Machine Learning based Predicting House Prices using Regression Technique

Machine Learning based Predicting House Prices using Regression Technique

Kunal Sapkal1, Pratik Nikam2, Rahul Rasal3 , Tilakram Yadav4  , Manoj Shelar5

Department of Computer Engineering, VPKBIET, Baramati

Department of Computer Engineering, VPKBIET, Baramati

Department of Computer Engineering, VPKBIET, Baramati

Department of Computer Engineering, VPKBIET, Baramati

Department of Computer Engineering, VPKBIET, Baramati

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Abstract - Predicting the price of a house helps for ascertain the house's selling price in a specific area and assist individuals in determining the ideal moment to purchase a home. Our goal in this machine learning task on house price prediction is to use data to develop a machine learning model capable of predicting housing values in the specified area. We will implement a linear regression algorithm on our dataset. By using real world data entities, we are going to predict the price of the house in that area. For better results we require data pre-processing units to increase the model's efficiency for this project we are using supervised learning, which is a part of machine learning. We have to go through different attributes of the dataset. This project provides us an overview on how to predict house prices using various machine learning models with the help of different python libraries. This suggested model is thought to be the most accurate one for estimating home prices and makes the most accurate predictions. This offers a succinct overview, which is necessary in order to forecast the price of the home. This project consists of what and how the house price model works with the assistance of machine learning technique using scikit-learn and which datasets we will be using in our proposed model.

Key Words:  house price, lasso regression, ridge regression, R-squared.