Ways to Handle Missing Value

Introduction

Missing value are a common occurrence in datasets and can stem from various reasons such as human error, data corruption, or intentional omission. Dealing with missing values is crucial as they can introduce bias in our analysis. It is important to carefully consider the nature of the missing data and choose the appropriate handling technique accordingly. There are different ways to handle missing data effectively, including deletion, imputation, and advanced techniques such as data mining algorithms or machine learning models. By handling missing values appropriately, we can ensure that our data analysis and models are more accurate and reliable, leading to more meaningful insights and results.

Missing Values

Missing values are a common occurrence in datasets and can stem from various reasons such as human error, data corruption, or intentional omission. Dealing with missing values is crucial as they can introduce bias in our analysis. For example:

The table above has shown 4 common missing value representations:

• N/A
• NULL
• NaN
• Empty Space

Keep in mind that the actual value might vary. For example, if a negative value occurs in the table above will be treated as a missing value (because it is impossible to be negative).

Types of Missing Value

In data science, there are three main types of missing values:

1. Missing Completely at Random (MCAR): This type of missing data occurs when the missing values have no relationship or pattern with the other variables in the dataset. In other words, the probability of data being missing is the same for all observations, regardless of any other information. For instance, suppose you are conducting a survey about people’s income, and some respondents failed to answer the income question. If the missingness of income is unrelated to any factors, such as age, gender, or income level itself, and occurs randomly (such as participants skipping questions), the missingness is MCAR.
2. Missing at Random (MAR): MAR occurs when the missing values have a systematic relationship with other variables in the dataset, but not with the missing values themselves. In other words, the probability of a value being missing depends on the observed data. Continuing with the income survey example, if the missingness of income is related to another variable, such as education level, but not to the actual income itself, it is MAR. For instance, respondents with higher education levels may be more likely to leave the income question unanswered. In this case, the missingness depends on the observed education level, but not directly on the missing income values.
3. Missing Not at Random (MNAR): This type of missing data occurs when the missing values have a relationship with the missing data itself. The missing values are not random and can introduce bias into the analysis. Sticking with the income survey, if the missingness of income is related to the income level itself, such as high-income individuals being less likely to disclose their exact income, it is MNAR. In this scenario, the missingness is directly linked to the values of the missing data (income) and is not related to any observed variables.

Finally, here are the methods to handle missing data:

1. Delete Missing Value

Deletion is the most simplest and intuitive way to handle them but not always correct. If the missingness is minimal and doesn’t impact the larger dataset, deletion might be a fast and good option. For example, if the missing value only occurs in 10 rows out of 100000, deleting them should be ok. Or deleting columns such as the height or weight of a person should be fine when predicting the churn rate of bank customers.

However, if the rows or columns are important, deleting missing values results in the removal of entire rows or columns from the dataset. This can lead to a significant loss of information.

Here is a simple example in Python:

import pandas as pd

# Example DataFrame with missing values
data = {'Name': ['John', 'Alice', 'Bob', 'Jane'],
        'Age': [25, None, 35, 30],
        'Gender': ['M', 'F', None, 'F']}
df = pd.DataFrame(data)

# Delete rows with missing values
df.dropna(inplace=True)

# Print the resulting DataFrame
print(df)

This code will remove any row from the DataFrame (df) that contains at least one missing value. The dropna() function with the inplace=True parameter ensures that the changes are made directly to the DataFrame.

Output:

  Name   Age Gender
0  John  25.0      M

Please note that deletion is a quick solution, but it may lead to a loss of data if the missing values contain important information. It’s important to carefully consider the impact of data removal on your analysis before applying this approach.

2. Impute Missing Value

Imputation is another common method for handling missing data. It involves estimating the missing values based on the available information in the dataset. There are several techniques for imputation, such as mean imputation, median imputation, mode imputation, or regression imputation.

• Mean Imputation: This method replaces the missing values with the mean of the non-missing values of that variable. It is a simple and straightforward approach that works well when the missing data is missing completely at random or missing at random.
• Median Imputation: Similar to mean imputation, this method replaces the missing values with the median of the non-missing values. It is a robust approach that can handle outliers better than mean imputation.
• Mode Imputation: This method replaces the missing values with the mode (most frequent value) of the non-missing values. It is commonly used for imputing categorical variables.
• Regression Imputation: This method uses regression models to predict the missing values based on the values of other variables in the dataset. It is a more advanced technique that can capture relationships between variables and produce accurate imputations.

Here’s an example in Python using the mean, median, and mode imputation techniques:

import pandas as pd

# Example DataFrame with missing data
data = {'Name': ['John', 'Alice', 'Bob', 'Jane'],
        'Age': [25, None, 35, 30],
        'Gender': ['M', 'F', None, 'F']}
df = pd.DataFrame(data)

# Impute missing data with mean
# change mean() to median(), if impute with median value
df['Age'].fillna(df['Age'].mean(), inplace=True)

# Impute categorical missing data with mode (although Bob is probably a male)
df['Gender'].fillna(df['Gender'].mode()[0], inplace=True)

# Print the resulting DataFrame
print(df)

Output:

   Name   Age Gender
0  John  25.0      M
1 Alice  30.0      F
2   Bob  35.0   None
3  Jane  30.0      F

Remember that imputation methods assume that the missing data follows a certain pattern and that the imputed values are representative of the missing values. However, imputation may introduce bias or distort the distribution of the variable, so it is important to exercise caution and consider the limitations of each imputation method.

3. Advanced Techniques

In addition to deletion and imputation, there are more advanced techniques for handling missing data, such as data mining algorithms or machine learning models. These methods involve using the available data to create models that can predict the missing values based on the relationships in the dataset. These techniques can often provide more accurate imputations but may require more computational resources and expertise in model building.

It’s important to note that there is no one-size-fits-all approach to handling missing data. The choice of method depends on the characteristics of the data, the amount of missingness, and the goals of the analysis. It’s recommended to carefully evaluate the advantages and disadvantages of each technique and select the one that best suits the specific needs of the analysis. Remember, the goal is to minimize any potential bias and maintain the integrity of the dataset.

Summary

In summary, handling missing values is a crucial aspect of data analysis. Whether these values are due to errors, equipment malfunctions, or simply unavailable information, it is important to address them appropriately to ensure accurate and reliable results.

There are several methods available to handle missing values, each with its own advantages and considerations. Deletion can be a straightforward approach, but it should be used cautiously to avoid biased analysis. Imputation techniques such as mean, mode, median, regression, and multiple imputation offer alternative ways based on available data and relationships between variables.

Choosing the most appropriate method depends on various factors, including the extent of missing data, the specific goals of the analysis, and the assumptions made by each imputation technique. Additionally, domain knowledge and expert insights can provide valuable context when filling in missing data.

By implementing these strategies, researchers and analysts can ensure the integrity and reliability of their data analysis. Proper handling of missing data is essential for producing accurate insights and drawing meaningful conclusions from datasets.