Welcome to the Real-Estate Price Predictor repository! This project aims to predict real-estate prices using machine learning algorithms.
Real-Estate Price Predictor is a machine learning project developed to assist users in estimating the price of real estate properties based on various features such as location, size, amenities, etc. The project utilizes supervised learning algorithms to train models on historical real-estate data and predicts prices for new properties.
The dataset used in this project consists of historical real-estate data containing information about properties such as size, location, number of bedrooms, number of bathrooms, amenities, and sale prices.
The features used for predicting real-estate prices include:
- Size of the property (in square feet)
- Location (latitude and longitude)
- Number of bedrooms
- Number of bathrooms
- Amenities (swimming pool, garden, garage, etc.)
- Distance to amenities (schools, hospitals, supermarkets, etc.)
The project utilizes various machine learning models for predicting real-estate prices, including:
- Linear Regression
- Random Forest
- Gradient Boosting
- Support Vector Regression
- Neural Networks
The performance of each model is evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) score to assess their accuracy in predicting real-estate prices.
To use the Real-Estate Price Predictor:
- Install the required dependencies by running:
pip install -r requirements.txt. - Clone the repository to your local machine.
- Preprocess the dataset and train the machine learning models using the provided scripts.
- Use the trained models to predict real-estate prices for new properties.
- Evaluate the performance of the models using the provided evaluation metrics.
Contributions to the Real-Estate Price Predictor project are welcome! If you have any suggestions, feature requests, or bug reports, please feel free to open an issue or submit a pull request.
This project is licensed under the MIT License. Feel free to use, modify, and distribute the code for personal or commercial purposes.