Project Overview: This project aims to build a machine learning model that predicts house prices based on various features such as the number of rooms, square footage, location, and more. The dataset used in this project comes from the Kaggle Housing Prices Competition. This project demonstrates the application of data preprocessing, feature engineering, and model evaluation techniques.
Motivation: The primary motivation behind this project is to explore regression techniques and feature engineering methods that can improve model performance. Understanding how different factors influence housing prices can be beneficial for real estate professionals, financial analysts, and anyone interested in the housing market.
Technologies Used: Programming Language: Python Libraries: Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn, XGBoost Tools: Jupyter Notebook, Git, GitHub