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. I have created this notebook to just try handful of ML regression algorithms via; sklearn pipeline.

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. TheilSen Regression

4. KNN Regressor

5. Decision Tree Regressor

6. Elastic Net

7. Ridge/Lasso

8. Stochastic Gradient Descent

9. Catboost

10. LightGBM

11. Gradient Boosting Regressor

12. Random Forest Regressor

13. Adaboost Regressor