USA housing price prediction using ML

Keywords: USA Housing Price,Prediction,Machine Learning

Background

There is a huge boom in the real estate market as people are tending to purchase their dream homes and live happily in the USA (United States of America). House prices are constantly fluctuating based on different features such as the number of rooms, the average age of the house, and the average income of the people in the area. The prediction of house prices will help both the real estate agencies and the people to buy houses by planning the budget and working accordingly. In this work, the author has tried to predict house prices using different regression techniques and find the best-fit model to obtain better accuracy. This document consists of linear regression, Random Forest Regression, Support vector Regression, and Decision Tree Regression

Aim & Objectives

Aim
The main aim of this project is predicting the price of the house based on the location and relevant features historical data.
Objective
• Data Cleaning and Pre-Processing.
• Data Visualisation.
• Outlier Detection and imputation.
• Build the ML model.
• LIME and SHAP For Defining The Models.

Research Methodology

various regression algorithms like linear regression, SVR,KNNR,DT,RF

Software & Tools

Jupyter, Google Collab

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