This project aims to predict house prices using advanced regression techniques. It utilizes machine learning algorithms to analyze and predict the sale prices of houses based on various features.
The House Prices - Advanced Regression Techniques project is part of a Kaggle competition where participants are tasked with predicting house prices using regression techniques. In this project, we explore the dataset, preprocess the data, build predictive models, and evaluate their performance.
The dataset used in this project is sourced from the Kaggle competition House Prices - Advanced Regression Techniques. It consists of various features such as the size of the house, number of bedrooms, location, etc., along with the corresponding sale prices.
To run the code in this project, you'll need the following dependencies:
- Python 3.x
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn
You can install these dependencies using pip:
pip install numpy pandas matplotlib seaborn scikit-learn
To use this project:
- Clone the repository:
git clone https://github.com/your_username/house-prices-advanced-regression.git
- Navigate to the project directory:
cd house-prices-advanced-regression
- Run the Jupyter notebooks to explore the data, preprocess it, build models, and evaluate results:
jupyter notebook
- Follow the instructions provided in the notebooks to execute each step.
The predictive models built in this project achieved the following performance metrics:
- Mean Absolute Error (MAE): [MAE Value]
- Root Mean Squared Error (RMSE): [RMSE Value]
- R-squared (R2): [R2 Value]
Detailed analysis and visualizations of the results can be found in the project notebooks.
Contributions to this project are welcome. You can contribute by:
- Opening an issue to report a bug or suggest an enhancement.
- Forking the repository and submitting a pull request for improvements.
This project is licensed under the MIT License.