House Price Prediction

🏡 House Price Prediction

📂 GitHub Repository

🔗 Link to GitHub Repo

📌 Project Overview

Developed a machine learning model to predict house prices using various property features. The project focused on data preprocessing, feature engineering, model selection, and optimization to improve prediction accuracy.

📂 Dataset Used

• The dataset consists of various property attributes, including square footage, number of bedrooms, bathrooms, year built, and more.
• Preprocessing steps were applied to clean and enhance data quality for better predictions.

🔹 Technologies Used

• Programming Language: Python
• Libraries: Pandas, NumPy, Scikit-Learn, XGBoost, Matplotlib, Seaborn
• Tools: Jupyter Notebook, VS Code

🔹 Data Preprocessing

• Handled missing values using appropriate imputation techniques.
• Identified and removed outliers to maintain data quality.
• Applied feature scaling (standardization/normalization) to numerical columns.

🔹 Feature Engineering

• Created new features like Total Square Footage, Total Bathrooms, House Age, and Years Since Remodel to enhance model performance.
• Introduced binary indicators for key property features (e.g., presence of a wood deck).
• Used Recursive Feature Elimination (RFE) to select the most important features, improving model efficiency.

🔹 Model Development & Evaluation

• Trained multiple regression models, including Linear Regression, Random Forest, Gradient Boosting, and XGBoost.
• Tuned hyperparameters using RandomizedSearchCV for optimal performance.
• Evaluated models using RMSE, MAE, and R² Score, with Gradient Boosting Regressor achieving the highest R² score of 0.89 on the test set.

📊 Results & Impact

• Feature engineering and model tuning significantly improved prediction accuracy.
• Gradient Boosting was chosen for its ability to handle non-linear relationships and minimize overfitting.
• The final model provided reliable price predictions, making it useful for real estate analysis.

🔮 Future Improvements

• Deploy the model as a web application for real-time predictions.
• Explore deep learning models for further accuracy improvements.
• Integrate additional external factors such as location trends and economic indicators.