How to Create Training Data for Machine Learning?

Training data sets are the key ingredients for machine learning to build a well-organized functional model that can work with the highest relevancy and give accurate results. And it is awfully hard to find such training data that can fit into your demand for machine learning. In this blog, we shall discuss how we can create training data for machine learning with simple processes and other alternates to gather such data at an affordable cost.

How to Build Training Data Set?

To build a functional model you must keep in mind the flow of operations involved in building a high-quality dataset. To solve a particular problem in respect of the same, the data should be accurate and authenticated by specialists. Just take an example if you want to determine the height of a person, then other features like gender, age, weight, or the size of clothes are among the other factors considered seriously.

In this example, the clothes, weight, and height of a person are important while color and fabric material will not add any value as training data. Such irrelevant have very little weightage while predicting the height of the person and as per the machine learning golden rules, the larger the data the results would be a better help to create the robust models.

Selection of Training Data

The first step towards creating machine learning data sets is selecting the right data sets with the right number of features for datasets. Moreover, the data should be reliable and should have the least number of missing values, because more than 25 to 30% of missing values are not considered during the training of machines.

However, in certain conditions, there are instances where the relationship between this feature and the Y feature is high. In such cases, you must manually imputed and handle the missing values to get better results.

For example, if an organization has borrowed a loan from the bank, and a feature containing a value like the GDP of the country is available with only 30% missing values. If this particular feature has a very high weightage to predict the institution can repay the loan or not then this feature need to be considered with high priority. While, if this feature doesn’t have any importance in developing the AI model, then there is no need to include such data.

Processing of Training Data

Once you have selected the right data the processing of data also involves a selection of the right data from the complete data set and building a training set and the entire process is given here step-by-step to process the training data service.

Organize and Format: As, the data can be distributed into different files or folders, so you need to find the relation between the datasets and preprocess to form a dataset of required dimensions. And data also need to be transformed into universal language before organizing into a particular format.

Cleaning of Data: This is one of the most important stages in data processing where you must remove unwanted characters from the data and cleaning up the missing values. The missing values can be removed or replaced and if there are such missing values they can be replaced or removed to get more accurate results.

Extraction of Features:This stage involves the process of analysis and optimization of the number of features. Here you must find out which features are important for prediction and select them for faster computations and low memory consumption. For example, if you are dealing with image classification, then remove all the irrelevant images.

Conversion of Training Data

After data selection and processing, you must convert the data into a meaningful dataset. The data conversion process also involves several steps that are discussed below.

Scaling: It is very essential when a dataset is placed while considering the linear data set like bank data. If the feature containing the transaction amount is important, then data must be scaled to build a robust model. To perform this action, the Correlation matrix, the Pearson method is used to find the relationship between such things. If data is not scaled by definite values this might lead to a misunderstanding of the data.

Disintegration and Composition: This step involves the breakup of a particular feature to build better training data for the model you understand more comprehensively. Splitting of time-series feature is one of the best examples of data disintegration in which you can extract the days, months, year, hour, minutes, seconds, etc. from a particular sample. Separating and processing such features may result in better accuracy to get a better output.

Composition: This is the last process that involves combining different features into a single feature to get more accurate or meaningful data. The composition of data sets combined with different features can be said to a true or high-quality data sets that can be used for machine learning. The more the data accurate the predictions would be also precise.

Conclusion

In this blog, you will get to know how to create training data for machine learning with a step-by-step process. It will help you to understand how a processed training set helps machine learning as a service to develop the relationship between the features. However, the entire process consumes a lot of time and required in-depth analysis and examination of the data to get the best results. As well-classified and well-organized datasets help machine learning models to train the models at a faster rate and get robust results in different scenarios.

How to Get Training Data for Machine Learning?

Nevertheless, if you do not have time or enough facilities to create the training data for machine learning you can get such data from companies providing training data at affordable pricing. And if you are looking for data labeling companies get in touch with Cogito that offers the best quality training datasets for machine learning and AI-oriented models required to train with such data and develop a functional model or useful business application. Cogito will give you the accurate and most relevant amount of data at the lowest cost while ensuring the quality to help you to train your AI-enabled machines to work with promising results.