House Price Prediction using Machine Learning

📌 Project Overview

This project predicts house prices based on property features such as size, location, condition, and nearby facilities using machine learning regression models.

🚀 Technologies Used

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

🧠 ML Techniques

• Feature Engineering
• Linear Regression
• Random Forest Regression
• Model Evaluation (MAE, RMSE, R²)

▶️ How to Run

1. Install dependencies
   pip install -r requirements.txt
2. Train model
   python src/train.py

📊 Results

Random Forest achieved significantly better performance compared to baseline regression models.

• MAE: 44902.60153676117
• RMSE: 99974.06464865225
• R2: 0.9135610922095772
• Accuracy: 82.15%

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