praveenobulreddy / usa-housing-price-prediction

In this project I have implemented 15 different types of regression algorithms including Linear Regression, KNN Regressor, Decision Tree Regressor, RandomForest Regressor, XGBoost, CatBoost., LightGBM, etc. Along with it I have also performed Hyper Paramter Optimization & Cross Validation.

The data which is used in this project has been taken from the kaggle. The dataset is of USA Housing Dataset which includes 7 columns including target variable "Price". In this task we have to predict the house prices in USA.

The project includes basic EDA, Outlier Analysis, Baseline Model Building, Model Comparison, Sklearn-Pipeline to Avoid Data Leakage, Cross Validation & Hyperparameter Tuning Using Randomsized Search CV & Prediction.

The Regression Algorithms which I have tested in this notebook are as follows:

1. Linear Regression

2. Robust Regression

3. Theil Sen Regression

4. Ridge Regression

5. Lasso Regression

6. Elastic Net Regression

7. Random Forest Regression

8. XGBoost Regression

9. Adaboost Regression

10. Gradient Boost Regression

11. LightGBM Regression

12. Cat Boost Regression

13. Decision Tree Regression

14. KNN Regression

15. Stochastic Gradient Descent Regression