https://github.com/waasiq/water-quality-anomaly-detection

ML Algorithms to detect water anomalies in water quality test process.
https://github.com/waasiq/water-quality-anomaly-detection

anomaly-detection machine-learning python random-forest regression

Last synced: about 5 hours ago
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ML Algorithms to detect water anomalies in water quality test process.

• Host: GitHub
• URL: https://github.com/waasiq/water-quality-anomaly-detection
• Owner: waasiq
• Created: 2022-01-26T20:21:59.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-04-17T15:02:37.000Z (over 2 years ago)
• Last Synced: 2024-05-02T06:00:07.134Z (7 months ago)
• Topics: anomaly-detection, machine-learning, python, random-forest, regression
• Language: Python
• Homepage:
• Size: 4.7 MB
• Stars: 1
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Water Quality Anomaly Detection

Implementation of Machine Learning Algorithm to detect water anomalies for water quality test process.

### Background:

Anomaly detection is a very specific branch of machine learning, the algortihms presented here were implemented for internship in Center SAU. OPC data was taken to local computer

using a C# code.

### Dataset:

Dataset for the values could not have been found on Kaggle or other sources. Due to the sensors not being setup custom dataset was coded which can be found in the data folder.

### Algorithms:

The following algorithms have been tested for the anomaly detection. They have been ranked from best anomaly finding results to the worst:



    
Isolation Forest

    
Random Forest

    
Logistic Regression

    
Local Outliner Factor



### Project Contributors



    
Waasiq Masood

    
Yusuf Özaslan



### Future plan:



    
Add a visualization for the project