When working on a machine learning model, the first step revolves around preparing the data for analysis. Having clean data is necessary to make it ready for prediction. Ignoring this step can affect the accuracy of the model and can lead to unreliable results.
This article talks about the most common data issues that users
face during model Development and the ways by which they can be fixed.
A. Missing Data
The most common issue that occurs when training the model is
the absence of values in dataset due to which the model can crash.
How the Missing Data Issue can be handled:
- Drop the fields where the missing data is very small
- Follow the below imputation techniques--
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For numerical columns, impute with mean/median
Ø For categorical columns, impute with mode
- Adding a binary column also helps in indicating missing values
B. Noisy Data
The prediction accuracy of the machine learning model
reduces with the irrelevant data in dataset. Such issues arise from incorrect
measurements, sensor errors, or human input mistakes.
Noisy Data issue can be tackled by following ways:
- Z-score:
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Also called the standard score
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Used for scaling the features in the dataset
Ø Tells how far the value is from the mean in terms of standard deviations
- Interquartile Range (IQR) Method:
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This method calculates the range within which
the middle 50% of your data lies.
Ø It helps in identifying outliers by finding values that fall significantly outside this range.
- Binning:
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Also called bucketing
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Used to transform continuous numerical variables
into discrete categorical variables.
C. Inconsistent Data
Inconsistencies in the data occur due to mistakes in data
entry, data processing, or data integration because of which the values in the
data show different formats or units.
How to handle Inconsistencies:
- Standardizing the formats using different functions to keep everything uniform.
- Validating the data against predefined rules to ensure it meets specific requirements.
D. Imbalanced Data
It occurs in classification problems of machine learning
where one category dominates the other.
How to fix Imbalanced Data Issue:
- Using the below resampling techniques to adjust the size of classes to make them more balanced—
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Oversampling
Ø Undersampling
- Using the below evaluation metrics to assess model performance on the dataset—
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Precision
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Recall
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F1-score
E. Duplicate Data
Another most common data issue that occurs in machine learning is data duplicity that can hamper the analysis process. This issue signifies repeated records in the dataset that can bias the model and inflate performance.
Best Practices for handling Data Duplicates:
- Analyze the impact of duplicates before removal, as some duplicates carry meaningful information.
- Combine exact and fuzzy methods for comprehensive deduplication.
- For large-scale datasets, consider machine learning-based deduplication to improve efficiency and accuracy.
F. High Dimensional Data
It refers to the datasets with a large number of features or
attributes, often exceeding the number of observations. Such datasets are
challenging due to their complexity and the volume of information each data
point contains.
Ways of dealing with High Dimensionality:
- Feature Selection—
Ø Identifying and retaining only the most relevant features to improve model performance.
- Principal Component Analysis—
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Transforming the complex data into a smaller set
of uncorrelated variables and also preserving the most variance in the data.
G. Overfitting
Overfitting happens when a model learns too much from the
training data instead of just the underlying pattern.
How the Overfitting issue can be handled:
- Simplifying the model by reducing the number of features
- Using L1 and L2 regularization techniques to penalize complexity
- Using cross validation i.e. splitting the dataset into a training set and test set for keeping the model as simple as possible.
H. Underfitting
Underfitting happens when a model is too simple to capture
the underlying structure of the data.
How the Underfitting issue can be handled:
- Increasing model complexity by using more advanced algorithms
- Adding meaningful features to the dataset
- Increasing training time of the model
- Transforming inputs to better represent patterns
Conclusion
With machine learning on the rise, the need for highly
accurate models for solving industrial problems has become greater, but what
comes first is the data that is used and the structure behind it.
