This article is the first in a series that helps working developers get up to speed on data science tools and techniques. We'll start with a brief introduction to the series, and explain everything we're going to cover. Developers and data scientists working on data analysis and machine learning (ML) projects spend the majority of their time finding, cleaning, and organizing datasets. We'll do this by using Python, Pandas, and Seaborn in a Jupyter notebook to clean up a sample retail store's messy customer database. This seven-part series will take the initial round of messy data, clean it, and develop a set of visualizations that highlight our work. Here's what the series will cover: Before we start cleaning our dataset, let's take a quick look at two of the tools we'll use: Pandas and Jupyter Notebooks.

Data scientists often use Anaconda Navigator [2], which houses popular and useful applications like JupyterLab, Jupyter Notebook, and RStudio. It is usually at these three applications where we tend to stop looking into this platform for other tools. As you navigate out of the home page or the home dashboard, you will see that there are the Environments, Learning, and Community sections. The latter two features are ones that we may miss, because they are not directly related to writing your own immediate code and working on your machine learning algorithm in the main notebook application. However, they are still important and may be something that you have not looked into yet.

Welcome to another article series! This time, we are discussing XGBoost (Extreme Gradient Boosting) -- The leading and the most preferred machine learning algorithm among data scientists in the 21st century. Most people say XGBoost is a money-making algorithm because it easily outperforms any other algorithms, gives the best possible scores and helps its users to claim luxury cash prizes from data science competitions. The topic we are discussing is broad and important so that we discuss it through a series of articles. It is like a journey, maybe a long journey for newcomers.

Step1: Make sure you have that Microsoft VC compiler, Anaconda navigator is properly installed in your system. I hope you have python and Microsoft VC then skip the below process and go to step 2. Otherwise, you can download those from the given links. After Microsoft VC compiler, you have to download Anaconda Navigator. To download anaconda navigator click here and then scroll down where you can see Anaconda Installers and click on (python 3.7) 64-bit graphical installer or 32-bit graphical installer depends on your system version then save. Depends on internet speed it will download in 5 to 10 minutes and then install.