The problem that we are going to solve here is that given a set of features that describe a house in Boston, our machine learning model must predict the house price. To train our machine learning model with boston housing data, we will be using scikit-learn’s boston dataset. In this dataset, each row describes a boston town or suburb. There are 547 rows and 13 attributes (features) with a target column (price). https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.names https://github.com/KARIM-MADRID/PY_ML_Houseprediction_Deployment
https://github.com/KARIM-MADRID/PY_ML_Houseprediction_Deployment
This repo consists of: 1)Flask(Web framework) in:app.py 2)Dataset: House_modified.csv 3)Machine learning model : house_price_prediction.py 4)Template:index.html 4)CSS & fonts in : static folder.
These 4 directories will help you to create and deploy a finished machine learning model onto the website.
