The art of data exploration is not easy and has no shortcuts to it. Times may arise when it is difficult to improve a model’s accuracy, and knowledge of various data exploration techniques will be a saving grace. In the field of machine learning, the area of data exploration has become quite an area of interest. It might be still evolving; however, by employing machine learning and understanding common patterns, it is indeed possible to identify relationships, patterns or algorithms in a given set of data.
Employment of machine learning is crucial as it mitigates manual labour and time involved in data exploration and reduces the errors that may occur in the employment of manual inspection, trial and or error, or other traditional exploration techniques.
Data exploration is a methodology that is very much like initial data analysis. A data analyst utilizes visual exploration to comprehend the contents of a dataset and its attributes, instead of using data management systems. These attributes could include the size, culmination, accuracy, potential connection between different data components or data files/ tables.
Both manual (drill down or filtering of data to understand similar patterns in data) and automated (data profiling or visualization) methods may be used for data exploration.
Essentially, Data exploration is pruning of data to remove unusable parts and identify potential relationships between different types of data.
Before delving into the steps involved in data exploration, it is essential to understand that the quality of output is directly proportional to the quality of input. In data exploration, a significant amount of project time is spent on preparation and cleaning of data.
Given below are certain steps that are to be followed while prepping data to build a predictive model-
In the instance that the variables are continuous, the central tendency, as well as the spread of the variable, must be understood. Central tendency is measured using mean, median, mode, min, max etc., and measure of dispersion is through the range, quartile, IQR, Variance, standard deviation, skewness and kurtosis, etc. Visualization methods of Histogram and Box Plot are usually adopted.
In the case of Categorical variables, a frequency table that reads the percentage of values using count and count% metrics must be used to understand the distribution of each category.
Categorical and categorical: To identify the relationship between two categorical variables two-way table, stacked column chart and chi-square test methods may be used.
Categorical and continuous: In understanding the relationship between categorical and continuous variables, box plots for each categorical variable level are to be drawn.
Continuous and continuous: Here, the pattern of scatter plot must be looked into while conducting an analysis between two continuous variables as a scatter plot defines whether the relationship can be linear or nonlinear.
There are various approaches/ techniques that may be adopted in data exploration. Some of them are:
Human beings measure visual data better than mathematical data; hence it is quite challenging for data scientists and data analysts to allocate significance to thousands of rows and columns of data and to impart said information without any visual parts.
Data visualization in data exploration uses familiar visual signals, for example, shapes, dimensions, colours, lines and angles with the goal that data analysts can successfully imagine and characterize the metadata and post that, perform data exploration. Playing out the underlying advancement of data exploration, data analysts are empowered to comprehend and distinguish variations and possible relationships that may have otherwise gone undetected.
At the end of the day, data exploration might take some efforts. It might involve large sets of data being identified and sorted using various techniques. These techniques may require time and efforts to understand and adopt. However, it differentiates a good model from a bad one.
In a world where data is often accumulated in large, unstructured volumes from sources all across the world, it is essential to understand and have a comprehensive view of the data. Such a comprehensive view Is necessary to be able to use the data collected for further analysis.
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