How a Data Analytics Training Institute in Patna Teaches Missing Data Handling

You may have analysed your dataset for hours only to find that information is missing. This can cause problems with calculations due to missing cells, hiding valuable information, and giving unreliable results. This is a common issue when performing data analytics and can occur due to incomplete data being entered, incorrect data being collected, or data simply not being available.

It is important to know how to spot and deal with these missing values. This guide will look at some ways we can identify and decide on the correct approach for dealing with missing values. If you want to learn how to do this, then this is the place to start!

BCIT World, a Data Analytics Training Institute in Patna, can provide you with the perfect learning environment.

What Is Missing Data in a Dataset?

Missing data refers to information that should be present in a dataset but is unavailable or has not been recorded. It commonly appears as empty cells, blank fields, NULL values, or NaN entries.

Missing data can occur when information is not filled in, there have been technical issues, the data could not be collected, or records are unavailable. This can cause problems with the quality of data and reduce accuracy within the analysis, leading to misleading conclusions.

Why Does Missing Data Occur?

Understanding why data is missing will help decide on an approach to address missing values. Different causes may require different solutions.

Here are some common reasons:

1. Incomplete Data Entry

When completing online forms, surveys, or registration documents, sometimes there will be missing data. An example of this would be a customer filling out their name and email address but missing their phone number.

2. Data Collection Errors

During the data gathering process, there can be issues that affect the quality of the data collected. Common problems can include sensor failures, interrupted connections, and software problems.

3. Data Transfer or Integration Problems

When data is gathered from various sources, there can be mismatches between records. This can be due to differing formats, data not being entered, or issues during data transfer.

4. Human Errors

Typing errors, missing data, and incorrect data management can result in missing data.

Types of Missing Data

There are 3 types of missing data we commonly talk about:

1. Missing Completely at Random (MCAR)

Missing Completely at Random occurs when the absence of information has no relationship with the missing value or other recorded information.

Example: A technical issue temporarily interrupts a survey, causing some responses to remain unrecorded. The interruption is unrelated to the participants or their answers.

2. Missing at Random (MAR)

Missing at Random occurs when the probability of information being missing is related to other available information in the dataset, rather than the missing value itself.

Example: In a customer survey, younger customers may be less likely to provide their employment details. If age is recorded, the missing employment information may be related to that recorded age group.

3. Missing Not at Random (MNAR)

Missing Not at Random occurs when the reason information is missing is directly related to the missing value itself.

Example: Customers with very low incomes may be less willing to disclose their salary details in a survey.

How to Find Missing Data 

As an integral part of your data analysis process, you will need to identify where any missing values are within your dataset and how much data you are missing.

You don’t need to have any advanced technical knowledge to do this. All you’ll need is a spreadsheet programme (like Microsoft Excel or Google Sheets).

Here are 5 ways to help identify missing data:

1. Check for Blank Cells

Start by examining your dataset for empty cells.

For example, if you are working with a customer database, check whether fields such as customer name, email address, age, and location contain the expected information.

Look for:

• Empty cells
• Blank rows
• Incomplete records
• Missing information in important columns

2. Look for Special Missing Value Indicators

Missing information is not always represented by an empty cell. Some datasets use special values or placeholders.

Common examples include:

For example, an ‘N/A’ could be used within the customer database to indicate that there was no phone number.

Look at the documentation of the dataset to see what values are being used to show missing data. Not all zeros or negative numbers will be missing data, as they may be valid values.

3. Check Missing Values in Each Column

Examine individual columns to understand which fields contain the most missing information.

Let’s say we have a student dataset containing 1,000 rows:

This comparison shows that attendance has the highest number of missing records.

4. Calculate the Percentage of Missing Data

This can be helpful to understand how many missing values there are within the data. Calculating the percentage will tell us how much data we don’t have in each column.

You can use this formula:

Missing Data Percentage = (Number of Missing Values / Total Number of Values) × 100

This helps analysts understand how much data is actually missing and what type of data cleaning technique may be required.

5. Look for Patterns in Missing Data

Do not focus only on the number of missing values. Also examine whether they occur in particular groups or records.

For example:

• Are customer phone numbers missing more frequently than email addresses?
• Are survey responses incomplete for a particular age group?
• Are sales records missing during specific months?
• Are multiple columns missing information in the same records?

Identifying these patterns helps you understand possible reasons behind the missing information and choose an appropriate handling method.

How to Handle Missing Data in a Dataset

Once you have identified missing values, the next step is to decide how to manage them.

There is no single solution that works for every dataset. The right approach depends on the amount of missing information, the type of data, and the purpose of your analysis.

Here are 6 common ways:

1. Remove Rows With Missing Values

One straightforward approach is to remove records that contain missing information.

For example, if a customer record is missing several important fields and cannot be verified, you may decide to exclude it from a particular analysis.

When should you use this method?

