Predicting-House-Prices-in-Python-LR-DTR-RFR-XGboost

Using Linear Regression, Decision Tree Regressor, Random Forest Regressor, and XGboost Regressor Algorithms to predict house prices

Project 1: House Price Predictor : Project Overview

• Applied the Machine Laerning Algorithms : Linear Regression, Decision Tree Regression, Random Forest Regressor, and XGboost Regressor to predict House prices
• After Exploratory Data Analysis, feature engineering is done to extract useful features to improve the accuracy of the model
• Significant features through Forward feature Selection are used to train the linear regression model, and the best model is selected accordingly.
• Hyperparameter tunning is done on Decision Tree Regressor, Random Forest Regressor, and XGboost Regressor to find optimal parameters for the model using RandomSearchCV
• Optimal parameters are used to reach the best Random Forest Regressor model
• Out of four , the best performing model is found to be of a XG boost Regressor.

About the Dataset

The dataset used in this project is obtained from Kaggle