QuickDataScience | Quick & Easy Data Science

We have discussed Exploratory Data Analysis, known as EDA & have also seen few powerful libraries that we can use extensively for EDA. EDA is a key step in Machine Learning, as it provides the start point for our Machine Learning task. But, there are a lot of issues related to traditional Data Analysis techniques. There are too many new libraries coming up in the market to rectify these issues. One such API is AutoViz, which provides Quick and Easy visualization with some insights about the data.

We had a look over the basics of EDA in our previous article  EDA - Exploratory Data Analysis . So now let's move ahead and look at how we can automate the process and the various APIs used for the same. We will be focusing on the 7 major libraries that can be used for the same. These are our personal favourites & we prefer to use them most of the time.  We will look into the libraries' & will cover the install, load, and analyse parts for each separately.  D-tale Pandas - Profiling Lux Sweetviz Autoviz ExploriPy Dora

EDA EDA - Exploratory Data Analysis is the technique of defining, analyzing and investigate the dataset. This technique is used by most data scientists, engineers and everyone who is related to or wants to work and analyze the data. Saying that, it includes the whole majority of us as at any point of time we are dealing with data and we un-knowingly do an initial analysis about which in technical terms is referred to as   "Exploratory Data Analysis". Here is a formal definition of the EDA:-  In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods.  Still confused about how every one of using this process..!! Let me explain it with a simple example... Suppose you and your group plan for lunch in a restaurant... as soon as we hear "lunch" and "restaurant" our mind starts creating a list of all the known places, next as someon...

What is Data Visualization?  Data Visualization as the name suggests is creating nice, beautiful and informative visuals from our data, which helps get more insights from the data. It helps us and the third person who sees our analysis or report in reading it better. Creating a good visualization helps us in understanding the data better and helps in our machine learning journey.  The data visualization process uses various graphs, graphics, plots for explaining the data and getting insights. DV is important to simplify complex data by making it more  accessible, understandable, and usable to its end users. If you want to know in more detail about data visualization you can Read IT Here .