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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.

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mukulsinghal001/USA-Housing-Price-Prediction

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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

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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.

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