Analysis and Visualization of Farmer Call Center Data

Abstract

In the last chapter, the focus was on cleaning and pre-processing of the Kisan Call Center (KCC) query data. Just to reiterate that Kisan Call Center is the Indian name for farmer call center. The agenda of this chapter is to perform exploratory data analysis (EDA) or descriptive data analysis. The objective of exploratory analysis is to form meaningful conclusions from the open source KCC query dataset so as to help the farmers and the government. To begin with, let us define—what is data analysis and how it is being used? Data analysis is a process of inspecting, cleansing, transforming data and preparing a model with the goal of discovering useful information, notifying conclusions and supporting decision-making. While there are various different forms of data analysis, in this chapter the concentration is on descriptive statistics, which is also known as exploratory data analysis and confirmative data analysis. This begs the question as to what is EDA? Putting it in simple words, exploratory data analysis is an approach to understand the data distribution, to discover any specific pattern or trend, to summarize the main characteristics and to explain the results using visual methods. EDA is used to test hypotheses and gather proof to prove the same. Usually, the data analysis is done, when data is too huge to form conclusions humanely, hence programming tools are used to analyse it. However, that is not enough; there is a need for visualization tool to comprehend the conclusions in a better way.