Dataset is taken from Kaggle.

A prediction system is used in this project to estimate the house's selling price based on various features. Two files, train, and test are provided and the price of the test data is to be estimated.. Two files, train, and test are provided and the price of the test data is to be estimated.

Data Correlation technique is used to find the correlation between the feature and target. Then the features are analyzed in their descending correlation values with the sales price.

The Algorithms used for prediction: -

1. Gradient Boosting Regression.
2. Random Forest Regression.
3. Linear Regression.
4. TheilSen Regressor.
5. RANSAC Regressor.
6. Lasso Regression.
7. Decision Tree.
8. Support Vector Regression.
9. Catboost Regression.

Metrics used: -

1. Rsquare
2. RMSE
3. MAE

Model Evaluation: -

On the basis of the values of metrics obtained, Gradient Boosting Regression is found to be the best model with a Rsquare value 0.890440 or 89.04%, RMSE value 0.016931, and MAE value 0.086820.