jailukanna / bindu_gadagottu_ml

Machine learning

Bindu_Gadagottu_ML

Machine learning

Regression

Regression analysis is one of the most important fields in statistics and machine learning. There are many regression methods available. Linear regression is one of them.

Linear Regression

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.

Python Packages for Linear Regression

• The package NumPy is a fundamental Python scientific package that allows many high-performance operations on single- and multi-dimensional arrays
• The package scikit-learn is a widely used Python library for machine learning.It provides the means for preprocessing data, reducing dimensionality, implementing regression, classification, clustering, and more.scikit-learn is also open source.

Linear Regression With scikit-learn

There are five basic steps when you’re implementing linear regression:

1. Import the packages and classes you need.
2. Provide data to work with and eventually do appropriate transformations.
3. Create a regression model and fit it with existing data.
4. Check the results of model fitting to know whether the model is satisfactory.
5. Apply the model for predictions.