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.