Get started with Python and Scikit-learn for regression models
INTRODUCTION:
In this course, you will use Scikit-learn and other tools to build machine learning models to perform what we call 'traditional machine learning' tasks. We have deliberately avoided neural networks and deep learning, as they are better covered in our forthcoming 'AI for Beginners' curriculum.
Scikit-learn makes it straightforward to build models and evaluate them for use. It is primarily focused on using numeric data and contains several ready-made datasets for use as learning tools. It also includes pre-built models for students to try. Let's explore the process of loading prepackaged data and using a built in estimator first ML model with Scikit-learn with some basic data.
CASE STUDY:
In this section, you will work with a small dataset about diabetes that is built into Scikit-learn for learning purposes. Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.
- There are many types of regression methods, and which one you pick depends on the answer you're looking for
- If you want to predict the probable height for a person of a given age, you'd use linear regression, as you're seeking a numeric value.
- If you're interested in discovering whether a type of food should be considered vegan or not, you're looking for a category assignment so you would use logistic regression.
NOTE1.DIABETES DATASET