Drinking water these days has to be monitored as it may contain several life-threating diseases and can also be affected by pollution. Therefore, it is highly required to make sure that there isn’t any water intrusion and to predict the levels of pollution beforehand. In this project, we used 3 Machine learning algorithms, namely, Decision Tree, Support Vector Machine, and KNN, to analyse and detect the presence of intrusion from the water dataset, which was obtained from a treatment plant for water located in Tunisian.
subhakar24/Decision-Tree-and-Support-Vector-Machine-for-Anomaly-Detection-in-Water-Distribution-Networks
Drinking water these days has to be monitored as it may contain several life-threating diseases and can also be affected by pollution. Therefore, it is highly required to make sure that there isn’t any water intrusion and to predict the levels of pollution beforehand. In this project, we used 3 Machine learning algorithms, namely, Decision Tree, Support Vector Machine, and KNN, to analyse and detect the presence of intrusion from the water dataset, which was obtained from a treatment plant for water located in Tunisian.
Python