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Student_Marks.csv

@@ -0,0 +1,101 @@
+number_courses,time_study,Marks
+3,4.508,19.202
+4,0.096,7.734
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+4,1.954,9.742
+3,2.061,8.924
+4,3.797,16.703
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+3,5.635,26.882
+5,3.913,19.106
+6,6.703,40.602
+6,4.130,22.184
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+6,3.561,19.128
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+3,6.335,32.357

app.py

@@ -0,0 +1,59 @@
+from flask import Flask, request, jsonify, render_template
+import tensorflow as tf
+import numpy as np
+import joblib
+import os
+
+app = Flask(__name__)
+
+# Load model and scalers globally for efficiency
+MODEL_PATH = 'student_marks_rnn_model.h5'
+SCALER_X_PATH = 'scaler_X.pkl'
+SCALER_Y_PATH = 'scaler_y.pkl'
+
+model = None
+scaler_X = None
+scaler_y = None
+
+def load_resources():
+    global model, scaler_X, scaler_y
+    if os.path.exists(MODEL_PATH) and os.path.exists(SCALER_X_PATH) and os.path.exists(SCALER_Y_PATH):
+        model = tf.keras.models.load_model(MODEL_PATH)
+        scaler_X = joblib.load(SCALER_X_PATH)
+        scaler_y = joblib.load(SCALER_Y_PATH)
+        return True
+    return False
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if model is None:
+        if not load_resources():
+            return jsonify({'error': 'Model or scalers not found. Run training first.'}), 500
+
+    try:
+        data = request.get_json()
+        num_courses = float(data['num_courses'])
+        time_study = float(data['time_study'])
+
+        # Preprocess
+        input_data = np.array([[num_courses, time_study]])
+        input_scaled = scaler_X.transform(input_data)
+        input_reshaped = input_scaled.reshape((1, 1, 2))
+
+        # Predict
+        prediction_scaled = model.predict(input_reshaped)
+        prediction = scaler_y.inverse_transform(prediction_scaled)
+        
+        result = float(prediction[0][0])
+        return jsonify({'marks': round(result, 2)})
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+if __name__ == '__main__':
+    load_resources()
+    app.run(debug=True, port=5000)

implementation_plan.md

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+# Implementation Plan - Student Marks Prediction using RNN
+
+This document outlines the steps to build an end-to-end Recurrent Neural Network (RNN) model to predict student marks based on the number of courses and study time.
+
+## 1. Data Exploration & Preprocessing
+- Load `Student_Marks.csv`.
+- Inspect data quality and statistics.
+- Normalize features (`number_courses`, `time_study`) and target (`Marks`) using `MinMaxScaler` or `StandardScaler`.
+- Split the dataset into training (80%) and testing (20%) sets.
+- **RNN Reshaping**: Reshape the input data to `(samples, time_steps, features)`. Since this is a simple tabular dataset, we will use `time_steps = 1`.
+
+## 2. Model Architecture
+- **Input Layer**: Shape `(1, 2)`.
+- **RNN Layer**: Use `SimpleRNN` or `LSTM` with 64 units.
+- **Dense Layer**: Hidden layer with 32 units, ReLU activation.
+- **Output Layer**: Single neuron for regression (predicted Marks).
+- **Compile**: Use `Adam` optimizer and `Mean Squared Error` (MSE) loss.
+
+## 3. Training
+- Train for 100 epochs (adjustable).
+- Use a validation split to monitor overfitting.
+
+## 4. Evaluation & Visualization
+- Evaluate the model on the test set.
+- Plot training and validation loss curves.
+- Compare predicted values with actual values.
+
+## 5. Inference
+- Create a script to make predictions on new data.

