This project involved collecting data through web scrapping, cleaning and processing data, and training a regression model to predict the home price based on the apartment's neighborhood, square feet, and the number of beds and baths. Finally, the model was hosted on a web page.
- BeautifulSoup was used for web scrapping.
- Data pre-processing includes data cleaning, handling missing values, and outlier detection (Python: pandas, numpy, matplotlib)
- Lasso model was implemented through grid search with a score of 83.7%. (Python: scikit-learn)
- The trained model was hosted through a Flask server for prediction (HTMML/CSS)
Final product:
