Data, more precisely sets of data is a crucial aspect of Machine Learning projects. Machine learning algorithms learn from data. It is important that you feed them the right data for the problem you want to solve. Even if the user has good data, the user needs to make sure that it is on a useful scale, and even that meaningful features are there. At the same time, the data cleaning and preparation process are the most difficult challenges that plague most projects. It is now the heart and soul of a business, Data scientists spend most of their time on data preparation instead of developing, optimizing, and deploying new Machine Learning projects. Data preparation refers to a set of procedures that reforms the data to get consumed by machine learning algorithms and learnings. And these procedures consume most of the time spent by data scientists on machine learning.
Here are some typical steps involved in selecting and preparing data for Machine Learning algorithms:
- Collection,
- Pre-Processing,
- Transformation
Data Collection
This is a critical first step for selecting and preparing data for Machine Learning that involves collecting data from various sources such as databases, files, and external repositories. If the user aims to use Machine Learning for predictive analytics, the first thing to do is combat data fragmentation. Data collection may be a tedious task that burdens the user and overwhelms them with instructions. Users need to consider what data they actually need to address the problem or situation they are working on. Data Augmentation and Data Labelling might be required to expand the size of the existing dataset without gathering more data and allowing it to get performed manually by crowd workers or automatically using specialized frameworks available in the market.
Data Pre-Processing
After collecting the required data, the second step for selecting and preparing data for Machine Learning Involves pre-processing. The data may be in an undesired format, unorganized, or extremely large further steps are needed to enhance its quality, a user needs to consider how they are going to use the data. The steps involved with pre-processing of data includes:
- Formatting: Data formatting is sometimes referred to as the file format that the user is using. If the user is aggregating data from different sources and updated by many people, it is a must to ensure that all variables within the same attribute are consistently written.
- Cleaning: Data Cleaning refers to removing or fixing messy data, removing duplicates, and managing missing values. Substituting the missing numerical values with mean figures or dummy values is also acceptable.
- Sampling: Data Sampling is required when a user has massive amount of data. Large amount of Data can result in much longer running times for algorithms and larger computational and memory requirements. Users can take a smaller representative sample of the selected data that may be much faster for exploring and prototyping solutions instead of considering the whole dataset.
Data Transformation
Also known as Feature Engineering, the last stage in selecting and preparing data for Machine Learning projects involves transforming the pre-processed data into forms that are more compatible with specific Machine Learning algorithms and learnings. The data can be transformed through various processes that include scaling, decomposition, or aggregation:
- Scaling: The preprocessed data may contain attributes with a mixture of scales for various quantities such as dollars, kilograms, and volume, etc. Many machine learning methods like data attributes have the same scale such as between 0 and 1 for all the smallest and largest value for a given feature. Therefore, Scaling is necessary to suppress this effect by suppressing all features to a similar level of magnitude.
- Decomposition: A feature that represents a complex concept that may be more useful to a machine learning method when splitting into its constituent parts. In simple words, a dataset that is complicated, decomposing it into various constituent parts may be more understandable to a Machine Learning algorithm.
- Aggregation: It refers to combining multiple features into a single feature that can be more useful for an algorithm. Aggregation can be performed to bring related features together and decrease the dimensionality of the dataset.
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