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Hello !!
Welcome to the latest edition of Business Analytics Review .
Data is the backbone of business analytics, but what happens when there are gaps in that data? Today, we’re talking about a common yet critical issue—missing values. Whether it's due to human error, system issues, or other factors, missing data can skew your results and lead to misguided decisions.
However, with the right strategies, you can navigate this challenge and ensure data integrity. Today, we'll explore various techniques to effectively handle missing values in your datasets.
Understanding Missing Data Patterns
Missing Completely at Random (MCAR): Missing values occur randomly and don't depend on other variables.
Missing at Random (MAR): Missing values depend on observed data but not on the missing data itself.
Missing Not at Random (MNAR): Missing values depend on the missing data itself, making imputation more challenging.
Common Techniques for Handling Missing Values:
DELETION -
Listwise Deletion: Remove entire rows or columns with missing values.
Pairwise Deletion: Remove only the rows or columns that contain missing values for a specific analysis.
IMPUTATION-
Mean/Median/Mode Imputation: Replace missing values with the mean, median, or mode of the respective column.
K-Nearest Neighbors (KNN): Impute missing values based on values from similar data points.
Multiple Imputation: Create multiple complete datasets by filling in missing values with plausible values.
DATA AUGMENTATION -
Generate synthetic data to fill in missing values or increase the size of your dataset.
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Handling Missing Values in Data: Best Techniques and Practical Approaches
Learn essential techniques for handling missing values in your datasets, including deletion, imputation, and predictive modeling, to improve data quality and enhance business analytics.
ML Handling Missing Values: Techniques to Address Data Gaps in Machine Learning Learn about deletion, imputation, and advanced approaches like KNN and regression-based techniques to ensure clean, accurate data for effective model training.
7 Essential Methods for Handling Missing Values in Datasets Discover 7 key techniques to handle missing values in datasets. Learn how to choose the right approach to maintain data integrity and improve your analysis.
Tool of the Day - InfoSphere Data Quality
InfoSphere Data Quality is a comprehensive data quality management tool designed to help organizations improve the accuracy, consistency, and completeness of their data. It offers a wide range of features to address various data quality challenges. For Example- Data Cleaning, Data standardization, Data profiling etc.
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Bonus Learning Material - pdf Guide (handling missing data)
Dealing-with-missing-data-the-art-and-science-of-imputation.pdf
Dive into the comprehensive guide by NASA on dealing with missing data. This resource explores the art and science of imputation techniques, offering insights on best practices for ensuring data accuracy and reliability in scientific research and analysis.
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