This learning project is a demonstration of my understanding of and ability to construct machine learning algorithms from scratch.

I implemented the following models:

• Classification:
  • KNN
  • Desicion tree
  • Random forest
  • Support vector machine
  • Boosting machines:
    • Adaptive boosting
    • Gradient boosting
• Clustering:
  • K-means

Each model is tested against its corresponding counterpart from the scikit-learn module with graphical demonstration of the decision rule of each model.

Please continue to the jupyter notebook file to see my models in action.