Deploy a Linear Regression Model to the web App for predicting housing price in Vancouver, British Columbia. The data used to train model is to be scraped from Craiglist, then EDA and feature engineering to be performed to extract maximum information from the data. We will try to work on accuracy to keep it up to the benchmark. Building the components needed by Flask microframework to create a web app. Using flask API we will get the input values from the user and predict the housing price. By this we can get experience of working on ML project from end-to-end.
Technologies to be used:
Python 3.6+ python packages: BS4/Selenium Numpy Flask Pandas Sklearn Seaborn Matplotlib Git HTML/CSS
Data to be scarped from: https://vancouver.craigslist.org/d/real-estate/search/rea