mahmoudmuawad / scikit-learn_Common_Practice

This Repo contains the Basic Usage of scikit-learn functions and classes

machine learning with scikit-learn

This Repo contains most of common uses of sci-kit learn

note : these notebooks can be used a self reference

Dependencies

scikit-learn requires:

- Python (>= 3.5)
- NumPy (>= 1.11.0)
- SciPy (>= 0.17.0)
- joblib (>= 0.11)

User installation

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip ::

pip install -U scikit-learn

or conda::

conda install scikit-learn

The documentation includes more detailed installation instructions <http://scikit-learn.org/stable/install.html>_.

Content

1 Preprocessing

2 Regression

• Linear Regression
• Regularization
• Random Forest Regression
• SVR
• Decision Tree Regression

3 Classification

• Logistic Regression
• Decision Tree
• SVM
• SVR
• KNN
• Naive Bayes
• Kernel SVM
• Random Forest

4 Clustring

• HC-Clustring
• Kmeans-Clustring