Almost every business depends on data to grow; in the near future1, leveraging the power of data might become a necessity for their survival. As the volume of data, we generate grows2, we need to learn better ways to extract value from it because not all data can be used in the raw form that it is generated in.
Before any type of data can be used by organizations, the data must go through a process to make it ready for use. Whether you work with a data collection service or collect your own data, preprocessing is an important step. If this process is not done right, it can degrade the dataset’s quality, triggering a chain of issues in various parts of the business.
What is data preprocessing?
Since raw data or unstructured data (Text, image, audio, video, documents, etc.) can not be directly fed into machine learning models, data preprocessing is used to make it usable.
Usually, this is the first step of starting a machine learning project to ensure that the data used for the project is well-formatted and clean. However, data preprocessing is not limited to developing and training AI or ML models. Organizations can use this process to prepare any data in their business. They can process:
- Customer data
- Department-specific data (Sales data, financial data, etc.)
- Product data
- Research data
Why is it important?
While working with datasets, you must have heard the term “garbage in, garbage out.” This simply means the performance of your AI/ML model (or any other data-hungry project) will be as good as the data that you train it with. Even the most sophisticated algorithms can produce garbage results or be harmful and biased if trained with dirty or unprocessed data.
In the current business environment, a successful business is considered one where data-driven decision-making is practiced. However, if the data is flawed, then the decisions driven from it will also be impaired. Preprocessing data before using it in any data-hungry project can remove data issues such as:
- Data duplication
- Image/video data with hidden objects
- Spelling errors in document/text data
- Audio data with too much noise, etc.
What are the steps of preprocessing data?
The following steps can be followed to preprocess unstructured data:
1. Data completion
One of the first steps of preprocessing a dataset is adding missing data. Feeding an AI/ML model with a dataset with missing fields can take time and effort. The following actions can be taken to manage missing fields:
- Consider the impact of the missing data: The data scientists must decide whether to manually fill, discard or ignore missing fields in a dataset. Their decision must consider the impact of the missing data, the size of the dataset, and the amount of the missing data.
- Use the average (mean) method: In some cases, average or estimated values can be added in the missing fields based on other values. For instance, missing fields can be filled with the average (mean) of the previous and next temperature values for a temperature-measuring application.
2. Data noise reduction
Noisy data is useless for machine learning models since they can not read it. Noisy data can make its way into the dataset due to faulty data collection, humanoid errors, glitches in the system, etc. It can be cleared in the following ways:
- Binning method: This method involves arranging the data in different segments and binning it. Then the data in the bins can be replaced by their average, medium, or minimum and maximum values.
- Regression method: A linear or multiple variable regression function is used to smooth the data.
- Clustering: method Clustering helps smoothen the data by identifying similar data groups in a dataset and adding them to separate clusters (groups).
3. Data transformation
Raw or unstructured data is collected from multiple sources in different formats. While some of these formats are acceptable by a machine learning model, others are not. The data transformation step involves changing the unacceptable forms of certain data types into acceptable ones. This is done in the following ways:
- Normalization: When the data involves large values, they are converted into ranges to bring uniformity to the values
- Attribute Selection: In this method, only the relevant attributes according to the project’s features are selected. For instance, if a computer vision system only needs to scan objects in daylight, data with dark images will be removed.
- Aggregation: This stage involves making a summary of the total dataset. For instance, purchasing data can be summarized to be shown as per month. It’s basically a description of the dataset.
- Concept hierarchy generation: Lower-level data is converted into higher-level data to make the data more general and organized. For instance, in data related to addresses, cities can be converted into countries.
4. Data Reduction
We have discussed before that the bigger the dataset, the more accurate the AI/ML model will be. However, this is only applicable when the quality is maintained. 1 clear image is better than 10 blurry ones. Sometimes datasets can involve redundant items that can make the dataset unnecessarily complicated.
In such cases, data reduction can help eliminate redundant data and bring down the dataset size to just right. However, if the dataset is too small, it can make the model underfitted or biased. Therefore it is important to ensure that the necessary data are not eliminated during the reduction process. Data can be reduced in the following ways:
- Creating data combinations: In this method, data is fitted into smaller pools. So, for instance, if the data tags are male, female, or doctor, they can be combined as male/doctor or female/doctor.
- Dimensionality reduction: This method involves eliminating unnecessary data points. For example, if a computer vision-enabled quality control system is not required to scan the products from different angles, then image data with angle variations can be removed. This can be done by using Algorithms such as K-nearest neighbors3.
- Data compression: This involves compressing large machine-learning data files. This can be done in a non-lossy way, by saving the original data, or a lossy way, by deleting the original data.
5. Data validation
This step consists in assessing the dataset for quality assurance. Validation involves feeding the data into a machine-learning model to test its performance. If the data scientists are unsatisfied, the data goes through the cleaning process again. This cycle is repeated till the optimum results are achieved.
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