Student Performance Predictor (ML Project)

Description

This is a Flask web application that uses Machine Learning to predict a student's performance index. Users input data such as study hours, sleep hours, previous scores, participation in activities and number of practiced questions, and the trained model outputs a predicted performance index.

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Technologies

• Python 3.9+
• Flask (web application framework)
• pandas, numpy (data handling)
• scikit-learn, scipy (machine learning)
• matplotlib, seaborn (for data analysis)
• dill (for saving)
• kaggle (for dataset download)

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Features

• Data Ingestion - load and split dataset
• Data Transformation - preprocessing and feature engineering
• Model Training - train and evaluate ML models
• Prediction Pipeline - reusable pipeline for inference
• Flask Web App - interactive UI for predictions

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Installation and Running

1. Clone the repository:

git clone https://github.com/Hanna-hk/Student_Performance_ML_project.git
cd Student_Performance_ML_project

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate    # Linux/Mac
venv\Scripts\activate       # Windows

3. Install dependencies:

pip install -r requirements.txt

4. Run the Flask server:

python app.py

5. Open in your browser:

http://127.0.0.1:5000/

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How It Works

1. Enter student data including study hours, sleep hours, previous scores, etc.
2. Click the "Predict Performance" button
3. View the predicted performance index with confidence metrics
4. Explore visualizations of how different factors affect performance

Model Performance

The machine learning model provides accurate predictions with:

• MAE Score: 1.65
• RMSE Score: 2.08
• Accuracy of the model is 98.84%