Scikit Learn Tutorial : Linear Regression

What is Scikit-Learn (Sklearn) ?

Scikit-learn is the most useful and robust library for machine learning in Python. The library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction. Sklearn is used to build machine learning models. It should not be used for reading the data, manipulating and summarizing it. This library is built upon NumPy, SciPy and Matplotlib.

 

The functionality that scikit-learn provides include:

• Regression
• Classification
• Clustering
• Model selection
• Preprocessing

As you move through Ruadroid’s Machine Learning content, you will become familiar with many of these terms. You will also see scikit-learn modules being used. For example:

sklearn.linear_model.LogisticRegression()


is a Logistic Regression model inside the linear_model module of sklearn.

The power of scikit-learn will greatly aid your creation of robust Machine Learning programs.

Linear Regression

Linear regression is a statistical approach for modelling relationship between a dependent variable with a given set of independent variables. Linear regression is probably one of the most important and widely used regression techniques. It’s among the simplest regression methods. One of its main advantages is the ease of interpreting results.

 

Simple Linear Regression

Let's start with the most familiar linear regression, a straight-line fit to data. A straight-line fit is a model of the form

y=ax+b

where a is commonly known as the slope, and b is commonly known as the intercept.

 Python Code