predict.py

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+import tensorflow as tf
+import numpy as np
+import joblib
+import os
+
+def predict_marks(num_courses, time_study):
+    # Load model and scalers
+    if not (os.path.exists('student_marks_rnn_model.h5') and 
+            os.path.exists('scaler_X.pkl') and 
+            os.path.exists('scaler_y.pkl')):
+        return "Error: Model or scalers not found. Please run train_rnn.py first."
+
+    model = tf.keras.models.load_model('student_marks_rnn_model.h5')
+    scaler_X = joblib.load('scaler_X.pkl')
+    scaler_y = joblib.load('scaler_y.pkl')
+    
+    # Preprocess input
+    input_data = np.array([[num_courses, time_study]])
+    input_scaled = scaler_X.transform(input_data)
+    
+    # Reshape for RNN (Samples, TimeSteps, Features)
+    input_reshaped = input_scaled.reshape((1, 1, 2))
+    
+    # Predict
+    prediction_scaled = model.predict(input_reshaped)
+    prediction = scaler_y.inverse_transform(prediction_scaled)
+    
+    return prediction[0][0]
+
+if __name__ == "__main__":
+    print("--- Student Marks Prediction RNN ---")
+    try:
+        nc = float(input("Enter number of courses: "))
+        ts = float(input("Enter time spent studying (hours): "))
+        
+        result = predict_marks(nc, ts)
+        if isinstance(result, str):
+            print(result)
+        else:
+            print(f"\nPredicted Marks: {result:.2f}")
+    except ValueError:
+        print("Invalid input. Please enter numeric values.")
+    except Exception as e:
+        print(f"Error: {e}")

scaler_X.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5fc9609d21ece6a569e3e84700fa0352b1c94950bfdd1d2e9d36766f4a6baebb
+size 743

scaler_y.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:91b3966771b5cab72bd1dd1f32373cc9fd6616a85606a61852b0b61381a76f3d
+size 719

