Access to clean water is a critical challenge in many parts of the world, including Rwanda. Water quality prediction is important for ensuring the availability of safe and clean water for drinking, agriculture, and other purposes. However, traditional methods for water quality prediction are often time-consuming and costly, and they may not provide accurate and timely information. To address this challenge, the Omdena Rwanda Chapter has initiated a project to develop an automated water quality prediction system using machine learning.
In this project, the Omdena Rwanda Chapter’s primary goal in this project is to develop an accurate and efficient machine learning model that can predict water quality based on a range of parameters such as Electrical conductivity of water, Amount of organic carbon in ppm, Amount of Trihalomethanes in μg/L, and turbidity. The model will be trained on a large dataset of historical water quality data and will be designed to provide predictions for water quality.
- Have a Look at the project structure and folder overview below to understand where to store/upload your contribution
- If you're creating a task, Go to the task folder and create a new folder with the below naming convention and add a README.md with task details and goals to help other contributors understand
- Task Folder Naming Convention : task-n-taskname.(n is the task number) ex: task-1-data-analysis, task-2-model-deployment etc.
- Create a README.md with a table containing information table about all contributions for the task.
- If you're contributing for a task, please make sure to store in relavant location and update the README.md information table with your contribution details.
- Make sure your File names(jupyter notebooks, python files, data sheet file names etc) has proper naming to help others in easily identifing them.
- Please restrict yourself from creating unnessesary folders other than in 'tasks' folder (as above mentioned naming convention) to avoid confusion.
├── LICENSE
├── README.md <- The top-level README for developers/collaborators using this project.
├── original <- Original Source Code of the challenge hosted by omdena. Can be used as a reference code for the current project goal.
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├── reports <- Folder containing the final reports/results of this project
│ └── README.md <- Details about final reports and analysis
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├── src <- Source code folder for this project
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├── data <- Datasets used and collected for this project
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├── docs <- Folder for Task documentations, Meeting Presentations and task Workflow Documents and Diagrams.
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├── references <- Data dictionaries, manuals, and all other explanatory references used
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├── tasks <- Master folder for all individual task folders
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├── visualizations <- Code and Visualization dashboards generated for the project
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└── results <- Folder to store Final analysis and modelling results and code.
- Original - Folder Containing old/completed Omdena challenge code.
- Reports - Folder to store all Final Reports of this project
- Data - Folder to Store all the data collected and used for this project
- Docs - Folder for Task documentations, Meeting Presentations and task Workflow Documents and Diagrams.
- References - Folder to store any referneced code/research papers and other useful documents used for this project
- Tasks - Master folder for all tasks
- All Task Folder names should follow specific naming convention
- All Task folder names should be in chronologial order (from 1 to n)
- All Task folders should have a README.md file with task Details and task goals along with an info table containing all code/notebook files with their links and information
- Update the task-table whenever a task is created and explain the purpose and goals of the task to others.
- Visualization - Folder to store dashboards, analysis and visualization reports
- Results - Folder to store final analysis modelling results for the project.