• When only a small number of records contain missing values.
• When the remaining records provide sufficient information.
• When removing those records is unlikely to introduce significant bias.

Too much row removal can reduce the size of your dataset and reduce the accuracy of your results. If the missing records share something important, it may also introduce bias.

2. Remove Columns With Too Many Missing Values

Sometimes there will be a column in your data where there are a lot of missing values.

For example, within a customer dataset, there may be a column for secondary contact numbers that are not available for most customers.

If the column isn’t important to your analysis, you could choose to drop the column.

However, you shouldn’t delete a column simply because there are lots of empty cells. Think about how important the data is and if there is a way of finding or estimating the missing data.

3. Replace Missing Values With the Mean

This method replaces missing numerical data with the mean of the data available in that column.

This can be useful if you have reasonably well-distributed numerical data; however, if there are very high or low values, these can affect the mean.

4. Replace Missing Values With the Median

Median imputation involves replacing missing numerical values with the middle value of the available observations.

If there are any outliers in your data set. Or if your data is inaccurate, then this is a good choice, as it will not be affected by extreme values.

5. Replace Missing Values With the Mode

Mode imputation involves replacing missing values with the most frequently occurring value in a dataset.

This method is often used on categorical data. This could include department names, product groups, or customer preferences. This, however, can cause overrepresentation of the most common category.

6. Use Advanced Imputation Methods

When missing information is more complex, advanced statistical methods can help estimate unavailable values.

Some commonly used approaches include:

• K-Nearest Neighbours (KNN) Imputation: Estimates missing values using information from similar records in the dataset.
• Regression Imputation: Uses relationships between available variables to estimate missing values.
• Multiple Imputation: Creates multiple plausible versions of missing values and combines the results to account for uncertainty.

These methods can be useful when relationships between different variables are important.

Best Practices for Managing Missing Data

To deal with missing values correctly you should follow certain best practices in order to do so. Below are some important best practices that can be followed when handling real datasets. This will help ensure that you have good data quality management.

1. Never Replace Missing Values With Zero Automatically

Zero is a valid numerical value in many datasets. For example, a customer with zero purchases is different from a customer whose purchase information is unavailable.

Replacing missing values with zero without understanding their meaning can lead to incorrect conclusions.

2. Understand the Original Data

Verify the information’s source and understand how each field was gathered before making any modifications.

3. Keep a Copy of the Original Dataset

Always store the original data before changes. This way you can compare the cleaned dataset with the original dataset and recover information in case something goes wrong.

4. Document Every Change

Maintain a record of the missing values you identified, the methods you selected, and the reasons behind your decisions.

5. Validate the Cleaned Dataset

After handling missing values, check whether the dataset is consistent and suitable for analysis.

Review the number of records, data types, unusual values, and relationships between columns.

Practical Example: Handling Missing Data in a Customer Dataset

Let’s take a simple example to understand the whole process.

Imagine an e-commerce company maintains a customer dataset to understand purchasing behaviour.

The dataset initially looks like this:

After reviewing the records, the company identifies three missing values.

Step 1: Identify missing information

The age, location, and purchase amount columns contain incomplete records.

Step 2: Understand the reason

The company checks whether the missing values resulted from incomplete customer forms, technical problems, or information that was never collected.

Step 3: Select suitable methods

• Age: Consider median imputation if the available values provide a reasonable estimate.
• Location: Use an Unknown category if the information cannot be verified.
• Purchase Amount: Check the customer’s transaction records before estimating anything.

Step 4: Review the updated dataset

After handling the missing values, the company checks whether the records are complete and logically consistent.

Step 5: Proceed with analysis

The cleaned dataset can now be used for suitable customer analysis, provided the remaining data quality issues have also been checked.

This example demonstrates why handling missing data requires more than replacing blank cells. Each missing value should be evaluated according to its meaning and purpose.

Conclusion

Handling missing data is quite common while dealing with real-world data sets. However, learning to handle them properly can improve the quality of your analysis. By identifying empty cells and knowing what type of data is missing, we can improve the quality of our analysis by choosing appropriate methods of filling data.

Understanding why the data is missing will help you determine if you should delete, replace, or leave it as it is. If you want to improve your data analytics skills and learn how to work with real data, then BCIT World, a Data Analytics Training Institute in Patna, can help you to build the knowledge and skills you need to progress your career.

Frequently Asked Questions

1. What is missing data?

Missing data consists of data that has been left blank or cannot be found within a dataset. This could be blank cells, Null, NaN, or other placeholders. This can happen due to incomplete forms, technical issues, or errors when collecting data.

2. What are the 3 types of missing data?

There are commonly 3 types of missing data. They are Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR).

3. Why is it important to deal with missing data in data analytics?

By dealing with missing data, it reduces the risk of losing any valuable information, ensures that misleading results aren’t produced, and makes your analysis more reliable. It also ensures your data is ready to be reported on, visualised and used in Machine Learning.