ankit-v2-1/water-quality-ml

🤖 Comparison of various machine learning classifier algorithms for water quality data.

Water quality ML

Comparison of various classifier for water Quality Data!
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About The Project

The problem statement is, we have given data of quality of water and factors responsible for that and we have to classify the data using classifier algorithms and then compare the data of result obtained in each algorithm.

Classifiers in Machine Learning: - A classifier in machine learning is an algorithm that automatically orders or categorizes data into one or more of a set of “classes.”

Different types of classifiers we have used:

1. Linear Regression
2. Logistic Regression
3. K Nearest Neighbor (KNN)
4. Decision tree
5. Naive Bayes
6. Support Vector Machine (SVM)

Data Description: - Dataset consists of 16 independent references and one output reference. Ref1 and Ref2 doesn't have any impact on water quality and also since ref 'CARBOHYDRATE' has zero value for all it doesn't affect the quality. We have water quality data of 88 different places.

Built With

• Python
• Jupyter Notebook
• Scikit-learn

Getting Started

To get a local copy up and running follow these simple steps.

Dependencies

This is an example of how to list things you need to use the software and how to install them.

• Scikit-learn
  
• Pandas

 pip install scikit-learn
 pip install pandas

Executing program

1. Clone the repo

   git clone https://github.com/ankit-v2-1/Water-quality-ML.git

Usage

For more examples, please refer to the Documentation

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgements

• Prof. M. Venkatesan
• G Victor Daniel