This project focuses on predicting house prices using machine learning models. It leverages various features like location, square footage, number of rooms, and other factors to determine the estimated house price. The dataset is analyzed through exploratory data analysis (EDA), feature engineering, and model building to ensure accurate predictions.
The dataset used contains information on:
- House Features: Square footage, number of bedrooms, bathrooms, and more.
- Location Data: Region, zip code, or city.
- Market Trends: House price fluctuations over time.
-
Data Preprocessing:
- Handling missing values.
- Encoding categorical variables.
- Feature scaling and transformation.
-
Exploratory Data Analysis (EDA):
- Visualizing house price trends.
- Correlation between features and price.
- Identifying outliers and anomalies.
-
Model Training:
- Linear Regression
- Decision Trees
- Random Forest
- Gradient Boosting
-
Model Evaluation:
- Mean Squared Error (MSE)
- R-squared Score
- Cross-validation performance
- Clone this repository:
git clone https://github.com/VIPULbunny/House-price-prediction.git
- Install required dependencies:
pip install numpy pandas matplotlib seaborn scikit-learn
- Run the Jupyter Notebook:
jupyter notebook
- Most important factors affecting house prices identified.
- Best-performing model selected based on evaluation metrics.
- Recommendations for real estate pricing strategies included.
This project is licensed under the MIT License.
Contributions are welcome! If you’d like to improve the analysis or add new models:
- Fork the repository.
- Create a feature branch:
git checkout -b feature-branch - Commit your changes:
git commit -m "Added new model" - Push to GitHub:
git push origin feature-branch - Open a Pull Request 🚀
For queries or collaborations, reach out via email or open an issue on GitHub.
⭐ If you find this project useful, please give it a star! 🌟