Water is an important element that is related to the human being because drinking water is a necessary element for health, also drinking water is considered as an element that also participates in the economy of a society, since it has a defined and industrialized process. Due to the presence of drinking water in different aspects of society, it is important to carry out research that contributes to this topic. The present research work is focused on a predictive analysis using a neural network model, which will allow us to predict and detect whether a given body of water is suitable for human consumption. The proposed model is based on an architecture that uses neural networks that was developed in the Python language, and a dataset obtained from the Kaggle web page was also used. This data set was used for training and validation. Within the preprocessing, the MinMax scaling method obtained from the Sklearn library was used. For the development of the model, the Keras library was used, which provided the necessary methods for the implementation of the seven dense layers that make up the neural network. At the end of the development, a model with an accuracy of approximately 70% was obtained. Finally, we invite for future research, to consider new architectures based on neural networks or other models based on other machine learning classification algorithms.
|Title of host publication||Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - 22 Sep 2021|
|Event||13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021 - Lima, Peru|
Duration: 22 Sep 2021 → 23 Sep 2021
|Name||Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021|
|Conference||13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021|
|Period||22/09/21 → 23/09/21|
Bibliographical notePublisher Copyright:
© 2021 IEEE.
- human consumption
- neural networks