Preparing data for machine learning - a blog about AI

Collecting and processing data is key to the successful use of machine learning (ML) in many areas, such as image and speech recognition, predicting trends, and process optimization. In this article, we explain the steps required to prepare data for the ML model.

1. data collection: the first step is to collect data that is representative of the problem to be solved. The data should be in an appropriate form and sufficient to train a model. There are various sources from which data can be collected, such as databases, APIs, web scraping, and IoT devices.

2. data preparation: after data collection, the data must be prepared to make it useful for training the ML model. This includes cleaning the data, i.e., removing erroneous or inconsistent data. It also includes transforming data, e.g., converting unstructured data into structured data. In addition, data must be categorized and tagged so that the model can interpret and correctly assign it.

3. data selection: Another important step is the selection of data to be included in the model. The data should have sufficient variance to ensure a realistic representation of the situation and should not be too highly correlated to avoid overfitting.

4. data formatting: formatting the data is another important step in preparing the data for the ML model. The data must be in a format that is supported by the ML platform. This may mean putting the data into a certain structure or adding certain metadata.

5. data analysis: after formatting and selection, the data must be analyzed to identify patterns and trends. This may involve analysis techniques such as data mining and statistical analysis. These techniques allow the data to be examined in terms of its properties, correlations, and distributions.

6. data partitioning: finally, the data must be partitioned into training, validation and test data. The training data is used to train the model, while the validation data is used to optimize the model. The test data is used to evaluate the performance of the model. The distribution of data across these partitions should be random and include a sufficient number of data points in each partition.

Overall, preparing data for machine learning is a complex process that involves a number of steps. The quality of the data has a major impact on the performance of the ML model, so it is important to be careful and thorough in data preparation.