1. Regression:

• linear model

  • Linear Regression ✓
  • Ridge Regression ✓
  • Lasso Regression ✓
  • ElasticNet Regression ✓

• KNN Regression (with 2 distance metric) ✓

  • uniform KNN ✓
  • distance Weight KNN ✓

• Tree Regression

  • Decision Tree Regression
  • Random Forest Regression

• Support Vector Regression (SVR)

2. Clustering:

• K-Means
• Hierarchical Clustering
• DBSCAN
• Mean Shift Clustering

3. Classification:

• KNN (with 2 distance metric) ✓
  • uniform KNN ✓
  • distance Weight KNN ✓
• Tree
  • Decision Tree ✓
  • Random Forest ✓
• Ensemble
  • AdaBost ✓
• Regression :
  • Logistic Regression ✓
  • Ridge Regression
  • Lasso Regression
  • ElasticNet Regression
• Bayesian
  • Gaussian ✓
  • Multinomial ✓
• Minimum Distance Classifier ✓
• Support Vector Machine (SVM)
• Perceptron ✓

3. Dimensionality Reduction:

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

4. Normalization data

• sandardScaler ✓
• MinMaxScaler ✓
• MaxAbsScaler ✓
• RobustScaler ✓
• Normalizer ✓

5. metrics

• regression :

  • MSE ✓
  • MAE ✓
  • R2-Score ✓

• Classification:

  • accourcy ✓
  • recall ✓
  • prescion ✓
  • f1-score ✓

6.cosine similarity

• cosine similarity ✓
• cosine similarity matrix ✓

7. Statistics

• covariance
• covariance matrix
• correlation