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:
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Linear Regression
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Robust Regression
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Theil Sen Regression
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Ridge Regression
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Lasso Regression
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Elastic Net Regression
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Random Forest Regression
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XGBoost Regression
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Adaboost Regression
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Gradient Boost Regression
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LightGBM Regression
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Cat Boost Regression
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Decision Tree Regression
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KNN Regression
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Stochastic Gradient Descent Regression