🏠 Building a Machine Learning Property Price Predictor (From Web Scraping to Deployment

1. 1Scrape property listings from websites using Python and Beautiful Soup
2. 2Preprocess the scraped data by handling missing values and encoding categorical variables
3. 3Train a machine learning model using the preprocessed data to predict property prices
4. 4Evaluate the performance of the model using metrics such as mean absolute error and R-squared
5. 5Deploy the model using a cloud platform such as Heroku or AWS to create a web application

Data scientists and machine learning engineers can benefit from this project to improve property price prediction, while software engineers can learn from the deployment aspect

💡 Web scraping and machine learning can be combined to create a powerful property price prediction system

🏠 Build a machine learning property price predictor from web scraping to deployment! 📈