🧠 Student Performance Prediction

This is a machine learning project where I built a model to predict whether a student will pass or fail based on their scores and background information.

📌 Project Overview

The goal is to help understand which factors affect student performance and make predictions based on:

• Gender
• Lunch type
• Test preparation
• Math, Reading, and Writing scores
• Parental level of education

The project includes:

• Data cleaning and exploration
• Visualisations to find patterns
• Model training (Logistic Regression, Random Forest, etc.)
• Performance evaluation
• A simple Streamlit web app for interaction

🛠️ Technologies Used

• Python
• Pandas, NumPy
• Seaborn & Matplotlib (for visualisation)
• Scikit-learn (for modelling)
• Streamlit (for the web app)
• Git & GitHub

🚀 How to Run the App

1. Clone the repo:

   git clone https://github.com/your-username/student-performance-prediction.git
   

2. Install the required libraries:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run app.py

📊 Model Insights

The model uses real student data and shows how different factors impact results. It also handles class imbalance using techniques like class weighting or SMOTE.

📄 License

This project is open source and available under the MIT License.