Machine-learning

• Machine learning is the study of computer algorithms that can improve automatically through experience and by the use of data.
• As a beginner, we can understand like given flow.
• Data -> Computer Algorithms -> Prediction simple right 😀!

Steps to follow up with me :

• Open this repository in local or VS Code web [By clicking dot( . )] 😉.
• Open Jupyter notebook files under notebook folder.
• Try to run and understand each cell of files in the order given.
• Cheers Let rock it 🎉🥳.

Data Preprocessing

1 . Data Preprocessing :

• Please check out to 1_data_preprocessing branch.
• Run 1 . Data Preprocessing.ipynb notebook file.

Regression

2 . Simple Linear Regression :

• Please check out to 2_simple_linear_regression branch.
• Run 2 . Simple Linear Regression.ipynb notebook file.

3 . Multiple Linear Regression :

• Please check out to 3_multiple_linear_regression branch.
• Run 3 . Multiple Linear Regression.ipynb notebook file.

4 . Polynomial Linear Regression :

• Please check out to 4_polynomial_linear_regression branch.
• Run 4 . Polynomial Linear Regression.ipynb notebook file.

5 . (SVR) Support Vector Regression :

• Please check out to 5_svr_support_vector_regression branch.
• Run 5 . (SVR) Support Vector Regression.ipynb notebook file.

6 . Decision Tree Regression :

• Please check out to 6_decision_tree_regression branch.
• Run 6 . Decision Tree Regression.ipynb notebook file.

7 . Random Forest Regression :

• Please check out to 7_random_forest_regression branch.
• Run 7 . Random Forest Regression.ipynb notebook file.

8 . Regression Model Selection :

• Please check out to 8_regression_model_selection branch.
• Run 8 . Regression Model Selection.ipynb notebook file.

9 . Regression Extras :

• Please check out to 9_regression_extras branch.
• Run 9 . Regression Extras.ipynb notebook file.

Classification

10 . Logistic Regression :

• Please check out to 10_logistic_regression branch.
• Run 10 . Logistic Regression.ipynb notebook file.

11 . K Nearest Neighbor Classification :

• Please check out to 11_k_nearest_neighbor_classification branch.
• Run 11 . K Nearest Neighbor.ipynb notebook file.

12 . (SVC) Support Vector Classification :

• Please check out to 12_svc_support_vector_classification branch.
• Run 12 . (SVC) Support Vector Classification.ipynb notebook file.

13 . Kernel (SVC) Support Vector Classification :

• Please check out to 13_kernel_support_vector_machine branch.
• Run 13 . Kernel Support Vector Machine.ipynb notebook file.

14 . Naive Bayes Classification :

• Please check out to 14_naive_bayes_classification branch.
• Run 14 . Naive Bayes Classification.ipynb notebook file.

15 . Decision Tree Classification :

• Please check out to 15_decision_tree_classification branch.
• Run 15 . Decision Tree Classification.ipynb notebook file.

16 . Random Forest Classification :

• Please check out to 16_random_forest_classification branch.
• Run 16 . Random Forest Classification.ipynb notebook file.

17 . Classification Model Selection :

• Please check out to 17_classification_model_selection branch.
• Run 17 . Classification Model Selection.ipynb notebook file.

18 . Classification Extras :

• Please check out to 18_classification_extras branch.
• Run 18 . Classification Extras.ipynb notebook file.

Clustering

19 . K Means Clustering :

• Please check out to 19_k_means_clustering branch.
• Run 19 . K Means Clustering.ipynb notebook file.

20 . Hierarchical Clustering :

• Please check out to 20_hierarchical_clustering branch.
• Run 20 . Hierarchical Clustering.ipynb notebook file.

Association Rule Learning

21 . Apriori ARL :

• Please check out to 21_apriori_arl branch.
• Run 21 . Apriori ARL.ipynb notebook file.

22 . Eclat ARL :

• Please check out to 22_eclat_arl branch.
• Run 22 . Eclat ARL.ipynb notebook file.