Understanding Data Pre-Processing in Statistical Analysis

Collected data is often incomplete, inconsistent and is likely to contain many errors. Data preprocessing is a proven method of resolving these issues.

In simple words, Data preprocessing is the process of collecting, selecting, and transforming data to analyze data. Basically, it means converting data into an understandable format.

It is also known as data cleaning or munging. It is the most time taking process of statistical analysis as it accounts for 80% of the time taken for analysis. Data preprocessing is very important in any statistical analysis, otherwise, it will directly impact the success rate of the project.

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Data preprocessing allows us to remove any unnecessary data with the use of various techniques, this allows the user to have a dataset that contains more valuable information.

These datasets are edited to correct the data corruption or human error. This is an important step to get accurate quantifiers like true positives, true negatives, False positives, and false negatives found in a Confusion matrix that are commonly used for a medical diagnosis.

Any unnecessary information is removed from the data which allows the analyst to have higher accuracy. Analysts use Python programming scripts accompanied by the pandas library which gives them the ability to import data from Comma-separated values as a data-frame.

Methods of Data Preprocessing:

Now let us discuss the methods of data preprocessing. Data Preprocessing is done using the two methods :

  1. Missing Value Treatment
  2. Outlier Correction

Let’s understand Missing Value Treatment First :

i. Missing value treatment

There could be several reasons behind the missing values like – human error, data incorrectly received, output error, and so on. To fill the missing values, we use imputation techniques like mean, median, and mode.

Let us now look at the second method of data pre-processing

ii. Outlier correction/ Treatment-

Before we understand the procedure, let’s understand what outliers are:

An outlier is the data point that lies outside the range of the remaining data points in a dataset.

Eg: The sale of electronic goods during holidays like Black Friday sale, new year, and so on, could result in outliers in data of sales for that entire year. This is because the electronic goods sales are way higher in these days of the year when compared to normal days.

To detect outliers we have a simple technique called the box plot method. The values that fall outside the upper and lower limit are called outliers.

Conclusion:

Data Preprocessing is a very crucial stage in the whole process of statistical analysis, and you can not afford to have any sort of mistake at this point. If you did any error at this point then you won’t get the desired result from the analysis. As said, this step itself takes almost 80% of the time of the statistical analysis process

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