Unsupervised Learning in Machine Learning > Data Preprocessing for Machine Learning

Handling Missing Data

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  • Substituting an arbitrary value.
  • Mode is being replaced.
  • Substituting Median.
  • Forward filling (replacing with preceding value).
  • Backward filling by replacing with the following value.
  • Interpolation.
  • Assume the most common value.

  • The first method is to delete rows or columns. When it comes to empty cells, we normally employ this strategy.
  • The second method involves substituting aggregated values for missing data.
  • The third method entails the creation of an unknown category.
  • The fourth method involves predicting missing values.

When no value is available in one or more of an individual's variables, missing data appears.

  • Deletions. Deletion in pairs. Deleting rows one by one from a list. Complete columns are being dropped. Imputation Techniques for Beginners. Using a constant value for imputation. Using statistics for imputation (mean, median, mode)
    Imputation of K-Nearest Neighbors.

  • Imputation by deduction. Rather than a statistical rule, this is an imputation rule determined by logical argument.
  • Imputation of the mean, median, and mode.
  • Imputation of Regression.
  • Imputation of Stochastic Regression.

Missing data must be handled either by deletion or imputation in the field of data-related research (handling the missing values with some estimation). Moving forward, data cleansing, or managing missing values, is the initial stage in data science when working with datasets.

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  2. Environment Setup part 1
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  3. Environment Setup part 2
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  4. Environment Setup part 3
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  1. Data Wrangling
    12m 26s
  2. Importing Libraries and Dataset
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  3. Handling Missing Data
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  4. Handling Missing Data - Practical
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  5. Encoding Categorical Data
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  6. Encoding Categorical Data - Practical
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  7. Splitting Dataset
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  8. Splitting Dataset - Practical
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  9. Normalizing the Data - Part 1
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  10. Normalizing the Data - Part2
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  11. Finding Machine Learning Datasets
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  12. Exploratory Data Analysis
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  13. Plotting Graphs - Part 1
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  14. Plotting Graphs - Part 2
    23m 44s
  15. Distribution Models - Part 1
    33m 54s
  16. Distribution Models - Part 2
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  17. Assignment of Data Preprocessing for Machine Learning
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  1. Machine Learning Paradigms
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  2. Sampling Methods
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  3. Underfitting and Overfitting in Models
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  4. Variance and Bias
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  1. Distance Metrics
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  2. K-Means Clustering
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  3. K-Means Clustering - Practical
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  6. Divisive Clustering - Practical
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  7. DBscan Spatial Clustering
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  8. FP Growth
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  9. Assignment of Unsupervised Learning Algorithms
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  1. Overview of Dimensionality Reduction
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  3. Princinpal Component Analysis - Practical
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  4. Linear Discriminant Analysis
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  5. Linear Discriminant Analysis - Practical
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  6. Assignment of Dimensionality Reduction
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  1. Advance Trends in Machine Learning
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  1. Course Summary
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  1. Interview Questions part 1
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  2. Interview Questions part 2
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  3. Interview Questions part 3
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  1. Career Guidelines
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