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Data Cleaning in Machine Learning: Best Practices and Methods

Enterprises nowadays are increasingly utilizing machine learning for acquiring, storing, and analyzing data in order to facilitate better decision making and automate business tasks.

Data in machine learning is considered as the new oil, and different methods are utilized to collect, store and analyze the ML data. However, this data needs to be refined before it can be used further. One of the biggest challenges when it comes to utilizing Machine Learning data is Data Cleaning.

Although data cleaning may not be mentioned too often, it is very critical for the success of Machine Learning applications. The algorithms that you may use can be powerful, but without the relevant or right data training, your system may fail to yield ideal results.

The main aim of Data Cleaning is to identify and remove errors & duplicate data, in order to create a reliable dataset. This improves the quality of the training data for analytics and enables accurate decision-making.

Needless to say, data cleansing is a time-consuming process and most data scientists spend an enormous amount of time in enhancing the quality of the data. However, there are various methods to identify and classify data for data cleansing.

Methods for Identifying and Classifying Data Errors

There are mainly two distinct techniques, namely Qualitative and Quantitative techniques to classify data errors. Qualitative techniques involve rules, constraints, and patterns to identify errors.

On the other hand, Quantitative techniques employ statistical techniques to identify errors in the trained data. Once the errors are identified using these techniques, they can be rectified by making changes in the script or by human intervention, or with a combination of both.

Data Cleaning consists of two basic stages, first is error identification and second is error solving. For any data cleaning activity, the first step is to identify the anomalies.

No matter which technique you employ to analyze errors, your technique should involve three questions that should be addressed:

  • What types of errors to identify?
  • How to identify these errors?
  • Where to identify these errors?

Answering these questions will help you clean the data and improve the quality of your machine learning data. Apart from this, there are certain best practices that can be used for data error identification and data cleaning.

Let’s take a look at things to consider when it comes to data cleaning.

Best Practices of Data Cleaning

Setting up a Quality Plan

For any activity, a proper plan is very much necessary. Before you can go ahead with data cleaning, you need to define your expectations from the function.

You need to define clear KPIs along with identifying areas where the data errors are more likely to occur and at the same time identifying the reasons for errors in the data. A solid plan will help you get started with your data cleaning process.

Fill-out missing values

One of the first steps of fixing errors in your dataset is to find incomplete values and fill them out. Most of the data that you may have can be categorized.

In such cases, it is best to fill out your missing values based on different categories or create entirely new categories to include the missing values.

If your data are numerical, you can use mean and median to rectify the errors. You can also take an average based on different criteria, — namely age, geographical location, etc., among others.

Removing rows with missing values

One of the simplest things to do in data cleansing is to remove or delete rows with missing values. This may not be the ideal step in case of a huge amount of errors in your training data.

If the missing values are considerably less, then removing or deleting missing values can be the right approach. You will have to be very sure that the data you are deleting does not include information that is present in the other rows of the training data.

Fixing errors in the structure

Ensure there are no typographical errors and inconsistencies in the upper or lower case.

Go through your data set, identify such errors, and solve them to make sure that your training set is completely error-free. This will help you to yield better results from your machine learning functions. Also, remove duplicate categorization from your data list and streamline your data.

Reducing data for proper data handling

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It is good to reduce the data you are handling. A downsized dataset can help you generate results that are more accurate. There are different ways of reducing data in your dataset.

Whatever data records you have, sample them and choose the relevant subset from that data. This method of data handling is called Record Sampling. Apart from this method, you can also use Attribute Sampling. When it comes to the attribute sampling, select a subset of the most important attributes from the dataset.

Conclusion

Data Cleaning is a critical process for the success of any machine learning function. For most machine learning projects, about 80 percent of the effort is spent on data cleaning. We have discussed some of the few points, but there are various other methods of refining your dataset and making your ML dataset error-proof.

eInfochips provides machine learning services to help enterprises develop custom solutions for face detection, vehicle detection, driver behavior detection, anomaly detection, and chatbots, running on machine learning algorithms. To know more, get in touch with us.

Picture of Smishad Thomas

Smishad Thomas

Smishad Thomas is the Customer Experience Manager at eInfochips. He has over 10 years of experience into customer service and marketing. Smishad has completed his Masters in English Literature along with a degree in Corporate Communication.

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