Welcome to the House Price Prediction project! This project predicts house prices using a Random Forest model implemented in Python. The model is built with the help of various libraries and tools including Scikit-learn, Pandas, NumPy, and more. The project is developed using PyCharm and includes HTML, CSS, and JavaScript files for a web-based user interface.
This project leverages a Random Forest model to predict house prices based on features provided in a dataset. The dataset used in this implementation is sourced from Kaggle, but it can be replaced with any other dataset that follows a similar format. The model achieves an R-squared score of approximately 0.73.
- Random Forest Regression Model: Utilizes a Random Forest algorithm to predict house prices.
- Web Interface: Includes HTML, CSS, and JavaScript files for user interaction.
- Flexible Dataset: Allows for changing datasets while maintaining similar performance.
- Python 3.x
- Required Python libraries:
pandas,numpy,scikit-learn,flask(for web interface)
To get started with this project, you'll need to set up your environment and install the required dependencies. Follow the steps below:
Clone the Repository
git clone https://github.com/vinit105/HousePricePrediction.git
cd house-price-predictionhouse-price-prediction(Project)
│
├── app.py
├── train_model.py
├── requirements.txt
├── templates/
│ ├── index.html
│ ├── style.css
│ └── script.js
└── README.md
- Mean Absolute Error (MAE): 7296291.85
- Mean Squared Error (MSE): 202192022318005.72
- Root Mean Squared Error (RMSE): 14219424.12
- R² Score: 0.73
- Cross-Validated RMSE: 13088487.70
Feel free to contribute to the project by submitting issues or pull requests. Your suggestions and improvements are welcome!
- Kaggle for providing the dataset.
- Scikit-learn for the machine learning library.
- Flask for the web framework.