Machine learning concepts. Data preparation
Each machine learning process related to the use of neural networks consists of at least two parts. The first part is related to data loading and preparation for training. The action is known as ETL (extract, transform, load). The second part concerns the actual training of the network. The overall process can be divided into the following parts and steps.
1. Extract, Transform, Load
1. Selection of input variables according to the problem being solved;
2. Structuring the data in a format suitable for loading it in the software environment;
3. Loading data into the environment;
4. Conversion of the data into an appropriate form;
5. Data separation – training and test data;
6. Data normalization.
2. Network training and evaluation
1. Building a network model according to the problem being solved;
2. Setting up network hyperparameters;
3. Network training;
4. Network testing;
5. Comparing the results;
6. Tuning the hyperparameters in order to achieve more satisfactory results;
7. Persisting the trained model. Exporting the model for further use and loading in other environments.
2.1. Extract, Transform, Load
Selection of input variables according to the problem being solved.
2.2. Structuring the data in a format suitable for loading it in the software environment
The input data is structured in a four-column text file, each of which contains values for the corresponding parameter. The text file has the following structure.
3. Loading data into the environment
4. Conversion of the data into an appropriate form
5. Data separation - training and test data
6. Data normalization
Accessing an API back-end from Angular client may result in missing response headers. A common scenario is a missing Authorization header, containing the JSON Web Token (JWT) which is returned from the back-end service when the user logs in successfully.
The neural network model consists of two layers – an LSTM layer and an output Dense layer. The reason for choosing an LSTM layer is the need to process sequences of time-related data