It has been one year since COVID-19 took over the globe. The virus has affected every country in some way or another. In some cases, the infection has spread drastically in comparison to the remaining countries. It is time to get straight to the point. Let us visualize the total cases across the world and India with
The Covid-19 dataset has been sourced from GitHub. The data links are provided at the end of this article, or you can search for them on Google. …
A line graph is one of the most commonly utilized plots to visualize continuous and time-dependent data. This type of data helps in understanding a particular trend over a specific period of time. You must have definitely come across line charts in wide range of presentations and sales pitches.
Let’s begin by making a simple line chart on a sample sales dataset from Kaggle 😊. Before we get into hands-on coding, I prefer creating
np.arrays of the selective columns.
Matplotlib is no stranger to the world of Data Visualization. It is one of the most popular libraries used to visualize arrays of data in Python. In short,
matplotlibwill help you create and plot graphs and perform operations simultaneously for better and intuitive presentation.
Let us understand this plot by plot and graph by graph 😊.
Bar graphs are the common types of plots when you need to measure numerical data across categories. Here is a sample visualization followed by the code for the same. The sample data is composed of random sales numbers and metadata. …
Microsoft Excel is one of the most widely used tools for descriptive statistics and exploratory data analysis. In my opinion, it is always beneficial to clean your dataset in raw excel before uploading it to a Jupyter notebook. Long story short, if you tend your dataset in excel, you will spend fewer time data wrangling in Pandas.
I’ll get straight to the point. We will be working with a sample excel sheet that comprises fake commercial property insurance policy data. Commercial property insurance protects your business or enterprise’s tangible/non-tangible assets against loss or damage arising out of certain events/disasters.
Simple Linear Regression is one of the foundations of machine learning modelling. It is the very first algorithm that you learn in your journey towards becoming a data scientist. What is the one thing that makes simple linear regression special? Well, there are five!
There are five regression assumptions. You must know all of them and consider them before performing any type of regression analysis. To begin with, let us look at the old school textbook definition of linear regression.
‘A linear regression is a statistical method of determining a linear approximation of a causal relationship between two or more…