Student Performance Prediction System

Project Overview:

An end-to-end machine learning project that predicts student performance based on various factors like attendance, study hours, and past academic records. The system is integrated with a Flask web application for user interaction, allowing users to input data and get performance predictions.

Technologies Used:

• Machine Learning: Python, scikit-learn, Pandas, NumPy
• Web Framework: Flask
• Visualization: Matplotlib, Seaborn
• Frontend: HTML, CSS (for basic UI)

Main Features:

• User Interaction: A Flask-based web app that allows users to input student details (e.g., study hours, attendance) and get performance predictions.
• Data Preprocessing: Handles missing values, data normalization, and feature encoding to prepare data for modeling.
• Model Building: Various machine learning models (e.g., Decision Trees, Random Forest, Logistic Regression,SVM,Boosting ,Bagging) are trained to predict student performance.
• Real-Time Prediction: Users can interact with the app and input data to get real-time predictions on student performance.
• Model Evaluation: Evaluates the models using accuracy, precision, recall, and confusion matrix for performance analysis.
• Visualization: Displays relevant data visualizations like scatter plots, histograms, and performance metrics for better understanding.

Steps to Run the Application:

• Clone the repository: git clone https://github.com/shagun122/Student_Performance_Predication_System.git
• Install required dependencies: pip install -r requirements.txt
• Start the Flask app: python app.py
• Open a browser and navigate to http://127.0.0.1:5000/ to interact with the application.