Getting started with Predictive Analysis

Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction via a consistence interface in Python.

Classification

In Classification, the output variable must be a discrete value. The task of the classification algorithm is to map the input value(x) with the discrete output variable(y).

Regression

In Regression, the output variable must be of continuous nature or real value. The task of the regression algorithm is to map the input value (x) with the continuous output variable(y).

Clustering

A way of grouping the data points into different clusters, consisting of similar data points. The objects with the possible similarities remain in a group that has less or no similarities with another group.

Dimensionality reduction

It is a way of converting the higher dimensions dataset into lesser dimensions dataset ensuring that it provides similar information.

Model selection

Model selection is the process of selecting one final machine learning model from among a collection of candidate machine learning models for a training dataset.

Preprocessing

Data preprocessing is a process of preparing the raw data and making it suitable for a machine learning model. It is the first and crucial step while creating a machine learning model.