Handling Missing Values: A Comprehensive Guide

Overview:

Missing data is a common issue in real-world datasets and can significantly impact the performance of machine learning models if not handled appropriately. This notebook provides a detailed exploration of different techniques to manage missing values, with a focus on both numerical and categorical features. Whether you're a beginner looking to understand the basics or an experienced data scientist seeking to refine your skills, this guide will equip you with practical methods to effectively deal with missing data.

Techniques to Handle Missing Values

1.Mean, Median, Mode Imputation:

Mean Imputation: Replace missing values with the mean of the observed values. Suitable for numerical data.

Median Imputation: Replace missing values with the median of the observed values. More robust to outliers than mean imputation.

Mode Imputation: Replace missing values with the most frequent value (mode). Useful for categorical data.

2.Random Sample Imputation:

Replace missing values with random samples drawn from the observed values. This can add variability and preserve the distribution of the data.

3.Capturing NaN Values with New Features:

Create a new binary feature indicating whether the original value was missing. This helps preserve information about missingness.

4.End of Distribution Imputation:

Replace missing values with a value at the end of the distribution, such as a very high or very low value. This is useful for detecting the impact of extreme values.

Techniques to Handle Categorical Missing Values

1.Frequent Category Imputation:

Replace missing values with the most frequent category (mode) in the categorical feature. This approach assumes that the most common category is a reasonable substitute for missing values.

2.Adding a Variable to Capture NaN:

Create an additional binary variable indicating whether the original value was missing. This method captures the information about missingness and allows the model to learn if the absence of data is informative.

3.Replacing NaN with a New Category:

Introduce a new category specifically for missing values (e.g., "Unknown" or "Missing"). This approach treats missing data as a separate category and can be useful when the absence of data might have a specific meaning.