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VIT HACK-A-THON: Applications of AI/ML in Environmental Sustainability

by kandikits Updated: Dec 23, 2022


This starter kit presents a very simple solution based on AI/ML that helps tackle the problem of water pollution caused due to poor quality detergents. This kit uses the 'IoT Sensor' dataset which has information on Detergent Quality - 'Good' or 'Bad', based on certain detergent manufacturing parameters. The solution uses the Supervised Machine Learning technique to predict whether the detergent is of good or bad quality thereby resulting in water pollution. This solution can be deployed for enhancing the footprint of detergents on Aquatic Ecosystems.


Here is how you get started!

  1. Identify your specific problem and your proposed use case/solution.
  2. Install this starter kit and customize it to suit your solution.
  3. Answer the questions on the submission form with a short video/screen recording/presentation explaining your solution.


For your HACK-A-THON, you may utilize this kit in the following ways:

  1. ‘BO5341_IoTData.csv’ is the dataset used to train the model. Replace it with the dataset of your choice.
  2. There is a ‘sample_predict.csv’ dataset wherein you can test this model with a similar dataset as ‘BO5341_IoTData.csv’.
  3. Checkout the output file ‘sample_predict_output.csv’ to see the results of the sample dataset you entered in ‘sample_predict.csv’
  4. ‘Logistic RegressionCV’ is used to train the model and fetch a training accuracy of 98%, and testing accuracy of 73%. You may improve the accuracy of the model using other algorithms as a submission
  5. You may use Matplotlib & Seaborn to plot other features to showcase your results apart from the confusion matrix used to analyze the result.

Deployment Information

Follow the below instructions to run the solution. This is our source code updated in GitHub for our VIT hackathon entry. The below repo is an example. Please replace with your source code from GitHub.


For Windows OS, Download, extract and double-click the kit installer file to install the kit. Note: Do ensure to extract the zip file before running it. The installation may take from 2 to 10 minutes based on bandwidth.

  1. When you're prompted during the installation of the kit, press Y to launch the app automatically and execute cells in the notebook by selecting Cell --> Run All from Menu bar
  2. To run the app manually, press N when you're prompted and locate the zip file 'environmental-sustainability-through-ml-main.zip'
  3. Extract the zip file and navigate to the directory 'environmental-sustainability-through-ml-main'
  4. Open command prompt in the extracted directory 'environmental-sustainability-through-ml-main' and run the command 'jupyter notebook'

For other Operating System,

  1. Click here to install python
  2. Click here to download the repository
  3. Extract the zip file and navigate to the directory 'environmental-sustainability-through-ml-main'
  4. Open terminal in the extracted directory 'environmental-sustainability-through-ml-main'
  5. Install dependencies by executing the command 'pip install -r requirements.txt'
  6. Run the command ‘jupyter notebook’ and select the notebook ‘Enironmental_Sustainability_IoTdata.ipynb’ on the browser window.

Instructions to Run

  1. Locate and open the Enironmental_Sustainability_IoTdata.ipynb notebook from the Jupyter Notebook browser window.
  2. Execute cells in the notebook by selecting Cell --> Run All from Menu bar You can execute the cells of notebook by selecting Cell from the menu bar.


For any support, you can reach us at FAQ & Support

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Group Name 1

notebookby jupyter

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Jupyter Interactive Notebook

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vscodeby microsoft

TypeScript star image 142167 Version:1.75.0

License: Permissive (MIT)

Visual Studio Code

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vscodeby microsoft

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Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

numpyby numpy

Python star image 22606 Version:1.24.1

License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.

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numpyby numpy

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The fundamental package for scientific computing with Python.
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pandasby pandas-dev

Python star image 36769 Version:1.5.2

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Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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pandasby pandas-dev

Python star image 36769 Version:1.5.2 License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning.

scikit-learnby scikit-learn

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scikit-learn: machine learning in Python

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scikit-learn: machine learning in Python
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Data Visualization

The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.

matplotlibby matplotlib

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matplotlib: plotting with Python

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matplotlibby matplotlib

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Troubleshooting

  1. If you encounter any error related to MS Visual C++, please install MS Visual Build tools
  2. While running batch file, if you encounter Windows protection alert, select More info --> Run anyway.
  3. During kit installer, if you encounter Windows security alert, click Allow.
  4. If you encounter Memory Error, check if the available memory is sufficient and it is proportion to the size of the data being used. For our dataset, the minimum required memory is 8GB.

If your computer doesn't support standard commands from windows 10, you can follow the instructions below to finish the kit installation.

  1. Click here to install python
  2. Click here to download the repository
  3. Extract the zip file and navigate to the directory 'energy-sustainability-through-ml-main'
  4. Open terminal in the extracted directory 'energy-sustainability-through-ml-main'
  5. Install dependencies by executing the command 'pip install -r requirements.txt'
  6. Run the command ‘jupyter notebook’ and select the notebook ‘Energy_Sustainability_IoTdata.ipynb’ on the browser window.