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Regression:\n- linear model \n    - Linear Regression ✓\n    - Ridge Regression ✓\n    - Lasso Regression ✓\n    - ElasticNet Regression ✓\n\n- KNN Regression (with 2 distance metric) ✓\n    - uniform KNN ✓\n    - distance Weight KNN ✓\n\n- Tree Regression\n    - Decision Tree Regression\n    - Random Forest Regression\n- Support Vector Regression (SVR)\n\n### 2. Clustering:\n- K-Means\n- Hierarchical Clustering\n- DBSCAN\n- Mean Shift Clustering\n\n### 3. Classification:\n\n- KNN (with 2 distance metric) ✓\n    - uniform KNN ✓\n    - distance Weight KNN ✓\n- Tree \n    - Decision Tree ✓\n    - Random Forest ✓\n- Ensemble\n    - AdaBost ✓\n- Regression : \n    - Logistic Regression ✓\n    - Ridge Regression \n    - Lasso Regression \n    - ElasticNet Regression \n- Bayesian \n    - Gaussian ✓\n    - Multinomial ✓\n- Minimum Distance Classifier ✓\n- Support Vector Machine (SVM)\n- Perceptron ✓\n\n\n\n### 3. Dimensionality Reduction:\n- Principal Component Analysis (PCA)\n- Linear Discriminant Analysis (LDA)\n\n\n### 4. Normalization data\n- sandardScaler ✓\n- MinMaxScaler ✓\n- MaxAbsScaler ✓\n- RobustScaler ✓\n- Normalizer ✓\n\n### 5. metrics \n\n- regression : \n    - MSE ✓\n    - MAE ✓\n    - R2-Score ✓\n\n- Classification:\n    - accourcy ✓\n    - recall ✓\n    - prescion ✓\n    - f1-score ✓\n\n### 6.cosine similarity \n- cosine similarity ✓\n- cosine similarity matrix ✓\n\n\n### 7. Statistics\n- covariance\n- covariance matrix\n- correlation\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fparvvaresh%2Fml-algorithms","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fparvvaresh%2Fml-algorithms","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fparvvaresh%2Fml-algorithms/lists"}