To deal with the missing values, we will do the following:
- We will specify a different class for the missing values.
- Now, we will check the distribution of values, and we would hold those missing values that are defining a pattern.
- Then, we will charge these into a yet another class, while eliminating others.
When dealing with a dataset with variables having more than 30% missing values, there are several strategies you can employ:
- Drop Variables: If the variables with missing values are not crucial for your analysis or modeling, you might consider dropping them entirely from your dataset. This simplifies your analysis and reduces noise introduced by missing data.
- Imputation: Imputation involves filling in missing values with estimated ones. Common techniques include:
- Mean/Median Imputation: Replace missing values with the mean or median of the observed values in that variable.
- Mode Imputation: Replace missing categorical values with the mode (most frequent value) of that variable.
- Regression Imputation: Predict missing values based on other variables using regression models.
- KNN Imputation: Use the values of nearest neighbors to impute missing values.
- Multiple Imputation: Generate multiple sets of plausible values for missing data to account for uncertainty.
- Domain-specific Imputation: In some cases, domain knowledge can help inform the imputation process. For example, if missing values represent zero values in a certain context, you might replace them accordingly.
- Feature Engineering: Instead of directly imputing missing values, you can create new features that indicate whether data was missing in the original variables. This can capture potentially useful information about the missingness pattern.
- Consideration of Missingness Mechanism: Understanding why data is missing can inform the choice of imputation method. Missing data can be missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). Different imputation methods may be more appropriate depending on the mechanism.
- Model-Based Imputation: Use machine learning algorithms to predict missing values based on other variables in the dataset.
- Ensemble Approaches: Combine multiple imputation methods to improve accuracy and robustness.
- Consultation with Domain Experts: In complex cases, consulting with domain experts can provide insights into the nature of missing data and the most appropriate strategies for handling it.
Ultimately, the choice of approach depends on factors such as the nature of the data, the analysis goals, and the assumptions about the missing data mechanism. It’s often advisable to try multiple approaches and evaluate their impact on the analysis results.