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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
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Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
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How to Handle Missing Values of Categorical Variables?

Chirag Goyal
Chirag Goyal Last Updated : 10 Dec, 2024
7 min read

Real-world data collection has its own set of problems, It is often very messy which includes missing data, presence of outliers, unstructured manner, etc. Before looking for any insights from the data, we have to first perform preprocessing tasks which then only allow us to use that data for further observation and train our machine learning model. Missing value in a dataset is a very common phenomenon in the reality. In this blog, you will see how to handle missing values for categorical variables while we are performing data preprocessing. Missing value correction is required to reduce bias and to produce powerful suitable models. Most of the algorithms can’t handle missing data, thus you need to act in some way to simply not let your code crash. So, let’s begin with the methods to solve the problem.

“Data is the fuel for Machine Learning algorithms”.

Table of contents

What are Missing Values in a Dataset?

In a dataset, missing values refer to the absence of data for one or more variables or observations. This can occur for various reasons, such as data entry errors, equipment malfunction, or participants failing to provide information. Missing values can affect statistical analyses, leading to biased or incorrect results. Thus, it is important to handle missing values appropriately by removing them or using imputation methods to estimate their values.

Types of Missing Data

  1. Missing Completely at Random (MCAR): When data is MCAR, the missingness occurs randomly, and there is no relationship between the missing values and the observed data. In other words, the probability of a value being missing is the same for all observations, regardless of their values.
  2. Missing at Random (MAR): When data is MAR, the missingness is not random, but other observed variables can explain it. In other words, the probability of a missing value depends on the observed data, not the missing data itself.
  3. Missing Not at Random (MNAR): When data is MNAR, the missingness is not random and cannot be explained by other observed variables. In other words, the probability of a value being missing depends on the missing data, which can lead to biased or incorrect results if not handled properly.

Methods for Dealing with Missing Values in Dataset

Let’s have a dummy dataset in which there are three independent features(predictors) and one dependent feature(response).

            Feature-1            Feature-2              Feature-3               Output
               Male              23                 24                  Yes
              – – – –              24                 25                  No
             Female              25                 26                  Yes
             Male              26                 27                   Yes

Here, We have a missing value in row-2 for Feature-1.

The popular methods which are used by the machine learning community to handle the missing value for categorical variables in the dataset are as follows:

Step 1: Delete the Observations

If there is a large number of observations in the dataset, where all the classes to be predicted are sufficiently represented in the training data, then try deleting the missing value observations, which would not bring significant change in your feed to your model.

For Example,1, Implement this method in a given dataset, we can delete the entire row which contains missing values(delete row-2).

Step 2: Replace Missing Values with the Most Frequent Value

You can always impute them based on Mode in the case of categorical variables, just make sure you don’t have highly skewed class distributions.

NOTE: But in some cases, this strategy can make the data imbalanced wrt classes if there are a huge number of missing values present in our dataset.

– Generally, replacing the missing values with the mean/median/mode is a crude way of treating missing values. Depending on the context, like if the variation is low or if the variable has low leverage over the response, such a rough approximation is acceptable and could give satisfactory results. In this case, since you are saying it is a categorical variable — this step may not be applicable.

For Example, 1, To implement this method, we replace the missing value by the most frequent value for that particular column, here we replace the missing value by Male since the count of Male is more than Female (Male=2 and Female=1).

Step 3: Develop a Model to Predict Missing Values

One smart way of doing this could be training a classifier over your columns with missing values as a dependent variable against other features of your data set and trying to impute based on the newly trained classifier.

Algorithms for Missing Values of Categorical Variables

  • Divide the data into two parts. One part will have the present values of the column including the original output column, the other part will have the rows with the missing values.
  • Divide the 1st part (present values) into cross-validation set for model selection.
  • Train your models and test their metrics against the cross-validated data. You can also perform a grid search or randomized search for the best results.
  • Finally, with the model, predict the unknown values which are missing in our problem.

NOTE: Since you are trying to impute missing values, things will be nicer this way as they are not biased and you get the best predictions out of the best model.

For Example, 1, To implement the given strategy, firstly we will consider Feature-2, Feature-3, and Output column for our new classifier means these 3 columns are used as independent features for our new classifier and the Feature-1 considered as a target outcome and note that here we consider only non-missing rows as our train data and observations which is having missing value will become our test data. We have to do the prediction using our model on the test data and after predictions, we have the dataset which is having no missing value.

