🏠 Linear Regression Project

This project was completed as part of José Portilla’s Python for Machine Learning & Data Science Masterclass on Udemy.
It demonstrates how to build and evaluate a Linear Regression model to predict house prices based on numerical features.

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📋 Project Overview

The goal of this project is to understand the relationship between different housing features (like area, number of rooms, etc.) and the target variable — house price.

Steps followed:

1. Data Loading & Cleaning
2. Exploratory Data Analysis (EDA)
3. Feature Selection & Scaling
4. Model Training using Linear Regression
5. Model Evaluation using R² and RMSE metrics

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🧠 What I Learned

• How to prepare data for machine learning models
• Building and interpreting a linear regression model using scikit-learn
• Evaluating regression models with statistical metrics
• Visualizing results with Matplotlib and Seaborn

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⚙️ Tools & Libraries

• Python
• Pandas
• NumPy
• Matplotlib / Seaborn
• Scikit-learn

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📊 Results

• The model achieves good predictive accuracy for house price prediction.
• Evaluation metrics such as R², MAE, and RMSE are used to measure performance.

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🚀 Future Improvements

• Try Ridge and Lasso Regression for regularization.
• Add polynomial features for non-linear relationships.
• Compare model results with tree-based methods.

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📁 Files in this Repository

• Linear_Regression_Project.ipynb — Jupyter notebook
• README.md — Project overview

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👨‍💻 Author

Vinay N.
Beginner in Machine Learning | Python Enthusiast