How to Handle Missing Values of Categorical Variables?
“Data is the fuel for Machine Learning algorithms”.
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.
Methods for dealing with missing values
Example 1, Let’s have a dummy dataset in which there are three independent features(predictors) and one dependent feature(response).
|– – – –||24||25||No|
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:
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).
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).
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.
Here’s the algorithm that you can follow:
– 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.
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.
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.
Load and Read the Dataset.
Find the number of missing values per column.
Apply Strategy-1(Delete the missing observations).
Apply Strategy-2(Replace missing values with the most frequent value).
Apply Strategy-3(Delete the variable which is having missing values).
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.
– Fit our Logistic Regression model.
– Predict the class for missing records.
This completes our implementation part!
Thanks for reading!
This article introduces you to different ways to tackle the problem of having missing values for categorical variables.
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About the Author
Currently, I pursuing my Bachelor of Technology (B.Tech) in Computer Science and Engineering from the Indian Institute of Technology Jodhpur(IITJ). I am very enthusiastic about Machine learning, Deep Learning, and Artificial Intelligence.
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