In this project I have implemented 14 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. 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