1. Data Pre-processing

• Importing Libraries
• Importing Data sets
• Handling the missing data values
• Encoding categorical data
• Split Data into Train data and Test data
• Feature Scaling

2. Regression

• Simple Linear Regression
• Multi Linear Regression
• Polynomial Regression
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression

3. Classification

• Logistic Regression
• K Nearest Neighbors Classification
• Support Vector Machine
• Kernel SVM
• Naive Bayes
• Decision Tree Classification
• Random Forest Classification

4. Clustering

• K-Means Clustering
• Hierarchical Clustering

5. Association Rule

• Apriori
• Eclat

6. Reinforcement Learning

• Upper Confidence Bounds
• Thompson Sampling

7. Natural Language Processing

• AWS Comprehend

8. Deep Learning

• Artificial Neural Networks (ANN)
• Convolutional Neural Networks (CNN)

9. Dimensionality Reduction

• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Kernel PCA

10. Model Selection

• Grid Search
• K-fold Cross Validation
• XGBoost

11. Data Visualization

• Matplotlib library in Python
• Tableau
• Power BI
• Grafana

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This is an open project and contribution in all forms are welcomed. Please follow these Contribution Guidelines

Adhere to the GitHub specified community code.

Check the official MIT License here.