static/css/style.css

@@ -0,0 +1,333 @@
+:root {
+    --primary: #6366f1;
+    --secondary: #a855f7;
+    --accent: #ec4899;
+    --bg: #0f172a;
+    --text: #f8fafc;
+    --glass: rgba(255, 255, 255, 0.05);
+    --border: rgba(255, 255, 255, 0.1);
+}
+
+* {
+    margin: 0;
+    padding: 0;
+    box-sizing: border-box;
+}
+
+body {
+    font-family: 'Outfit', sans-serif;
+    background-color: var(--bg);
+    color: var(--text);
+    overflow-x: hidden;
+    min-height: 100vh;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+}
+
+/* Background Animations */
+.background-blobs {
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    z-index: -1;
+    overflow: hidden;
+}
+
+.blob {
+    position: absolute;
+    border-radius: 50%;
+    filter: blur(80px);
+    opacity: 0.4;
+    transition: all 1s ease;
+    animation: float 20s infinite alternate;
+}
+
+.blob-1 {
+    width: 400px;
+    height: 400px;
+    background: var(--primary);
+    top: -100px;
+    right: -100px;
+}
+
+.blob-2 {
+    width: 500px;
+    height: 500px;
+    background: var(--secondary);
+    bottom: -150px;
+    left: -150px;
+    animation-delay: -5s;
+}
+
+.blob-3 {
+    width: 300px;
+    height: 300px;
+    background: var(--accent);
+    top: 50%;
+    left: 10%;
+    animation-delay: -10s;
+}
+
+@keyframes float {
+    0% {
+        transform: translate(0, 0) scale(1);
+    }
+
+    100% {
+        transform: translate(100px, 50px) scale(1.1);
+    }
+}
+
+/* Layout */
+.container {
+    max-width: 1000px;
+    width: 90%;
+    padding: 2rem 0;
+}
+
+header {
+    text-align: center;
+    padding: 2rem;
+    margin-bottom: 2rem;
+}
+
+h1 {
+    font-size: 3rem;
+    font-weight: 800;
+    letter-spacing: -1px;
+    margin-bottom: 0.5rem;
+}
+
+.accent {
+    background: linear-gradient(to right, var(--primary), var(--secondary));
+    -webkit-background-clip: text;
+    background-clip: text;
+    -webkit-text-fill-color: transparent;
+}
+
+header p {
+    color: #94a3b8;
+    font-size: 1.1rem;
+}
+
+/* Glass Card */
+.glass-card {
+    background: var(--glass);
+    backdrop-filter: blur(12px);
+    -webkit-backdrop-filter: blur(12px);
+    border: 1px solid var(--border);
+    border-radius: 24px;
+    padding: 2.5rem;
+    box-shadow: 0 8px 32px 0 rgba(0, 0, 0, 0.37);
+}
+
+.prediction-grid {
+    display: grid;
+    grid-template-columns: 1fr 1fr;
+    gap: 2rem;
+}
+
+@media (max-width: 768px) {
+    .prediction-grid {
+        grid-template-columns: 1fr;
+    }
+}
+
+/* Inputs */
+.input-group {
+    margin-bottom: 1.5rem;
+}
+
+label {
+    display: block;
+    margin-bottom: 0.5rem;
+    font-weight: 600;
+    color: #cbd5e1;
+}
+
+input {
+    width: 100%;
+    background: rgba(0, 0, 0, 0.2);
+    border: 1px solid var(--border);
+    border-radius: 12px;
+    padding: 1rem;
+    color: white;
+    font-size: 1rem;
+    font-family: inherit;
+    transition: all 0.3s ease;
+}
+
+input:focus {
+    outline: none;
+    border-color: var(--primary);
+    box-shadow: 0 0 0 4px rgba(99, 102, 241, 0.2);
+}
+
+/* Button */
+button {
+    width: 100%;
+    background: linear-gradient(135deg, var(--primary), var(--secondary));
+    border: none;
+    border-radius: 12px;
+    padding: 1rem;
+    color: white;
+    font-size: 1.1rem;
+    font-weight: 700;
+    cursor: pointer;
+    transition: all 0.3s cubic-bezier(0.175, 0.885, 0.32, 1.275);
+    position: relative;
+    overflow: hidden;
+}
+
+button:hover {
+    transform: translateY(-3px);
+    box-shadow: 0 10px 20px -5px rgba(99, 102, 241, 0.5);
+}
+
+button:active {
+    transform: translateY(0);
+}
+
+button:disabled {
+    opacity: 0.7;
+    cursor: not-allowed;
+}
+
+/* Loader */
+.loader-dots {
+    display: none;
+    justify-content: center;
+    gap: 5px;
+    position: absolute;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+}
+
+.loader-dots span {
+    width: 8px;
+    height: 8px;
+    background: white;
+    border-radius: 50%;
+    animation: bounce 0.6s infinite alternate;
+}
+
+.loader-dots span:nth-child(2) {
+    animation-delay: 0.2s;
+}
+
+.loader-dots span:nth-child(3) {
+    animation-delay: 0.4s;
+}
+
+@keyframes bounce {
+    to {
+        transform: translateY(-10px);
+        opacity: 0.3;
+    }
+}
+
+/* Result Section */
+.result-section {
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+    align-items: center;
+    text-align: center;
+    min-height: 300px;
+}
+
+.placeholder .icon-pulse {
+    font-size: 4rem;
+    margin-bottom: 1rem;
+    animation: pulse 2s infinite;
+}
+
+@keyframes pulse {
+    0% {
+        transform: scale(1);
+    }
+
+    50% {
+        transform: scale(1.1);
+    }
+
+    100% {
+        transform: scale(1);
+    }
+}
+
+.hidden {
+    display: none;
+}
+
+.score-circle {
+    width: 180px;
+    height: 180px;
+    border: 5px solid var(--primary);
+    border-radius: 50%;
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+    align-items: center;
+    margin-bottom: 1.5rem;
+    background: radial-gradient(circle, rgba(99, 102, 241, 0.1) 0%, transparent 70%);
+}
+
+#scoreValue {
+    font-size: 3rem;
+    font-weight: 800;
+    color: var(--text);
+}
+
+.score-circle p {
+    text-transform: uppercase;
+    font-size: 0.8rem;
+    letter-spacing: 2px;
+    color: #94a3b8;
+}
+
+/* Animations */
+.animate-fade-in {
+    animation: fadeIn 1s ease-out;
+}
+
+.animate-slide-up {
+    animation: slideUp 0.8s ease-out;
+}
+
+.animate-slide-up-delayed {
+    animation: slideUp 0.8s ease-out 0.2s backwards;
+}
+
+@keyframes fadeIn {
+    from {
+        opacity: 0;
+    }
+
+    to {
+        opacity: 1;
+    }
+}
+
+@keyframes slideUp {
+    from {
+        opacity: 0;
+        transform: translateY(30px);
+    }
+
+    to {
+        opacity: 1;
+        transform: translateY(0);
+    }
+}
+
+footer {
+    text-align: center;
+    margin-top: 3rem;
+    color: #64748b;
+    font-size: 0.9rem;
+}

