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🏡 ImmoEliza Real Estate Pricing Project

Project Overview

The ImmoEliza project aims to develop a machine learning model that predicts real estate prices in Belgium using data from Immoweb. The dataset will include over 10,000 real estate listings with at least 18 features per listing, including location, price, property type, and condition. The model is intended to provide accurate property price predictions based on available data.

🚀 Goals:

• Scrape real estate data from Immoweb.
• Clean and preprocess the collected data.
• Build a machine learning model to predict real estate prices.
• Deploy the model as a user-friendly tool for real estate pricing insights.

🗓 Timeline:

• Data Collection: October 1st - October 10th, 2024
• Model Development: October 11th - October 15th, 2024
• Deployment and Testing: October 16th - October 20th, 2024

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📂 Structure

The project is divided into multiple phases, each of which focuses on a specific part of the AI pipeline:

1. Web Scraping: Collect real estate listings and detailed features using Selenium and BeautifulSoup.
2. Data Processing: Clean, transform, and preprocess the data using Python libraries like pandas, numpy, and regex.
3. Modeling: Build a price prediction model using machine learning algorithms such as linear regression, random forests, or gradient boosting.
4. Deployment: Deploy the model as a web-based tool using Flask or Streamlit.

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🌱 Initial Steps:

1. Scraping Immoweb Data: Using Selenium to automate the extraction of real estate listing information from Immoweb.
2. Processing Collected Data: Cleaning data, handling missing values, and creating new features (e.g., price per square meter).
3. Building ML Model: Creating a machine learning pipeline to train, validate, and test the real estate price prediction model.

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💡 Key Components:

• Web Scraping: Automate data collection from Immoweb using Selenium and BeautifulSoup.
• Data Cleaning: Preprocess raw scraped data to ensure accuracy and consistency.
• Feature Engineering: Create new features based on existing data to improve model accuracy.
• Modeling: Implement machine learning models for price prediction.
• Deployment: Build an accessible user interface to query the model for predictions.

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👩‍💻 Skills and Technologies:

• Python: The core language for data collection, cleaning, and modeling.
• Selenium & BeautifulSoup: Tools for web scraping and automating data collection.
• pandas & numpy: Libraries for data manipulation and preprocessing.
• Scikit-learn: For machine learning model implementation.
• Flask/Streamlit: For building a web application to interact with the predictive model.

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📅 Project Timeline

Phase	Description	Deadline
Data Collection	Web scraping real estate data	October 10, 2024
Data Cleaning	Clean and preprocess the data	October 11, 2024
Model Development	Build machine learning models	October 15, 2024
Model Deployment	Deploy the model as a web tool	October 20, 2024

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📞 Contact Information:

In case of any questions or help, feel free to reach out:

• Lead Developer: [Korostelova Anastasiia]
• Email: [email protected]

P.S. I did not manage to collect all the necessary information, but I tried very hard and will continue to try to improve the project.

☀️ Happy Coding!

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