### What are the different categories of missing data?

Different categories of missing data are: (a) Missing Completely at Random (MCAR), (b) Missing at Random (MAR), and (c) Missing Not at Random

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Different categories of missing data are: (a) Missing Completely at Random (MCAR), (b) Missing at Random (MAR), and (c) Missing Not at Random

Mean Imputation, Mode Imputation, Extreme Value Imputation, Nearest Neighbor Imputation, Expectation Maximization

Mean Imputation involves replacing the missing values with the mean of the non-missing observations.

Replace the missing values with the most frequently occurring value from the non-missing observations.

Replace the missing values with an arbitrary value located at the far end of the distribution of the feature, for example 999

Replace the missing values with the average of the observation’s k-nearest neighbors.

Use the iterative EM algorithm to impute missing observations using the “most likely” values based on the complete observations.

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- Machine Learning 101 (30)
- Statistics 101 (38)
- Supervised Learning (114)
- Regression (42)
- Classification (46)
- Logistic Regression (10)
- Support Vector Machine (10)
- Naive Bayes (4)
- Discriminant Analysis (5)
- Classification Evaluations (9)

- Classification & Regression Trees (CART) (23)

- Unsupervised Learning (55)
- Clustering (28)
- Distance Measures (9)
- Dimensionality Reduction (9)

- Deep Learning (23)
- Data Preparation (34)
- General (5)
- Standardization (6)
- Missing data (7)
- Textual Data (16)