Data Cleaning Steps Explained

While analyzing a dataset, what happens if you directly apply the analytical tools to raw data collected from different sources? The analytical tools give garbage results. Similarly, you cannot use raw data in machine learning applications. You first need to perform data preprocessing to convert the raw data into a useful format. Data preprocessing includes various steps like data cleaning, data transformation, and data reduction. In this article, we will discuss the requirements and benefits of data cleaning along with the steps involved in data cleaning.

What is Data Cleaning?

Data cleaning is the process of removing impurities and inaccuracies from the data. When we get a raw dataset, it contains outliers, mixed data values, mixed data types, and missing attributes. This data isn’t suitable for analysis. 

To make the data suitable for analysis, we use different data cleaning techniques. We correct and restructure the data to make it uniform and suitable for analysis by using different data cleaning techniques. 

Importance of Data Cleaning

While building machine learning models and analytical tools, you require a better dataset instead of powerful algorithms. If you have a dataset with errors and inaccuracies, you cannot build an accurate machine learning model by even running powerful algorithms on a high-performance computer.

• When you collect data from different sources, the format of data might be different for each source. In such a case, you require data cleaning so that you can reformat and restructure the entire dataset uniformly. 
• Collecting data from multiple sources also leads to increased chances of errors like mismatched data types and mixed data values. By using data cleaning techniques, you need to remove the errors for the data to be useful. 
• Having a uniform dataset will help you apply different machine learning techniques to the dataset without much difficulty. This will help you analyze the dataset and interpret results very efficiently. 
• Having fewer impurities in the dataset also helps you build prediction models with high accuracy. This can have a huge impact on the business. Having an accurate prediction can help businesses earn millions of dollars in profits. In turn, you, the data scientist will also get paid a huge sum.

Data Cleaning Steps

In the data cleaning process, you need to follow certain steps to obtain useful data from the raw data. Let us discuss the data cleaning steps one by one. 

Specify the Problem Statement

While starting a data analysis or machine learning project, you should know what metrics you want from the analysis. You should write down the questions that need to be answered using data analysis. This will help you decide what attributes of the data are important for your task.

Filter out the Useful Data

After specifying the problem statement and deciding on important attributes of the data, you need to remove the data irrelevant for your analysis in the next step. There is no need to waste resources by using unnecessary and irrelevant data in your analysis.

Remove Redundancy in the Data

After deciding on useful attributes in the dataset, you need to remove redundant and duplicate data entries in the next data cleaning step. You can perform correlation analysis to find if there are highly correlated attributes. In case you find highly correlated attributes, you can choose to drop one or more attributes from the dataset.

Remove Structural Errors in the Data

After removing redundancy from the data, the next data cleaning step is to fix the structural errors in the data. You need to correct spelling, improper capitalization, and wrong data type. For instance, a given data set can contain the salary of people as strings instead of integers. In such a case, you need to convert the strings to integers before analyzing the data.

Handle Missing Data

After removing structural errors, you need to handle missing data. For this, you can use different data cleaning techniques. For example, you can choose to remove the entries with missing data if the entries are small in number compared to the size of the dataset. Otherwise, you can choose to fill the missing values based on a statistical measure.

You might also need to restructure your data to be able to neglect the effect of the missing values in the dataset.

Remove Outliers

Outliers have the ability to affect the statistical measures significantly. Therefore, you need to identify and remove the outliers from your dataset before data analysis.

Validate the Data

Data validation is the final data cleaning step in which we decide if the data is consistent, uniform, and of high quality. If we have enough data for analysis and the data is uniformly structured, you can use different data analysis tools to analyze your data. 

Conclusion

Data cleaning is the most annoying but most useful process in data analysis. Without properly formatted and structured data, you can do nothing. Therefore, you need to follow the data cleaning steps discussed in this article to prepare the data for further analysis. 

I hope you enjoyed reading this article. To know more about data analysis and machine learning techniques, you can read this article on machine learning tools available for data analysis. You might also like this article that discusses trending machine learning applications.