practical-ml

ML algorithms implemented from scratch and with scikit-learn

Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
1. Regression.ipynb		1. Regression.ipynb
10. K-Means clustering.ipynb		10. K-Means clustering.ipynb
11. Custom K-Means.ipynb		11. Custom K-Means.ipynb
12. Titanic dataset prediction with K-Means.ipynb		12. Titanic dataset prediction with K-Means.ipynb
13. Mean Shift from scratch.ipynb		13. Mean Shift from scratch.ipynb
2. Regression - predicting and forecasting.ipynb		2. Regression - predicting and forecasting.ipynb
3. Regression - pickling and scaling.ipynb		3. Regression - pickling and scaling.ipynb
4. Regression from scratch.ipynb		4. Regression from scratch.ipynb
5. Regression - testing assumptions.ipynb		5. Regression - testing assumptions.ipynb
6. Classification - KNN.ipynb		6. Classification - KNN.ipynb
7. KNN - from scratch.ipynb		7. KNN - from scratch.ipynb
8. KNN - Comparing from scratch solution with scikit-learn method.ipynb		8. KNN - Comparing from scratch solution with scikit-learn method.ipynb
README.md		README.md
breast-cancer-wisconsin.data		breast-cancer-wisconsin.data
linearregression.pickle		linearregression.pickle

Provide feedback