Hands-on Project

Real Estate price Prediction

Steps:

1.introduction

2.Data Cleaning

3.Feature Engineering

4.Outliers Removal

5.Model Building

6.Python Flask server

7.Website or UI

8.Deploy machine learning model  to production AWS

Introduction:

This data science project series walks through step by step process of how to build a real estate price prediction website. We will first build a model using sklearn and linear regression using banglore home prices dataset from kaggle.com. Second step would be to write a python flask server that uses the saved model to serve http requests. Third component is the website built in html, css and javascript that allows user to enter home square ft area, bedrooms etc and it will call python flask server to retrieve the predicted price. During model building we will cover 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. Technology and tools wise this project covers, 1) Python 2) Numpy and Pandas for data cleaning 3) Matplotlib for data visualization 4) Sklearn for model building 5) Jupyter notebook, visual studio code and pycharm as IDE 6) Python flask for http server 7) HTML/CSS/Javascript for UI

Data Cleaning:

Feature Engineering:

Outliers Removal:

Model Building:

Python Flask Server:

Website or UI:

Deploy machine learning model to production AWS:

I will show you how to deploy machine learning model to production on amazon aws ec2 instance. We will use nginx web server that will server http requests. For AWS EC2 we will use ubuntu server on which we will deploy our web application as well as python flask server. Using nginx reverse proxy /api requests will be routed to python flask server running on same machine. Here is the timeline and list of topics we are covering today,