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This GitHub repository houses a water treatment plant classification project. Leveraging Python and machine techniques, it categorizes the plant's operation state using sensor data. The project covers data preprocessing, EDA, model training, and hyperparameter tuning, highlighting expertise in data science, ML, and data visualization.

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MahanPourhosseini/Water-Treatment-Plant

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Problem Statement

This problem comes from the daily measures of sensors in an urban waste water treatment plant. The objective is to classify the operation state of the plant at each of the stages of the treatment process. The plant is constituted by a primary settler, a biological reactor, and a secondary settler. After the biological reactor, where the level of substrate is reduced by the action of microorganisms, the water flows to the secondary settler where the biomass sludge settles. Clean water hence remains at the top of the settler and can be easily carried out of the plant. A portion of the sludge is returned to the bioreactor’s input to maintain an appropriate level of biomass, allowing the oxidation of organic matter, while the rest of the sludge is purged.

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Attributes

Water treatment plant dataset has 38 attributes. All atrributes are numeric and continuous:

  1. Q-E (input flow to plant)
  2. ZN-E (input Zinc to plant)
  3. PH-E (input pH to plant)
  4. DBO-E (input Biological demand of oxygen to plant)
  5. DQO-E (input chemical demand of oxygen to plant)
  6. SS-E (input suspended solids to plant)
  7. SSV-E (input volatile supended solids to plant)
  8. SED-E (input sediments to plant)
  9. COND-E (input conductivity to plant)
  10. PH-P (input pH to primary settler)
  11. DBO-P (input Biological demand of oxygen to primary settler)
  12. SS-P (input suspended solids to primary settler)
  13. SSV-P (input volatile supended solids to primary settler)
  14. SED-P (input sediments to primary settler)
  15. COND-P (input conductivity to primary settler)
  16. PH-D (input pH to secondary settler)
  17. DBO-D (input Biological demand of oxygen to secondary settler)
  18. DQO-D (input chemical demand of oxygen to secondary settler)
  19. SS-D (input suspended solids to secondary settler)
  20. SSV-D (input volatile supended solids to secondary settler)
  21. SED-D (input sediments to secondary settler)
  22. COND-D (input conductivity to secondary settler)
  23. PH-S (output pH)
  24. DBO-S (output Biological demand of oxygen)
  25. DQO-S (output chemical demand of oxygen)
  26. SS-S (output suspended solids)
  27. SSV-S (output volatile supended solids)
  28. SED-S (output sediments)
  29. COND-S (output conductivity)
  30. RD-DBO-P (performance input Biological demand of oxygen in primary settler)
  31. RD-SS-P (performance input suspended solids to primary settler)
  32. RD-SED-P (performance input sediments to primary settler)
  33. RD-DBO-S (performance input Biological demand of oxygen to secondary settler)
  34. RD-DQO-S (performance input chemical demand of oxygen to secondary settler)
  35. RD-DBO-G (global performance input Biological demand of oxygen)
  36. RD-DQO-G (global performance input chemical demand of oxygen)
  37. RD-SS-G (global performance input suspended solids)
  38. RD-SED-G (global performance input sediments)

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This GitHub repository houses a water treatment plant classification project. Leveraging Python and machine techniques, it categorizes the plant's operation state using sensor data. The project covers data preprocessing, EDA, model training, and hyperparameter tuning, highlighting expertise in data science, ML, and data visualization.

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