Data Cleaning: Handling Missing and Inconsistent Data

Learning Objectives Identify missing data (e.g., null, NaN) within a dataset. Explain the difference between deletion and imputation strategies for handling missing values. Apply mean, median, or mode imputation to fill missing numerical or categorical data. Detect inconsistent data, such as formatting, capitalization, or unit differences. Write a simple algorithm or pseudo-code to standardize inconsistent text data. Articulate why data cleaning is a critical first step in any data analysis or machine learning project. Ever tried to follow a recipe with missing ingredients or confusing instructions? 🧑‍🍳 Messy data is just like that, and learning to clean it is the first step to becoming a data chef! This tutorial will teach you the essential skills of data cleaning. You w...

TermDefinitionExample Missing Data (NaN/Null)A value that is absent from a record in a dataset. It's a placeholder for information that was not collected or was lost.In a list of student records, a student's 'Age' field is empty. This is missing data. Inconsistent DataData that represents the same concept but is recorded in different, non-standardized formats.A 'Country' column containing entries like 'USA', 'U.S.A.', and 'United States'. They all mean the same thing but are written differently. Data ImputationThe process of replacing missing data with substituted values. This is a common strategy to avoid deleting incomplete records.If a student's test score is missing, we could replace it with the average (mean) score of a...

Pattern: Detecting Missing Values LOOP through each record in dataset: LOOP through each field in record: IF field is NULL or NaN: MARK record as having missing data Use this algorithmic pattern to systematically check every piece of data in your dataset to find where information is missing. This is the first step before you can decide how to handle it. Pattern: Mean Imputation 1. CALCULATE the sum of all non-missing values in a column. 2. COUNT the number of non-missing values. 3. CALCULATE mean = sum / count. 4. REPLACE all missing values in that column with the calculated mean. This is a common technique for filling in missing numerical data. It's best used when the data doesn't have extreme outliers, as they can heavily influence the mean. Patte...