House-Price-Prediction

Model deployment with flask framework, using Linear Regression to predict the sales value in the third month using rate of interest and sales of the first two months.

Deploy Machine Learning Models Using Flask to take your models from jupyter notebook to production.

env setup and Flask installaion

Create virtual env

py -m venv env

Activate virtual env

env/Scripts/activate

Install Flask in your env

pip install flask

set FLASK_APP = app.py

Dataset

Created dummy sales dataset for this project which has four columns — rate , sales_in_first_month, sales_in_second_month and sales_in_third_month

Project Structure

model.py (contains code for the machine learning model to predict sales in the third month based on the sales in the first two months.)
app.py (contains Flask APIs that receives sales details through GUI or API calls, computes the predicted value based on our model and returns it.)
request.py (uses requests module to call APIs defined in app.py and displays the returned value.)