Algorithms for machine learning take inputs from the data you feed them. Therefore, it is important that you have the right kind of data, while solving a particular problem. Processing the data is necessary before any algorithm, works on it. Here, you will come to know how you can prepare data for machine learning.

Why is it necessary to prepare data?

Data preparation refers to the process of cleaning a set of given data. Before you feed the data to the algorithms, it needs to be organised and processed. The noisy data needs to be transformed into a database.

It gets a proper shape, being organised in the columns in the intended structure. In other words, preparing data for machine learning refers to its transformation to information, which one can quickly understand.

It is not logical to use unstructured data while trying to gain the bets insights from machine learning. Therefore, you need to cleanse and prepare the same, before actually using it.

Steps of preparing data for machine learning

The process of getting the data ready for machine learning involves three stages. These include:

• Selecting the data
• Preprocessing data
• Transforming the data
• Selecting the data

Selecting the data

Firstly, it is necessary to choose a subset of data available, based on which the results will appear. Therefore, consider the data that needs to be addressed, given that you have got a particular problem. At times, people select data based on certain assumptions. However, you need to remember these assumptions, so that you can get them tested later on.

Focus on the following questions, when you choose the data set.

• To what extent will the data be available? You may have connected systems, database tables and other sources to derive the raw data.
• Is any data unavailable? What changes could have taken place if you had that data? Certain data may not be recorded. Hence, one might be able to simulate or derive this data.
• What information is not required to address a particular problem? It is easier to exclude data, than to include new ones.

Considering these aspects, you need to select the range of data for machine learning.

Preprocessing data

When you have chosen a range of data, you need to chalk out a plan regarding how to use the same. This process involves putting the data into a particular form, on which the algorithms will work.

During preprocessing of data, three major stages are involved.

• Formatting: The selected data needs to be organised in the desired pattern, or formatted. This will make it easier to work on it. For instance, you might want data obtained from a relational database into a flat file.
• Cleaning: The data cleaning process involves fixing or removal of missing data. In certain cases, the data may not be fully available. The data necessary to address a particular issue may not be available. Again, data may have to be eliminated under certain conditions. In some attributes, the data may be highly sensible. You may have to remove this data, or anonymise it to maintain confidentiality.
• Sampling: Most of the time, you will have more data than you actually need. The sampling method is used to select the data in these cases. Data overload will result in the algorithms taking longer time. The memory and computational requirements may also be larger. A small sample, representing the entire range of data works out much faster. The solutions can be prototyped, before you consider the entire set of data.

The tools of machine learning that are generally used will have an impact on preprocessing.

Transforming the data

Transforming the data forms the last stage of processing it. This step is generally influenced by the algorithm you choose, and the knowledge of the respective problem domain. In general, one needs to revisit various transformations of data that has been preprocessed, when they work on a specific problem.

Three techniques associated with transformation of data include:

• Scaling
• Attribute decompositions
• Attribute aggregations

Scaling: In the preprocessed data, certain attributes may contain a mixture of different scales for units like dollars, sales volumes or kilogram. Methods of machine learning, like data attributes have a similar scale, ranging between 0 and 1. This represents the smallest and largest values, respectively, for a certain feature. You need to choose a particular feature scaling, that you may want to perform.

Attribute decompositions: Certain features may be representing a complex structure, that a machine learning method might find more useful, when it is divided into the respective parts. For instance, a date and time components may be further divided. Maybe, the minute may be relevant to the issue under consideration. Therefore, you need to identify the feature decomposition that has to be performed.

Attribute aggregations: Certain features in the data may be aggregated to consolidate them into a single one. This might be relevant to the problem under consideration. For instance, each time a person logs into a system, instances of data may be available. This can be calculated and an aggregate on the number of times the person had logged in can be derived. This can make the process simplified.

Engineering the features from available data may take a lot of time. However, it has a direct influence on the algorithm’s performance. It helps in deriving meaningful insights through analysing the data.

6 steps to consider, before you feed data into the ML algorithm

• Have a look at the missing values
• Frame and structure the data after cleaning it
• Identify relevant features, that account for regression or classification during training
• Select the ML algorithm, based on the desired output and data available
• A small part of the training data has to be split into a validation set to find out whether the model overfits during training
• Maintain an optimal level for the learning rate, ensuring that the model does not over or undershoot during the process of correcting errors

Now you are aware of the basic steps that need to be considered when you train a machine learning model. The preprocessing stage is the most difficult one, while the algorithm application and output prediction process involve little effort.

Aarsh, Co- Founder & COO, Gravitas AI

www.gravitas.ai

• ai
• Machine learning