Predicting rental prices is essential for both tenants and landlords to make informed decisions. This project aims to build a machine-learning model that can predict house rental prices in Lagos State. By analyzing various features such as location, number of bedrooms, and amenities, the model provides valuable insights into rent pricing trends.
The dataset used for this project contains a comprehensive collection of rental property information in Lagos State scraped from property.pro.ng. It includes features such as location, number of bedrooms, bathrooms, and more. You can find the data set here.
The libraries used in this project include:
- Pandas: Data manipulation and analysis.
- NumPy: Numerical computations and array operations.
- Matplotlib: Data visualization.
- Seaborn: Enhanced data visualization.
- Scikit-learn: Machine learning and predictive modelling.
The results of the Lagos Rent Prediction model demonstrate its ability to provide accurate rent price estimates. The model's performance is evaluated based on the R2-Square metrics, and its predictions can help both tenants and landlords make well-informed decisions.
You can read about the project here.
The webpage for the project was built with Python Flask, HTML, and JavaScript. You can visit the webpage here!!