How Do You Handle Missing or Corrupted Data in a Dataset using machine Learning?

 Handling missing or corrupted data is an important step in machine learning data preprocessing. There are several methods for dealing with missing or corrupted data in a dataset, depending on the type and amount of missing/corrupted data and the specific problem being solved. Here are some common approaches:

  1. Data Imputation

Data imputation involves filling in missing values with estimates based on the rest of the data. One Machine Learning Classes in Pune is mean imputation, where the missing values are replaced with the mean value of the corresponding feature. Other methods include regression imputation, k-nearest neighbor imputation, and hot-deck imputation.

  1. Deletion

Deletion involves removing data points with missing or corrupted values from the dataset. This approach can be effective if the amount of missing data is relatively small and does not significantly affect the overall distribution of the data. However, if the amount of missing data is large, deletion may result in a significant loss of information.

  1. Interpolation

Interpolation involves estimating missing values based on the values of neighboring data points. Linear interpolation, spline interpolation, and polynomial interpolation are common interpolation methods.

  1. Model-based methods

Model-based methods involve building a machine-learning model that can handle missing or corrupted data. This approach can be effective Machine Learning Course in Pune data is related to other features in the dataset and can be predicted from them. Examples of model-based methods include multiple imputation and matrix factorization.

  1. Domain-specific methods

Domain-specific methods involve using knowledge of the problem domain to handle missing or corrupted data. For example, in time-series data, missing values can be estimated based on the previous or next observed value.

Conclusion

In summary, there are several methods for handling missing or corrupted data in a dataset, depending on the type and amount of missing/corrupted data and the specific problem being solved. The Online Machine Learning Training in Pune of method depends on various factors, such as the amount of missing data, the distribution of the data, and the available computational resources. Careful handling of missing or corrupted data can improve the accuracy and reliability of machine learning models.


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