Exploratory production data analysis using Python

“Data is the new oil”, the significance of oil is derived from the fact that oil companies have been ruling the globe for decades. Major global tension has its base in the form of oil. Contemplating data requirements, data collection, data processing, and data cleaning are the stages that precede EDA. An appropriate decision needs to be made from the data collected about different fields which are primary stored in electronic databases. Data mining is the process that gives an insight into the raw data and EDA forms the first stage of Data mining.

 Exploratory Data Analysis is a method of evaluating or comprehending data in order to derive insights or key characteristics. EDA is a critical component of any data science or machine learning process. You must explore the data, understand the relationships between variables, and the underlying structure of the data in order to build a reliable and valuable output based on it. The EDA stages will be carried out in this tutorial using the Python programming language.

 Stages of EDA

1. Definition of the problem – To define a problem, it is important to define the primary objective of the analysis alongside defining main deliverables, roles, and responsibilities, the present state of the data, setting a timeline, and analyzing the cost to benefit ratio.
2.  Preparation of data – In this stage, characteristics of data are being comprehended, the dataset is cleaned, and irrelevant data are deleted.
3. Analyzing the data – In this stage, the data are being summarized, hidden correlations are being derived, predictive models are being developed and evaluated, and summary tables are being generated.
4. Results representation – Finally, the dataset is being presented to the target audience in the form of graphs, and summary tables.