Step 4: Deleting the variable

If there are an exceptionally larger set of missing values, try excluding the variable itself for further modeling, but you need to make sure that it is not much significant for predicting the target variable i.e, Correlation between dropped variable and target variable is very low or redundant.

For Example, 1, To implement this strategy to handle the missing values, we have to drop the complete column which contains missing values, so for a given dataset we drop the Feature-1 completely and we use only left features to predict our target variable.

Step 5: Apply unsupervised Machine learning techniques

In this approach, we use unsupervised techniques like K-Means, Hierarchical clustering, etc. The idea is that you can skip those columns which are having missing values and consider all other columns except the target column and try to create as many clusters as no of independent features(after drop missing value columns), finally find the category in which the missing row falls.

For Example, 1, To implement this strategy, we drop the Feature-1 column and then use Feature-2 and Feature-3 as our features for the new classifier and then finally after cluster formation, try to observe in which cluster the missing record is falling in and we are ready with our final dataset for further analysis.

Implementation in Python

 Import necessary dependencies.

missing values import libraries

 Load and Read the Dataset.

 Load and Read the Dataset.

 Find the number of missing values per column.

missing values is null

 Apply Strategy-1(Delete the missing observations).

Apply Strategy

Apply Strategy-2(Replace missing values with the most frequent value).

Apply Strategy

Apply Strategy-3(Delete the variable which is having missing values).

Apply Strategy-3

Apply Strategy-4(Develop a model to predict missing values).

For this strategy, we firstly encoded our Independent Categorical Columns using “One Hot Encoder” and Dependent Categorical Columns using “Label Encoder”.

– Read and Load the Encoded Dataset.

– Make missing records as our Testing data.

– Make non-missing records as our Training data.

– Separate Dependent and Independent variables.

- Separate Dependent and Independent variables.

– Fit our Logistic Regression model.

- Fit our Logistic Regression model missing values

– Predict the class for missing records.

# Predict the class for a missing record
input = [[24, 25, 0]]
ln.predict(input)

output :

array([1.])

This completes our implementation part!

End Notes

Missing values in a dataset can be a challenge in data analysis and lead to biased or incorrect results if not handled appropriately. Therefore, it is essential to identify the type of missing data and use appropriate techniques to handle them, such as imputation or exclusion.

If you want to learn more about handling missing data and other advanced data analysis techniques, consider enrolling in our Blackbelt program. You will gain in-depth knowledge of data analysis techniques, data visualization, machine learning, and statistical modeling, equipping you with the skills needed to become a data-driven professional in today’s data-intensive world.

Frequently Asked Questions

Q1. What is a good way to fill in missing values in a dataset?

A. A good way to fill in missing values in a dataset is using imputation techniques such as mean, median, mode, interpolation, or hot-deck imputation. The chosen method should depend on the dataset’s nature and the missing values’ extent.

Q2. What is a missing value in a database called?

A. In a database, a missing value is called a null value or null. It represents the absence of data for a particular observation or variable and can be caused by various factors such as data entry errors or missing information.

Q3. How do you handle missing values in a data frame?

A. To handle missing values in a data frame, one can either remove the rows or columns that contain missing values or fill in the missing values using imputation techniques such as mean or median imputation, interpolation, or hot-deck imputation. The chosen approach depends on the analysis type and the missing data extent.

Chirag Goyal
Chirag Goyal

I am a B.Tech. student (Computer Science major) currently in the pre-final year of my undergrad. My interest lies in the field of Data Science and Machine Learning. I have been pursuing this interest and am eager to work more in these directions. I feel proud to share that I am one of the best students in my class who has a desire to learn many new things in my field.

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Responses From Readers

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Anand Ramteke
Anand Ramteke

The analysis/recommendations does not account for the following: 1. Missing Mechanism (MCAR/MAR/ NMAR), 2. Supervised / Unsupervised technique, 3. Importance of understanding business/domain prior to imputing 4. There is no information on how any missing value treatment method can do to the model - like add bias, artificially strengthen relationships, etc. I think the author should do more research on this topic. Thanks, Anand

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