student_marks_rnn_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29409cd915cafaa6e6f738015011063472d41074c74985eaecfa3d139f156745
+size 411720

templates/index.html

@@ -0,0 +1,127 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>GradePredict AI | Student Performance Analytics</title>
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style.css') }}">
+    <link href="https://fonts.googleapis.com/css2?family=Outfit:wght@300;400;600;800&display=swap" rel="stylesheet">
+</head>
+<body>
+    <div class="background-blobs">
+        <div class="blob blob-1"></div>
+        <div class="blob blob-2"></div>
+        <div class="blob blob-3"></div>
+    </div>
+
+    <main class="container">
+        <header class="glass-card animate-fade-in">
+            <h1>GradePredict <span class="accent">AI</span></h1>
+            <p>Advanced RNN-based Student Performance Forecasting</p>
+        </header>
+
+        <section class="prediction-grid">
+            <div class="glass-card input-section animate-slide-up">
+                <h2>Predict Your Score</h2>
+                <form id="predictionForm">
+                    <div class="input-group">
+                        <label for="num_courses">Number of Courses</label>
+                        <input type="number" id="num_courses" name="num_courses" placeholder="e.g. 5" min="1" max="100" required>
+                    </div>
+                    <div class="input-group">
+                        <label for="time_study">Daily Study Hours</label>
+                        <input type="number" id="time_study" name="time_study" placeholder="e.g. 4.5" step="0.1" min="0" max="24" required>
+                    </div>
+                    <button type="submit" id="predictBtn">
+                        <span class="btn-text">Calculate Score</span>
+                        <div class="loader-dots" id="loader">
+                            <span></span><span></span><span></span>
+                        </div>
+                    </button>
+                </form>
+            </div>
+
+            <div class="glass-card result-section animate-slide-up-delayed">
+                <div id="resultPlaceholder" class="placeholder">
+                    <div class="icon-pulse">🎓</div>
+                    <p>Enter your details to generate prediction</p>
+                </div>
+                <div id="resultOutput" class="output hidden">
+                    <div class="score-circle">
+                        <span id="scoreValue">0.00</span>
+                        <p>Marks</p>
+                    </div>
+                    <h3>Predicted Efficiency</h3>
+                    <p id="analysisText">Based on your input, here is your forecasted performance.</p>
+                </div>
+            </div>
+        </section>
+
+        <footer class="animate-fade-in">
+            <p>&copy; 2026 GradePredict AI | Powered by LSTM Recurrent Neural Networks</p>
+        </footer>
+    </main>
+
+    <script>
+        document.getElementById('predictionForm').addEventListener('submit', async (e) => {
+            e.preventDefault();
+            
+            const btn = document.getElementById('predictBtn');
+            const loader = document.getElementById('loader');
+            const btnText = btn.querySelector('.btn-text');
+            const placeholder = document.getElementById('resultPlaceholder');
+            const output = document.getElementById('resultOutput');
+            const scoreValue = document.getElementById('scoreValue');
+
+            // UI State: Loading
+            btn.disabled = true;
+            btnText.style.opacity = '0';
+            loader.style.display = 'flex';
+
+            const formData = {
+                num_courses: document.getElementById('num_courses').value,
+                time_study: document.getElementById('time_study').value
+            };
+
+            try {
+                const response = await fetch('/predict', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify(formData)
+                });
+
+                const data = await response.json();
+
+                if (data.marks !== undefined) {
+                    placeholder.classList.add('hidden');
+                    output.classList.remove('hidden');
+                    
+                    // Animate counter
+                    animateValue(scoreValue, 0, data.marks, 1000);
+                } else {
+                    alert('Error: ' + data.error);
+                }
+            } catch (err) {
+                alert('Connection Error: ' + err.message);
+            } finally {
+                btn.disabled = false;
+                btnText.style.opacity = '1';
+                loader.style.display = 'none';
+            }
+        });
+
+        function animateValue(obj, start, end, duration) {
+            let startTimestamp = null;
+            const step = (timestamp) => {
+                if (!startTimestamp) startTimestamp = timestamp;
+                const progress = Math.min((timestamp - startTimestamp) / duration, 1);
+                obj.innerHTML = (progress * (end - start) + start).toFixed(2);
+                if (progress < 1) {
+                    window.requestAnimationFrame(step);
+                }
+            };
+            window.requestAnimationFrame(step);
+        }
+    </script>
+</body>
+</html>

