Boston-House-price-prediction-using-regression

This is an Applied Machine learning project on Predicting House prices, using Boston housing dataset.

The folder 'notebooks' contains files linearRegression.ipynb , pymachineproject.ipynb ,RandomForrestRegressor.ipynb.

These Jupyter Notebook files, contain the entire code that is required, for training, evaluating and finally testing the machine learning models "linear regression", "XGBoost regression", "Randomforrest regression" respectively on the Boston housing dataset, after Preprocessing it.

The File app.py contains the code, for running the Web API of the final model ('XGBoost regression'), chosen after Model evaluation.

Packages Required for Running the Web API:

flask.

xgboost.

pandas.

joblib.

numpy.

Running the web app:

Download all the files in the repository.Transfer these files('app.py','templates','static','Xgb.pkl') to your python directory. Run the file app.py in the python command line. Finally,the web application runs on your localhost, on your default web browser.