This project aims to predict rental prices using machine learning models and real-world data. The repository covers the entire process, including data collection, preprocessing, feature engineering, model training, and evaluation.
The goal of this project is to accurately predict rent prices by analyzing features such as location, property size, number of rooms, and more. Various machine learning techniques are used and evaluated for performance, with the aim of creating an optimized prediction model.
To run this project locally, follow these steps:
- Clone the repository:
git clone https://github.com/demirelfth/rent-prediction.git- Navigate to the project directory:
cd rent-predictionThe dataset used for this project consists of real-world rental price data, including features like:
- Location (latitude, longitude)
- Property size (square meters)
- Number of rooms
- Building age
- Additional features like furnished status, heating type, etc.
Make sure to clean and preprocess the dataset before training the model.
This project uses a variety of machine learning algorithms, such as:
- Linear Regression
- Random Forest
We evaluate these models using metrics like Mean Squared Error (MSE) and R-squared.
Contributions are welcome! If you find any issues or have suggestions for improvement, feel free to open an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.