This data science project series walks through step by step process of how to build a real estate price prediction website. I will first build a model using sklearn and linear regression using bangaluru housing prices dataset from kaggle.com. Second step would be to write a python flask server that uses the saved model to serve http requests. Third component is the website built in html, css, bootstrap and javascript that allows user to enter home square ft area, bedrooms etc and it will call python flask server to retrieve the predicted price. During model building I will cover data science concepts such as data loading and cleaning, outlier detection and removal, feature engineering, dimensionality reduction, gridsearchcv for hyperparameter tunning, k fold cross validation etc.
- Python
- Numpy and Pandas for data cleaning
- Matplotlib for data visualization
- Sklearn for model building
Use banglore_home_prices_model.pickle o test the model.
😄 Happy Coding