Agadir House Prices Prediction

Description

Welcome to the Agadir House Prices Prediction tool! This project leverages machine learning to estimate real estate prices in Agadir, Morocco. The tool uses a Gradient Boosting Regressor model to provide accurate price predictions based on various property features.

How It Works

1. Data Collection:

   • We collect comprehensive real estate data from Mubawab, a leading property listing platform in Morocco. This data includes:
     • Property Prices: Historical and current sale prices.
     • Property Features: Size (square meters), number of bedrooms, number of bathrooms, type of property (apartment, villa, etc.), and location details within Agadir.

2. Data Preparation:

   • Cleaning: The raw data is processed to handle missing values, outliers, and inconsistencies. This step ensures data quality and reliability.
   • Transformation: Data is transformed into a format suitable for machine learning. This includes:
     • Normalization: Scaling features to a uniform range to ensure model performance.
     • Encoding: Converting categorical variables (like property type) into numerical values.
     • Feature Engineering: Creating new features from existing data to enhance model accuracy (e.g., price per square meter).

3. Model Training:

   • Gradient Boosting Regressor: We use an advanced Gradient Boosting Regressor model, which builds an ensemble of weak learners to improve prediction accuracy.
   • Bayesian Optimization: We optimize hyperparameters of the Gradient Boosting Regressor using Bayesian methods, which intelligently searches for the best parameters to enhance model performance.
   • Validation: We employ cross-validation techniques to evaluate the model's performance and avoid overfitting. This involves splitting the data into training and validation sets to test the model's predictive power.

4. Model Hosting:

   • Deployment: The trained model is packaged and deployed on a server using Docker. This allows for:
     • Scalability: Handling multiple user requests simultaneously.
     • Portability: Ensuring the model runs consistently across different environments.
   • API Integration: The server exposes an API endpoint that receives property details from the web interface and returns price estimates.

5. Real-Time Prediction:

   • Web Interface: Users interact with a user-friendly web interface to input property details such as size, location, and features.
   • Request Handling: The web application sends a POST request with the property details to the server's API.
   • Price Estimation: The server processes the request using the trained model and returns an estimated price in real-time.
   • User Feedback: The estimated price is displayed on the web interface, allowing users to see predictions immediately.

By following these steps, we provide users with a powerful tool for estimating real estate prices in Agadir, leveraging the latest advancements in machine learning and data science.

Contact

Portfolio: https://elqadi.me

LinkedIn: Hassan EL QADI