Mastering Data Cleaning: Techniques and Best Practices for Handling Missing Data
Have you ever tried to analyze a dataset only to find that the data is riddled with errors, inconsistencies, and inaccuracies? This is a common challenge faced by researchers, data analysts, and data scientists, and it highlights the importance of data cleaning.
In this article, we will begin by outlining the meaning of data cleaning and why it is fundamental for dependable and accurate data analysis. In addition, we will examine how to manage a common data obstacle, missing data. While readers without prior experience in statistical analysis may still find this article interesting, some knowledge of the subject matter will be helpful in understanding the content.
Data cleaning refers to identifying and processing any errors or corruption in a dataset to provide high-quality data for modeling and inference-making. It is a critical step in the data analysis process because it ensures that the results of the analysis are accurate and reliable. Data cleaning can be described from the following aspects.
Missing value treatment
Outlier Treatment
High-quality data plays an important role in building a good data model. The lack of some data in the dataset can introduce large model deviations due to incorrect analysis of the behavior and relationship of variables leading to incorrect prediction.
First, you must understand some of the causes of missing data in real-world applications as only when you know the cause can you handle the missing values correctly.
Causes of missing data
Data loss is inevitable, in fact, it is a common phenomenon. The possible causes are as follows
Human negligence and Machine fault
Nonexistent data
Intentional concealing of data
High requirements on real-time data etc.
Types of Missing Data
The term complementary variable describes a variable within a dataset with no missing values, while an uncomplimentary variable refers to a variable containing missing values. A clear understanding of these terms is important in deciding which type of missing data you have in your dataset.
The common types of missing data are :
Missing Completely at Random
When data is Missing Completely at Random, the likelihood of missing values occurring within any given variable is entirely random and unrelated to its characteristics. This type of missing data is commonly the result of human error or mechanical failure during data collection.
Missing at Random
In the case of Missing at Random, the occurrence of missing values is dependent on certain complementary variables.
For instance, the value of the variable “Family Income” is more likely to be missing when the variable “Occupation” is Student. In this case, occupation is a complementary variable.
Missing not at Random
When data is Missing not at Random, missing data is related to the value of the uncomplemented variable itself. This type of missing data often occurs in situations where individuals are reluctant or unwilling to disclose certain pieces of information.
One common example of missing not at random can often be found in survey data, where individuals with high salaries may be less likely to disclose their income.
Understanding the type of missing data in a dataset is essential for the accurate processing of missing values. Failure to consider the type of missing data, particularly in the case of Missing at Random, can have significant impacts on the distribution and outcomes of the dataset.
Methods of handling missing data
Missing value processing methods are categorized into the following
Deletion
Imputation
Do nothing
Deletion
Deleting missing values can be an effective method for handling missing data in a dataset, but it is typically best suited for large datasets with only a small percentage of missing values. You can directly delete the data if the value is missing completely at random. Usually in a dataset, if the missing values exceed 20% of the total values in a variable, the variable can be deleted. However in a real-world application, if the variable is of commercial importance, then deleting the variable may cause a loss of user information.
Imputation
According to statistical principles, imputing a missing value involves using the distribution of non-missing data objects in the variable. Data analysts use various imputation methods, with Mean, Mode, and Median imputation being the three most commonly used methods.
Different data types use different methods.
In the case of Categorical data, it is preferable to use the mode for replacing missing values
For Interval and Ordinal data, it is preferred to use mode or mean to impute missing values.
Numerical data is conventionally mostly imputed with the mean of the distribution.
Besides mean, median, and mode imputation, we can also apply advanced techniques such as K Nearest Neighbors (KNN) and Regression models to predict missing values in a dataset. Although these methods offer higher precision, they are more complex and computationally expensive.
Do nothing
Removing rows of data that contain missing values decreases the amount of available data for analysis. Similarly, imputing missing data modifies the original data distribution to some degree and may introduce additional noise nodes.
Therefore, If a model cannot handle missing values, it is necessary to clean and process the data before feeding it into the model. In cases where the model can handle missing values, such as with decision tree models, it is acceptable to let the model handle the missing data.
Summary
In this article, we have discussed the importance of data cleaning in data analysis and focus on one of the most common challenges faced in data cleaning - handling missing data. The article also delves into the various methods of handling missing data.