{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Student Performance Prediction using Python, MongoDB, and Scikit-Learn\n",
    "**A Beginner-Friendly Machine Learning Project for B.Tech CSE Students**\n",
    "\n",
    "### Project Highlights:\n",
    "- **Database:** MongoDB (`student_performance_db`)\n",
    "- **Connection Library:** PyMongo\n",
    "- **Dataset Size:** 1,000 Student Records\n",
    "- **ML Algorithm:** Linear Regression\n",
    "- **Evaluation Metrics:** $R^2$ Score and Mean Absolute Error (MAE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Install & Import Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all required packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pymongo import MongoClient\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(\"All libraries imported successfully!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Generate 1,000 Realistic Student Records & Store in MongoDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set random seed for consistency\n",
    "np.random.seed(42)\n",
    "num_records = 1000\n",
    "\n",
    "# 1. Features generation\n",
    "student_ids = [f\"STU{1001 + i}\" for i in range(num_records)]\n",
    "study_hours = np.round(np.random.uniform(1.0, 9.5, num_records), 1)\n",
    "attendance = np.round(np.random.uniform(60.0, 99.0, num_records), 1)\n",
    "previous_marks = np.round(np.random.uniform(40.0, 95.0, num_records), 1)\n",
    "assignments_score = np.random.randint(4, 11, num_records)\n",
    "\n",
    "# 2. Calculate realistic Final Marks with realistic noise\n",
    "noise = np.random.normal(0, 2.5, num_records)\n",
    "final_marks = (0.45 * previous_marks + 3.5 * study_hours + 0.25 * attendance + 1.5 * assignments_score + noise)\n",
    "final_marks = np.round(np.clip(final_marks, 30.0, 100.0), 1)\n",
    "\n",
    "# Combine into DataFrame\n",
    "df_raw = pd.DataFrame({\n",
    "    \"student_id\": student_ids,\n",
    "    \"study_hours\": study_hours,\n",
    "    \"attendance\": attendance,\n",
    "    \"previous_marks\": previous_marks,\n",
    "    \"assignments_score\": assignments_score,\n",
    "    \"final_marks\": final_marks\n",
    "})\n",
    "\n",
    "# Connect to MongoDB and save records\n",
    "client = MongoClient(\"mongodb://localhost:27017/\")\n",
    "db = client[\"student_performance_db\"]\n",
    "collection = db[\"students\"]\n",
    "\n",
    "collection.delete_many({})\n",
    "collection.insert_many(df_raw.to_dict(orient=\"records\"))\n",
    "print(f\"Successfully stored {collection.count_documents({})} documents in MongoDB!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Fetch Data from MongoDB using PyMongo & Show Sample Records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch all documents from MongoDB\n",
    "data_from_db = list(collection.find())\n",
    "df = pd.DataFrame(data_from_db)\n",
    "\n",
    "print(f\"Fetched {len(df)} records from MongoDB.\")\n",
    "# Display 5 sample records\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Data Preprocessing & Train-Test Split (80% / 20%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop MongoDB '_id' and identifier 'student_id'\n",
    "clean_df = df.drop(columns=['_id', 'student_id'])\n",
    "\n",
    "# Check for missing values\n",
    "print(\"Missing values check:\")\n",
    "print(clean_df.isnull().sum())\n",
    "\n",
    "# Separate Features (X) and Target (y)\n",
    "X = clean_df[['study_hours', 'attendance', 'previous_marks', 'assignments_score']]\n",
    "y = clean_df['final_marks']\n",
    "\n",
    "# Split: 80% Training, 20% Testing\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "print(f\"Training set: {X_train.shape[0]} rows, Testing set: {X_test.shape[0]} rows\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Train Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize and fit model\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Print learned weights\n",
    "for feature, weight in zip(X.columns, model.coef_):\n",
    "    print(f\"Weight for {feature:20s}: {weight:.4f}\")\n",
    "print(f\"Intercept (c): {model.intercept_:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Evaluation Metrics & Comparison Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict on testing dataset\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "\n",
    "print(f\"R-squared (R2) Score : {r2:.4f} (Accuracy: {r2*100:.2f}%)\")\n",
    "print(f\"Mean Absolute Error  : {mae:.2f} marks\")\n",
    "print(f\"Root Mean Sq Error   : {rmse:.2f} marks\")\n",
    "\n",
    "# Display 10 Sample Predictions\n",
    "comparison = pd.DataFrame({\n",
    "    \"Actual Marks\": y_test.values[:10],\n",
    "    \"Predicted Marks\": np.round(y_pred[:10], 1),\n",
    "    \"Absolute Error\": np.round(np.abs(y_test.values[:10] - y_pred[:10]), 1)\n",
    "})\n",
    "comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Visualizing Actual vs Predicted Marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# Scatter plot\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(y_test, y_pred, color='#2563EB', alpha=0.6, edgecolors='k', s=40)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], color='#DC2626', linestyle='--', linewidth=2, label='Perfect Fit')\n",
    "plt.title(\"Actual vs Predicted Marks\", fontweight='bold')\n",
    "plt.xlabel(\"Actual Marks\")\n",
    "plt.ylabel(\"Predicted Marks\")\n",
    "plt.grid(True, linestyle=':', alpha=0.6)\n",
    "plt.legend()\n",
    "\n",
    "# Bar chart for 10 samples\n",
    "plt.subplot(1, 2, 2)\n",
    "indices = np.arange(10)\n",
    "width = 0.35\n",
    "plt.bar(indices - width/2, y_test.values[:10], width, label='Actual', color='#3B82F6')\n",
    "plt.bar(indices + width/2, y_pred[:10], width, label='Predicted', color='#10B981')\n",
    "plt.title(\"Comparison for 10 Students\", fontweight='bold')\n",
    "plt.xlabel(\"Student Index\")\n",
    "plt.ylabel(\"Marks\")\n",
    "plt.xticks(indices, [f\"S{i+1}\" for i in indices])\n",
    "plt.grid(True, linestyle=':', alpha=0.6)\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Test Prediction with a New Custom Student"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_student = pd.DataFrame([{\n",
    "    'study_hours': 7.0,\n",
    "    'attendance': 90.0,\n",
    "    'previous_marks': 82.0,\n",
    "    'assignments_score': 9\n",
    "}])\n",
    "\n",
    "predicted_score = model.predict(custom_student)[0]\n",
    "print(f\"Predicted Final Marks for custom student: {predicted_score:.1f} / 100\")"
   ]
  }
 ],
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