Читать книгу Machine Learning Algorithms and Applications - Группа авторов - Страница 23

In terms of control flow, the working of our model can be explained with respect to training model and testing model:

 1. Training Model: As the first step of the training model, the data is fetched from the OpenAQ Open Data Community and is pre-processed to remove any kind of noise from the data. The cleaned world data is passed for K-means clustering. Before setting the number of clusters required to classify the data we measured Silhouette coefficient to determine the optimal number of clusters required. On the second hand, the cleaned single place data is passed to the LSTM for different places. The output of the world data clustering and LSTM training of single place data is passed to measure the performance using MAE and RMSE values. Also, the world data after clustering is assigned labels using the AQI table. The labeled data is then split into testing data and training data. SVM training is done with values of parameters as input and air quality as output. At the end, 10-fold cross-validations were done and performances were measured using confusion matrix, precision and recall parameters.

 2. Testing Model: Under testing, new data was fetched using API. It was passed to the respective places LSTM. Future values of all parameters were predicted by the LSTM. This was passed as input to the SVM and the final result was prediction of air quality and assignment of AQI was done.