RANDOM FORESTS, GBM,XGBOOST, LIGHTGBM,CATBOOST

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1-Random Forests

2-GBM

3-XGBoost

4-LightGBM

5-CatBoost

6-FeatureImportance

7-Hyperparameter Optimization with RandomSearchCV (BONUS)

8-Analyzing Model Complexity with Learning Curves (BONUS)

9-Visualizing the Decision Tree

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DECISION TREE CLASSIFICATION:CART

1-Exploratory Data Analysis

2-Data Preprocessing & Feature Engineering

3-Modeling using CART

4-Hyperparameter Optimization with GridSearchCV

5-Final Model

6-Feature Importance

7-Analyzing Model Complexity with Learning Curves (BONUS)

8-Visualizing the Decision Tree

9-Extracting Decision Rules

10-Extracting Python/SQL/Excel Codes of Decision Rules

11-Prediction using Python Codes

12-Saving and Loading Model

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END TO END DIABETES MACHINE LEARNING PIPELINE I

1-Exploratory Data Analysis

2-Data Preprocessing & Feature Engineering

3-Base Models

4-Automated Hyperparameter Optimization

5-Stacking & Ensemble Learning

6-Prediction for a New Observation

7-Pipeline Main Function

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END TO END DIABETES MACHINE LEARNING PIPELINE II

1-Helper Functions

2-Pipeline Main Function

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END TO END DIABETES MACHINE LEARNING PIPELINE III

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KNN

1-Exploratory Data Analysis

2-Data Preprocessing & Feature Engineering

3-Modeling & Prediction

4-Model Evaluation

5-Hyperparameter Optimization

6-Final Model

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SALES PREDICTION WITH LINEAR REGRESSION

1-Simple Linear Regression with OLS Using Scikit-Learn

2-Guess

3-Prediction Success

4-Multiple Linear Regression

5-Model

6-Guess

7-Evaluating Prediction Success

8-Simple Linear Regression with Gradient Descent from Scratch