Linear Regression from Scratch (Gradient Descent)

This repository contains a simple implementation of Linear Regression using Gradient Descent in Python. The goal is to learn the relationship between study hours and exam scores from a small dataset.

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📌 Project Overview

• 🔢 Predict exam scores based on study hours.
• 🧮 Implements gradient descent manually without any ML libraries.
• 📉 Tracks error (MSE) across epochs.
• 📈 Plots for data, regression line, and MSE per epoch.

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📁 Files Included

File/Folder	Description
linear_regression.py	Core script containing the linear regression implementation
data2.csv	Dataset: study hours vs exam scores
images/	Folder containing generated plots

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🧪 Dataset

The dataset contains two columns:

• studytime (float): Number of hours studied
• score (float): Exam score received

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📷 Visualizations

📊 Data Points

📈 Linear Regression Fit

🧠 Loss (MSE) vs Epochs

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🚀 How to Run

python linear_regression.py

Make sure data2.csv is present in the same directory as linear_regression.py.

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🙋‍♂️ Author

Yash Dhankhar

Feel free to use, learn, and modify!

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