Disease Prediction using water quality dataset (ML)

Published on 16 Apr 2022. Written by Atharv Deshpande

There are several Machine Learning projects currently available. As it is a leading technology, there are many project ideas based on this field. It will be helpful for engineering students to complete their final year project. Disease prediction from water quality using machine learning is one of the best project ideas. Skyfi Labs will help you with the basic steps of implementing this project. 

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SLNOTE


Project Description

Water-related diseases are the biggest reason for diseases and deaths around the world. The diseases due to water quality are caused by water contaminated with viruses, bacteria, metals, toxins and other chemical contaminants. Hence we need continuous monitoring of water quality and gaining insights from collected data to estimate unsolicited events like a disease outbreak. Currently, there is no system which can predict diseases on the basis of water quality. Therefore we require a water quality monitoring system which can also analyze the water quality data, estimate probable disease spread and raise alerts on occurrence of such events. There are some milestones for the project implementation.

• Collection of data
• Save that data on the cloud
• Use machine learning algorithm for Training and Testing
• Raise an alert on a mobile device.

SLLATEST
Technologies Used

• Cloud Computing
• Machine Learning
• IoT devices(if possible)

Project Implementation

1. The first step is to collect water-related data with the help of sensors. Or if you don’t have experimental sensors then you can find pre-collected data also which contains attributes like temperature, TDS, Turbidity, pH and conductivity. The data is easily available on many websites.
2. After that collected data should be stored on the cloud. This is only for the monitoring and storing of the data. You can use a cloud service for the storage of the data.
3. If you want to improve the project, you can display water quality data by using P10 dot matrix display.
4. Then you have to train and test the data, for that you need an accurate dataset. Water quality-related dataset can easily be available on any health-related website like WHO or west Bengal pollution control board or you can simply find it on Kaggle.
5. For training and testing of the data, you can use a gradient boosting classifier machine learning algorithm.
6. Use a spyder scientific environment which gives features like advanced editing and debugging.
7. Scikit-learn is also known as learn which is a machine learning library developed in python language. It is used for classification, Support vector machine, regression, gradient boosting classifier and many more algorithms.
8. The algorithm helps for the prediction of the disease.
9. The next step is to give an alert to nearby government offices or any water purifying plant.
10. You can use push bullet application which is the easiest way to transfer data from mobile to pc and vice versa. Just you need to log in with the same Google account. You can install this app as many as you want to send the notification. 
11. If water quality is a danger for health then it will automatically give the notification to the devices.

SLDYK
Kit required to develop Disease Prediction using water quality dataset (ML):

• Python

Technologies you will learn by working on Disease Prediction using water quality dataset (ML):

• IoT
• Machine Learning
• Cloud Computing

Any Questions?