In this data science project, first I build a machine learning model using sklearn and liner regression using banglore home prices dataset from kaggle.com. Then I write a python flask server that uses the saved model to serve http requests. During model building I covered almost all data science concepts such as data load 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
- Jupyter notebook, visual studio code and pycharm as IDE
- Pyhton flask for http server
- Postman for testing
I don't have much expertise in frontend development, So I leave that part for the time being. But I will cover it in future for sure.