mukulsinghal001 / USA-Housing-Price-Prediction

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.

USA House Price Prediction Using Regression

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