Missing values are a common challenge in data analysis, and we have incorporate techniques for effectively handling them. We employ two main approaches: univariate imputation and multivariate imputation.
Univariate imputation involves filling in missing values based on the distribution of individual features. Here are some examples of univariate imputation techniques used:
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Mean Imputation: Missing values in numerical features are replaced with the mean of the available values for that feature.
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Median Imputation: Similar to mean imputation, missing values in numerical features are replaced with the median of the available values, which is less sensitive to outliers.
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Mode Imputation: Categorical features with missing values are imputed by replacing them with the mode (most frequently occurring value) of the available values.
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Random Imputation : Missing values in any feature are randomly assigned values from the observed values of that feature. This method helps maintain the statistical properties of the data.
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Constant Imputation: For features where missing values carry a specific meaning or when missingness is systematic, we replace the missing values with a constant value, such as zero or a designated placeholder value.
Multivariate imputation takes into account the relationships between features to impute missing values more accurately. We utilize the following multivariate imputation techniques:
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K-nearest neighbors (KNN) Imputation: Missing values in both numerical and categorical features are estimated by considering the values of the K nearest neighbors. This approach leverages the relationships between data points to provide more reliable imputations.
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Multiple Imputation by Chained Equations (MICE): MICE imputation iteratively estimates missing values using regression models. The missing values for a particular feature are imputed based on the observed values and imputations from other features. This process is repeated multiple times to generate several imputed datasets, which are then combined for further analysis.
By incorporating these univariate and multivariate imputation techniques, we aim to handle missing values effectively and ensure the integrity of our data analyses.