Machine learning is all about training machines by feeding data to algorithms. But this becomes a challenging task as the data needs to be error-free before feeding to the machines. Therefore, cleaning the erroneous and irrelevant data is crucial for achieving efficiency and accuracy in results. While utilizing the ML data, the most tedious and time-consuming task is the cleaning of data. Inaccurate and irrelevant data can affect the quality of the training data for analytics.

Data analysts and scientists have to spend an enormous amount of time classifying erroneous data. They do this through qualitative and quantitative techniques. The qualitative method includes patterns, constraints, and rules, while the quantitative method uses statistics to identify errors. Usually, data cleaning involves two steps, first identifying the error and, secondly, solving it.

When it comes to data cleaning, there are certain practices that most data scientists use. So, consider using the following points while performing data cleaning for machine learning.

Best Practices Of Data Cleaning In Machine Learning

Fill-Out Incomplete Values

This is the initial step when cleaning data. You have to identify the missing values in the dataset and fill them out. You can categorize most of your data, and it is best that you complete the missing values based on the categories. You can also create an entirely new category to include the missing values. Numerical data can be rectified using mean and median. The other way is taking average based on different criteria such as age, geographical location, etc.

Deleting Rows Having Missing Values

Removing or deleting the rows with missing values is another way of cleaning the datasets. This is considered a good approach if the missing values are less. However, while doing so, you’ll have to ensure that the rows you are deleting do not have any information that already exists in rows of your Machine Learning training data.

Fixing Structural Errors

Structural errors can include typographical errors and inconsistencies in the lower or upper case. In order to make your dataset error-free, you will have to go through the datasets, identify the errors, and then correct them. Besides, you’ll need to streamline the data by removing duplicate categorization. Doing this will help you get better results.  

Downsize Dataset

A dataset that contains only relevant information will give more accurate results. Reducing the datasets while handling them makes more sense here. You can do this through different methods, such as record sampling and attribute sampling. Record sampling is when you sample the available datasets and then take out the relevant subset. In attribute sampling, the subset of the most important attribute is selected.

A Machine Learning bootcamp can help you learn all the aspects of machine learning.  And, data cleaning is a critical step in the machine learning process. In any ML project, most of the time goes into cleaning data. There are numerous data cleaning tools that can aid you in keeping your data clean and consistent. You must know how to use the best tools for data cleaning and make your data error-free.