House Price Prediction 🏠

Housing Market is among most volatile markets. Various parameters affect the pricing of a unit. Due to which many genuine but also bogus factors can be brought in to manipulate prices . Aim here is to predict pricing considering various genuine factors while learning and implementing statical algorithms, Machine Learning, Data Analytics using Python , Pyspark, MongoDB, Power BI and to deploy it using User interface base models in Heroku Cloud.

ML-Notebook 👨‍💻

We have performed different Regression models to predict the price of Houses based on Featured selected. XGBoost achieved R2 score of 0.8882 which is better than other Machine Learning models

1. Linear Regression
2. Lasso Regression
3. Ridge Regression
4. Elastic Net Regression
5. DecisionTree Regression
6. RandomForest Regression
7. XGBoost Regression

Visualization 👨‍🏫

Data Visualization is graphical representation of information and data by using tools like charts,graphs,maps.This tools provide an accessible way to see and understand trends,patterns in data. We have used PowerBI for Visualization

connect on email [email protected]

we have deployed our project on Heroku cloud :

https://housepricepredictor01.herokuapp.com/