House Price Prediction – ML Regression Project

This project uses various regression algorithms to predict house prices based on multiple housing features. The dataset used is similar to the popular Kaggle House Prices: Advanced Regression Techniques dataset.

📌 Problem Statement

Predict the final sale price of houses based on a variety of features like size, neighborhood, year built, number of rooms, etc. This is a classic supervised regression problem.

🧠 Machine Learning Workflow

🔹 Models Trained

• Linear Regression – baseline model
• Decision Tree Regressor
• Random Forest Regressor – best performing
• Hyperparameter Tuning with GridSearchCV to fine-tune Random Forest

📊 Evaluation Metrics

• Cross-validation (CV): 5-fold cross-validation
• Root Mean Squared Error (RMSE): Primary metric used for evaluation

🛠️ Tools & Libraries

• Python (JupyterLab)
• Pandas, NumPy, Matplotlib, Seaborn
• Scikit-learn
• Joblib (to save the trained model)

⚙️ Project Structure:

---house-price-prediction/ ├── notebook.ipynb # Jupyter notebook with full code and analysis ├── model.joblib # Final trained model (Random Forest) ├── sample_data.csv # Sample dataset ├── requirements.txt # Python dependencies ├── README.md # Project overview └── .gitignore