• NIKITA BHARAMBE
• Abhishek Jadhav
• Sharayu Saraf
• Vedant Pathak
• Prof. Vishal Chaudhary

We find that the positive effect of real estate in- vestment on economic growth is stronger in short term while in long term turn to be negative. That's why, housing investment is an important factor for short-term economy fluctuations and lead to downside risk in the long term. This is an important motivating factor for house price prediction and recommendatin. Machine learning which plays an important role in various sector from past years. such as healthcare, finance, Manufacturing, Advertising, Real Estate, etc. In this project, we are going to create a web application which is used by two types of users. First is buyer, who will search house for buying purpose according to his requirements. And Second is seller, where seller have to add some property details for predicting the house price, enter date for forecasting the price till that date and budget range for recommending best location. This project uses datasets, which includes house’s features, location and price. We are using different feature selection methods and feature extraction method with Multiple Linear Regression to predict the current house price and using content based recommendation system to recommend best location according to their budget in nearby area of interest.

Prediction using Catboost algorithm.

"House Price Prediction and Recommendation System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.f336-f339, June-2021, Available :http://www.jetir.org/papers/JETIR2106746.pdf

"House Price Prediction and Recommendation System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppf336-f339, June-2021, Available at : http://www.jetir.org/papers/JETIR2106746.pdf