📈 Linear Regression Implementation

📌 Overview

This repository demonstrates the implementation of Linear Regression in Python using Scikit-learn. It covers essential steps such as data preprocessing, model training, performance evaluation, and result visualization. The project is useful for anyone looking to understand and apply linear regression techniques effectively.

🔍 Key Features

Data Preprocessing – Handling missing values, feature scaling, and encoding.

Model Training – Implementing Simple and Multiple Linear Regression.

Performance Metrics – Evaluating the model using RMSE, MAE, and R² Score.

Visualization – Scatter plots, regression line plotting, and residual analysis. 🚀 Getting Started

Clone the repository:

git clone https://github.com/vaishnavibhutada/linear_regression_python.git cd linear_regression_python

Install dependencies:

pip install -r requirements.txt

Run the notebook or scripts:

jupyter notebook practical_3.ipynb

📊 Visualization Examples

Regression Line on Data Scatter Plot

Residual Error Distribution

🛠 Technologies Used

Python 🐍

Pandas

NumPy

Scikit-learn

Matplotlib

Seaborn