Data Preprocessing Techniques for Machine Learning and AI

Data Preprocessing Techniques for Machine Learning and AI

Before applying either of these data preprocessing techniques, you must consider a model’s purpose and data requirements. These techniques do not fit every dataset.

Even if you opt for a curated AI data pack, it is crucial to ascertain that the provider processed the data as requested. This way, you reduce the likelihood of wasting time, computing resources and money during model development.

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Here are widely used data preprocessing techniques in machine learning and AI:

1. Data integration

If a model requires data from multiple sources, avoid preprocessing datasets separately. Doing this may lead to duplication, inconsistencies, or poor identity resolution.

Say a model requires customer data from specific spreadsheets and databases. The spreadsheets store customer names as, “full name,” while the databases separate the names as, “first name,” and, “last name.” Such inconsistent records slow down development as the model needs more time to ‘understand’ that the records are of the same customers.

Integrating the records under a standard column like, “full name,” solves this problem.

Data sources often have varied formats, data types, and naming conventions. However, you must harmonize the data into a final dataset, with a standardized structure.

Use ETL (Extract, Transform, Load) pipelines to ease the process. These pipelines extract data from specified sources, transform it to a common format, and load it to a specific database.

2. Data cleaning

Assess datasets for duplicate records, missing entries, formatting inconsistencies, or noise. The process of finding and eliminating these issues is what we refer to as data cleaning.

Duplicate records increase the likelihood of developing a biased model. Use tools like Pandas that identify and eliminate duplicate entries.

If some rows or columns have missing data, you can delete them. However, if a large portion of the dataset is missing, fill the gaps with estimates.

Some common estimation techniques include calculating the mean, mode, or median. Use the mean if the column with missing values follows a general trend or pattern. If not, compute the median.

Mode comes in when you are dealing with categorical data like, city or gender. Since you can’t get the median or mean, you fill the gaps with the most appearing value.

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3. Data reduction

Training an AI model on an extremely large dataset takes time. It also costs a lot, especially because of the storage costs. Here’s where data reduction comes in!

Data reduction is the process of reducing the size of a dataset without losing the essential patterns and relationships. Doing this improves computational efficiency during model training and enhances model performance in real-life applications.

Common data reduction techniques include dimensionality reduction, feature selection, and sampling.

Dimensionality reduction is the process of reducing the number of features (variable or columns) in a dataset to keep the most important only. A method like Principal Component Analysis (PCA) combines correlated features into fewer, distinct features.

Feature selection, on the other hand, involves identifying and retaining only the most relevant features in a dataset. Use a method like Lasso Regression, penalizing less important variables while automatically keeping the most useful ones.

Besides the features or variables, you can reduce the number of rows through random or stratified sampling.

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4. Data augmentation

Sometimes, obtaining the required dataset size can become expensive or near impossible. This is common in areas like healthcare or autonomous driving where the required data is either sensitive or difficult to obtain.

If you are looking to expand the size of an insufficient dataset, data augmentation is the answer. It is the process of creating more samples or variations from existing data. This could be image, text, audio, or video data.

For images, you can scale, crop, flip, rotate, blur, or even adjust color sharpness. This helps the AI model understand how the contents of a photo appear in different lighting conditions, angles, or distances.

If you are training a natural language processing model, you can replace the text samples with synonyms, translate the text, paraphrase text, or insert random words inside text.

For audio-based models, you can add background noise, stretch the time, speed up the audio, or change the pitch. These modifications help sound classification or recognition systems perform better in varying scenarios.

5. Data balancing

In some cases, you are going to realize that a dataset contains more samples of a certain event compared to another.

For example, a medical dataset with only 5% of the samples being of patients with a certain disease. If you train a model on such a dataset, there’s a high likelihood of it labeling a sick patient as “healthy.” This is because the model fed on overly biased data.

While you can generate synthetic samples of sick patients or reduce the sample count of healthy patients, it is advisable to take a hybrid approach. Reduce the sample size of healthy patients while increasing that of sick patients to find the right balance between diversity and data quantity.

There are also algorithmic strategies such as class weighting. This involves directing the model to give more importance to minority classes during training.

Wrapping Up!

And there you have it! Five data preprocessing techniques for machine learning and AI. Assess the purpose and data requirements of a model before applying either of the techniques.

Avoid training a model on unprocessed data as it may lead to issues like inaccurate output, poor predictions, or biased output. Data preprocessing is the foundation for efficient, accurate, and dependable AI solutions.

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