A machine learning project for predicting house prices using advanced regression techniques. This project includes comprehensive data preprocessing, feature engineering, and model optimization to achieve high accuracy in predictions.
- Introduction
- Features
- Installation
- Models Used
- Data Preprocessing
- Feature Engineering
- Contributing
- License
RealEstateValuePredictor is designed to predict house prices using a variety of regression models. It includes detailed data preprocessing steps to handle missing values and outliers, and feature engineering to enhance model performance.
- Comprehensive data preprocessing
- Advanced feature engineering
- Utilization of multiple regression models:
- LGBMRegressor
- XGBRegressor
- CatBoostRegressor
- Model optimization using GridSearchCV
- Feature importance analysis and visualization
- Clone the repository:
git clone https://github.com/rohanag03/House-Price-Prediction.git
- Install the required packages:
pip install -r requirements.txt
- LGBMRegressor: LightGBM model optimized for speed and performance.
- XGBRegressor: Extreme Gradient Boosting model known for its robustness.
- CatBoostRegressor: Categorical Boosting model that handles categorical data efficiently.
- Handling missing values
- Outlier detection and replacement
- Log transformation of skewed data
- Creation of new features to enhance model performance
- Encoding categorical variables
- Scaling and normalization of numerical features
Contributions are welcome! Please fork this repository and submit a pull request for any improvements or bug fixes.
This project is licensed under the MIT License. See the LICENSE file for more details.