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
4. Clustering
- K Means Clustering
- Hierarchical Clustering