Scikit-learn cheat sheet: classification and regression methods

Machine learning has been integrated with our daily lives, for example, voice assistants in our phone, resume scanner in a company, etc,. Scikit-learn is a popular Python library for performing classification, clustering, and regression algorithms. Its an important part of some python data science libraries like matplotlib, NumPy ( for graphs and visualization) and SciPy ( for mathematics )

Python provides a lot of tools for implementing Classification and Regression. Scikit-learn is used to perform those. The basic steps of supervised learning include:

1. Load the required libraries.

2. Load the dataset

3. Splitting the dataset into training and test set

4. Train the model.

5. Evaluate the model.

LOADING THE LIBRARIES

LOADING THE DATASET

SPLITTING INTO TRAIN AND TEST SET

TRAINING THE MODEL

EVALUATING THE MODEL

So, we know the basic steps for classification and regression. Let's see about the methods for it, which can be used in ML systems that simplifies the ML programming

10 POPULAR CLASSIFICATION METHODS

10 POPULAR REGRESSION METHODS

WHAT TO LEARN NEXT?

As you continue the Scikit-learn journey, here are the next algorithms and topics to learn:

1. Support Vector Machine
2. Random Forest
3. Cross-validation techniques
4. grid_search
5. fit_transform
6. n_clusters
7. n_neighbors
8. sklearn.grid

Happy Learning!!