SuccessSage: Student Exam Performance Indicator and Predictor

SuccessSage is a comprehensive end-to-end machine learning project aimed at predicting student exam performances. It leverages a variety of educational and demographic data to provide insights and predictions, enabling educational stakeholders to better understand and improve student outcomes.

Features

• Performance Prediction: Utilizes a variety of machine learning models to predict student performance in exams based on their demographic and academic data.
• Model Optimization: Employs a grid search to find the best hyperparameters for each model, ensuring optimal performance.
• Data Pipeline: Includes complete data ingestion, transformation, and model training pipelines, which automate the process from raw data to predictions.
• Web Interface: An interactive Flask-based web interface for easy data input and result visualization.
• Logging and Error Handling: Comprehensive logging and custom exception handling to ensure traceability and ease of debugging.

Data Overview

The project utilizes a dataset containing several features that are indicative of student academic performance:

• Demographic Information: Includes gender, race/ethnicity, and whether the student receives a free/reduced lunch.
• Academic Background: Covers parental level of education and whether the student completed a test preparation course.
• Scores: Reading and writing scores are used as input features.

Data Processing

• Data Ingestion: Automatically ingests data from a CSV file, splits it into training and testing datasets.
• Data Transformation: Transforms categorical data using one-hot encoding and scales numerical features to prepare them for model training.
• Training and Test Datasets: The prepared data is split into training and testing sets to evaluate model performance.

Installation

1. Clone the Repository

   git clone https://github.com/Shubham235Chandra/SuccessSage.git
   cd SuccessSage

2. Set Up a Virtual Environment (optional but recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install Requirements

   pip install -r requirements.txt

Running the Application

1. Start the Flask App

   python app.py

   This will run the web application on localhost on port 5000.

2. Access the Web Interface

   • Open a browser and navigate to http://localhost:5000/.
   • Use the web form to input student data and receive predictions.

Project Structure

• app.py: Flask application's entry point.
• application.py: Manages routes and web form handling.
• predict_pipeline.py: Manages the prediction pipeline including preprocessing and model predictions.
• data_ingestion.py: Manages the ingestion and initial processing of data.
• data_transformation.py: Implements the preprocessing pipeline.
• model_trainer.py: Manages the training of machine learning models using various algorithms such as RandomForest, DecisionTree, GradientBoosting, and more.
• utils.py: Utility functions for serialization and other tasks.
• logger.py: Configures logging for monitoring.
• exception.py: Custom exception handling for robust error management.

Contributing

Contributions are welcome! Please fork the repository and submit pull requests with your proposed changes.

License

Distributed under the MIT License. See LICENSE file for more information.