train_rnn.py

@@ -0,0 +1,112 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import SimpleRNN, Dense, LSTM, Dropout
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+import joblib
+import os
+
+# 1. Load Data
+def load_data(file_path):
+    df = pd.read_csv(file_path)
+    print("Dataset Head:")
+    print(df.head())
+    return df
+
+# 2. Preprocessing
+def preprocess_data(df):
+    X = df[['number_courses', 'time_study']].values
+    y = df['Marks'].values.reshape(-1, 1)
+
+    # Scaling
+    scaler_X = MinMaxScaler()
+    scaler_y = MinMaxScaler()
+
+    X_scaled = scaler_X.fit_transform(X)
+    y_scaled = scaler_y.fit_transform(y)
+
+    # Split
+    X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_scaled, test_size=0.2, random_state=42)
+
+    # Reshape for RNN: (samples, time_steps, features)
+    # Here time_steps = 1
+    X_train = X_train.reshape((X_train.shape[0], 1, X_train.shape[1]))
+    X_test = X_test.reshape((X_test.shape[0], 1, X_test.shape[1]))
+
+    return X_train, X_test, y_train, y_test, scaler_X, scaler_y
+
+# 3. Build Model
+def build_model(input_shape):
+    model = Sequential([
+        LSTM(64, activation='relu', input_shape=input_shape, return_sequences=True),
+        Dropout(0.2),
+        LSTM(32, activation='relu'),
+        Dense(16, activation='relu'),
+        Dense(1) # Output for regression
+    ])
+    
+    model.compile(optimizer='adam', loss='mse', metrics=['mae'])
+    return model
+
+# 4. Main Execution
+if __name__ == "__main__":
+    file_path = 'Student_Marks.csv'
+    if not os.path.exists(file_path):
+        print(f"Error: {file_path} not found.")
+        exit()
+
+    df = load_data(file_path)
+    X_train, X_test, y_train, y_test, scaler_X, scaler_y = preprocess_data(df)
+
+    print(f"X_train shape: {X_train.shape}")
+    print(f"y_train shape: {y_train.shape}")
+
+    model = build_model((X_train.shape[1], X_train.shape[2]))
+    model.summary()
+
+    # Training
+    print("\nStarting training...")
+    history = model.fit(
+        X_train, y_train,
+        epochs=100,
+        batch_size=8,
+        validation_split=0.1,
+        verbose=1
+    )
+
+    # Evaluation
+    print("\nEvaluating model...")
+    loss, mae = model.evaluate(X_test, y_test)
+    print(f"Test Loss (MSE): {loss:.4f}")
+    print(f"Test MAE: {mae:.4f}")
+
+    # Plot History
+    plt.figure(figsize=(10, 5))
+    plt.plot(history.history['loss'], label='Train Loss')
+    plt.plot(history.history['val_loss'], label='Val Loss')
+    plt.title('Model Loss (MSE)')
+    plt.xlabel('Epochs')
+    plt.ylabel('Loss')
+    plt.legend()
+    plt.savefig('loss_plot.png')
+    print("Loss plot saved as 'loss_plot.png'")
+
+    # Predictions
+    y_pred_scaled = model.predict(X_test)
+    y_pred = scaler_y.inverse_transform(y_pred_scaled)
+    y_actual = scaler_y.inverse_transform(y_test)
+
+    # Compare first 5
+    print("\nSample Predictions:")
+    for i in range(5):
+        print(f"Actual: {y_actual[i][0]:.2f}, Predicted: {y_pred[i][0]:.2f}")
+
+    # Save Model and Scalers
+    model.save('student_marks_rnn_model.h5')
+    joblib.dump(scaler_X, 'scaler_X.pkl')
+    joblib.dump(scaler_y, 'scaler_y.pkl')
+    print("\nModel saved as 'student_marks_rnn_model.h5'")
+    print("Scalers saved as 'scaler_X.pkl' and 'scaler_y.pkl'")