Advanced house price predictuion

This project focuses on predicting house prices using advanced machine learning techniques and real estate data analysis.

The system estimates property prices based on various features such as location, area, number of rooms, building quality, and other housing characteristics.

Project workflow included:

- Data cleaning and preprocessing

- Handling missing values and outliers

- Feature engineering and feature selection

- Correlation analysis

- Encoding categorical variables

- Data normalization and scaling

Several machine learning models were implemented and compared, including:

- Linear Regression

- Random Forest Regressor

- XGBoost Regressor

- Gradient Boosting Regressor

The models were evaluated using:

- R² Score

- Mean Squared Error (MSE)

- Root Mean Squared Error (RMSE)

- Mean Absolute Error (MAE)