This project focuses on predicting house prices using the King County housing dataset. Four different regression models were implemented and compared: Linear Regression, Ridge Regression, Decision Tree Regression, and XGBoost Regression. The project includes data preprocessing, exploratory data analysis, feature engineering, model training, and evaluation. The goal is to determine the most effective model for accurate house price prediction.

Key features of this project:

• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Feature engineering
• Model training and hyperparameter tuning
• Evaluation of model performance using metrics such as Mean Squared Error (MSE), R-Squared (R²), and Mean Absolute Error (MAE)
• Visualization of model performance and feature importance

Explore the code and insights derived from this project to understand the intricacies of house price prediction and the comparative performance of different regression techniques. Open to collaboration and feedback!

Languages and Tools:

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

Feel free to reach out for any questions or collaboration opportunities!