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New Project: Student Performance Prediction using Linear Regression #399

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# Student Performance Linear Regression Analysis

<p align="center">
<img src="assests/main.png" width=40% height=40%>
</p>

## 🛠️ Description

This open-source Python project performs **student performance analysis** using **Linear Regression**.
It aims to **predict student scores or academic outcomes** based on input features such as **study hours**, **attendance**, or **other performance metrics**.

It’s a beginner-friendly project to understand how **machine learning models**—especially regression algorithms—can be used to analyze real-world data and make predictions.

You can:
1. Explore the dataset and visualize student performance trends.
2. Train a Linear Regression model using scikit-learn.
3. Evaluate and visualize the model’s performance with error metrics and prediction plots.

Feel free to modify, improve, or expand this project as you wish!

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## 📘 Dataset Information

The dataset contains information related to student learning and outcomes.
Example columns may include:
- **Hours Studied** – number of hours a student studied.
- **Previous Scores** – marks from past tests or exams.
- **Attendance (%)** – percentage of classes attended.
- **Final Score / Performance** – target variable to predict.

The dataset can be customized or replaced with any CSV containing similar features.

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## ⚙️ Languages or Frameworks Used

This project uses:
- Python 🐍
- Pandas
- NumPy
- Matplotlib / Seaborn
- Scikit-learn

All dependencies are listed in the `requirements.txt` file.
To install them, run:

```bash
pip install -r requirements.txt

## 🤖 Author

GitHub Profile → [https://github.com/Hacknova49](https://github.com/Hacknova49)
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