🎓 student-score-predictor - Predict Student Scores Easily

📌 Project Description

The Student Score Predictor uses Machine Learning to estimate a student's final exam score. The model considers factors like study habits and academic history.

This project is beginner-friendly and built with Python and Scikit-learn. You can easily understand how different factors affect a student's performance.

🧠 Objective

The goal is to create a model that can predict a student’s performance based on:

• 📚 Study Hours
• 😴 Sleep Hours
• 🏫 Attendance Percentage
• 📝 Previous Exam Score

📊 Machine Learning Models Used

We trained two regression models:

1. Linear Regression
2. Random Forest Regressor 🌲

The model that performs best is saved and ready for future predictions.

📚 Dataset Information

The dataset contains synthetic student performance data. Here’s what each feature represents:

Feature	Description
study_hours	Number of hours studied per day
sleep_hours	Average sleep hours per night
attendance	Attendance percentage
previous_score	Score from the previous exam
final_score	Final exam score (this is the target variable)

🚀 Getting Started

To get started with the Student Score Predictor, follow these steps:

1. System Requirements

Ensure your computer meets the following requirements:

• Windows, macOS, or Linux operating system.
• At least 4GB of RAM.
• A stable internet connection for downloading files.

2. Download & Install

To download the latest version of the application, visit the link below:

Download Student Score Predictor

After visiting the link, follow these steps:

1. Locate the latest release at the top of the page.
2. Click on the appropriate file for your operating system.
3. Save the file to your computer.

3. Run the Application

Once the download is complete:

1. Locate the downloaded file on your computer.
2. Double-click the file to run the application.
3. Follow the on-screen instructions to input your data and see your score prediction.

📊 How to Use the Application

1. Input Data: Enter your study hours, sleep hours, attendance percentage, and previous exam score.
2. View Prediction: Click "Predict" to see how your factors influence your expected score.
3. Adjust Inputs: Modify the input values to explore different scenarios.

🌟 Additional Features

• User-Friendly Interface: The application features a clean interface for easy navigation.
• Data Visualization: View simple graphs representing your predicted score based on input data.

📞 Support

If you have any questions or feedback, feel free to reach out. You can find us on our GitHub page or in the issues section of this repository.

⚙️ Future Improvements

We plan to improve the application by adding:

• More regression models for broader predictions.
• User account features for personalization.
• Online data tracking options for returning users.

📖 License

This project is licensed under the MIT License. Feel free to use it for personal or educational purposes.