6.1,53,3,1
 S190,Commerce,Regular,Orange,13.5,81,5,1
 S191,Arts,Irregular,Red,11.6,45,5,1
 S192,Arts,Irregular,Yellow,23.0,91,1,1
 S193,Science,Regular,Orange,10.3,70,4,1
 S194,Arts,Irregular,Blue,15.4,69,4,0
 S195,Arts,Regular,Green,16.5,76,5,1
 S196,Commerce,Irregular,Red,2.3,89,4,1
 S197,Commerce,Irregular,Purple,17.3,61,0,1
 S198,Engineering,Regular,Purple,6.1,81,5,0
 S199,Science,Regular,Yellow,24.1,74,5,0
 S200,Arts,Irregular,Blue,5.4,56,3,1
 S201,Arts,Irregular,Red,11.5,93,4,1
 S202,Engineering,Borderline,Blue,4.0,88,5,1
 S203,Arts,Irregular,Red,24.9,80,0,1
 S204,Science,Regular,Yellow,13.6,91,3,1
 S205,Engineering,Regular,Yellow,15.7,92,1,0
 S206,Science,Irregular,Purple,3.5,83,5,1
 S207,Engineering,Regular,Blue,19.2,90,0,0
 S208,Engineering,Irregular,Orange,6.8,82,2,1
 S209,Commerce,Borderline,Purple,22.7,81,0,0
 S210,Science,Irregular,Blue,6.7,77,0,1
 S211,Arts,Regular,Purple,6.4,85,3,1
 S212,Arts,Irregular,Orange,2.8,50,1,1
 S213,Science,Irregular,Green,12.9,93,2,1
 S214,Arts,Irregular,Green,15.0,92,4,1
 S215,Arts,Borderline,Green,3.5,42,4,0
 S216,Science,Regular,Green,19.8,90,0,0
 S217,Engineering,Borderline,Red,12.4,48,3,1
 S218,Science,Borderline,Green,14.1,60,3,1
 S219,Engineering,Regular,Orange,12.1,85,4,1
 S220,Arts,Regular,Yellow,11.2,50,2,1
 S221,Arts,Regular,Yellow,14.9,89,1,0
 S222,Arts,Regular,Red,5.6,90,2,1
 S223,Commerce,Regular,Green,6.2,70,5,1
 S224,Engineering,Irregular,Green,21.8,40,5,0
 S225,Commerce,Regular,Green,23.8,57,1,0
 S226,Commerce,Regular,Purple,10.6,77,4,1
 S227,Science,Borderline,Blue,8.2,99,1,1
 S228,Commerce,Regular,Purple,16.8,78,3,1
 S229,Science,Borderline,Blue,11.4,56,5,1
 S230,Science,Regular,Green,2.6,78,0,1
 S231,Commerce,Regular,Green,5.6,72,0,0
 S232,Engineering,Irregular,Purple,18.5,87,4,1
 S233,Engineering,Irregular,Purple,17.2,46,3,1
 S234,Engineering,Borderline,Blue,2.6,86,2,1
 S235,Engineering,Borderline,Yellow,7.1,72,3,1
 S236,Engineering,Borderline,Blue,7.3,98,0,0
 S237,Commerce,Regular,Purple,17.5,61,0,0
 S238,Commerce,Regular,Orange,2.5,78,5,1
 S239,Arts,Borderline,Purple,4.4,96,3,1
 S240,Engineering,Borderline,Orange,20.4,46,3,1
 S241,Commerce,Borderline,Red,6.1,96,0,1
 S242,Arts,Irregular,Purple,17.0,81,2,1
 S243,Engineering,Regular,Red,7.5,44,4,1
 S244,Science,Borderline,Yellow,4.3,58,4,1
 S245,Arts,Regular,Blue,7.6,82,3,1
 S246,Commerce,Borderline,Orange,18.6,44,4,0
 S247,Science,Borderline,Blue,21.7,75,0,0
 S248,Science,Irregular,Red,21.1,61,0,1
 S249,Science,Borderline,Orange,11.1,77,3,1
 S250,Arts,Borderline,Blue,17.4,43,3,1
 S251,Commerce,Regular,Purple,6.7,92,5,1
 S252,Science,Borderline,Green,8.7,98,3,1
 S253,Engineering,Irregular,Red,22.6,98,2,1
 S254,Science,Borderline,Blue,2.3,85,4,1
 S255,Science,Regular,Red,4.0,48,3,1
 S256,Arts,Borderline,Red,6.8,85,1,0
 S257,Arts,Regular,Orange,2.6,41,5,1
 S258,Commerce,Regular,Orange,6.2,41,1,0
 S259,Engineering,Borderline,Green,15.4,59,0,0
 S260,Arts,Regular,Purple,11.7,69,4,0
 S261,Science,Regular,Red,22.5,86,3,1
 S262,Commerce,Borderline,Blue,20.8,81,1,0
 S263,Science,Borderline,Yellow,9.9,67,4,1
 S264,Science,Irregular,Red,8.0,56,0,1
 S265,Arts,Irregular,Red,10.7,100,2,1
 S266,Commerce,Irregular,Green,15.6,66,5,1
 S267,Engineering,Regular,Purple,8.2,67,4,1
 S268,Engineering,Regular,Yellow,16.4,50,4,1
 S269,Arts,Irregular,Orange,11.4,88,2,1
 S270,Arts,Borderline,Orange,14.7,82,4,1
 S271,Commerce,Borderline,Blue,12.0,53,0,1
 S272,Engineering,Borderline,Yellow,8.8,45,0,1
 S273,Engineering,Regular,Orange,23.8,81,2,0
 S274,Engineering,Irregular,Blue,19.6,79,5,1
 S275,Science,Regular,Purple,5.2,56,3,0
 S276,Engineering,Irregular,Blue,22.0,97,1,1
 S277,Science,Irregular,Orange,13.2,49,3,1
 S278,Commerce,Borderline,Green,22.6,66,4,0
 S279,Science,Regular,Green,20.4,97,1,0
 S280,Commerce,Regular,Green,11.8,65,2,0
 S281,Engineering,Regular,Orange,2.5,78,2,1
 S282,Engineering,Borderline,Green,8.2,64,5,1
 S283,Commerce,Irregular,Yellow,14.5,56,1,1
 S284,Arts,Regular,Purple,16.6,99,0,0
 S285,Commerce,Regular,Blue,7.9,65,1,0
 S286,Arts,Borderline,Blue,5.2,94,4,1
 S287,Science,Regular,Orange,21.2,54,0,0
 S288,Science,Borderline,Green,24.6,53,2,1
 S289,Science,Borderline,Green,14.1,42,1,1
 S290,Engineering,Regular,Red,5.9,79,3,1
 S291,Science,Regular,Purple,8.3,62,5,1
 S292,Arts,Borderline,Yellow,2.4,92,3,1
 S293,Science,Regular,Blue,23.0,68,5,1
 S294,Commerce,Regular,Red,4.7,98,4,1
 S295,Commerce,Borderline,Red,15.3,51,3,1
 S296,Engineering,Borderline,Orange,8.3,92,1,0
 S297,Commerce,Borderline,Blue,14.7,87,1,0
 S298,Arts,Borderline,Orange,17.0,86,3,1
 S299,Science,Regular,Yellow,21.1,91,1,0
 S300,Engineering,Regular,Red,6.7,86,4,1