Data Classification, Clustering, and Regression is part 5 of this series on Data Analysis. The focus of this article is to use existing data to predict the values of new data. 

If you missed the other posts in this series, read them here: 

• • Part 1: An Introduction to Data Analytics  
  • Part 2: What is Data
  • Part 3: Basic Data Visualization Techniques
  • Part 4: Clean Raw Data

This is part 4 of this series on Data Analysis. The focus of this article is how to take raw data and make it more suitable for analysis.

If you missed the other posts in this series, read them here: 

• An Introduction to Data Analytics  
• What is Data
• Basic Data Visualization Techniques

Introduction

Welcome back! Earlier, we had covered the basics of getting started with machine learning and Python. (Here is that blog if you missed that: DataScience & Machine Learning: Where to start with Python) The current blog will take the next step and introduce some ML (Machine Learning) concepts and algorithms.

Since we are going to use Python, we will stick to the sklearn Python library as our choice.

Introduction

In this blog, we will talk about how you can use Python for practical applications of Data Science and Machine Learning. If you are looking for basic machine learning concepts, I would suggest reading this previous introductory blog and then coming back here.