Data rarely comes in usable form. Data wrangling and exploratory data analysis are the difference between a good data science model and garbage in, garbage out.

Speeding up the exploratory data analysis process using flexible automation and consistent reporting allows analysts to deliver analyses quickly while ensuring precise, accurate results.

The Exploratory Data Analysis Problem The prudent scientist must interrogate the data with a laundry list of statistical questions to determine the data’s fit-for-use in AI and ML projects.

An exploratory data analysis has been performed on the dataset to explore the effects of different factors like holidays, fuel price, and temperature on Walmart’s weekly sales.