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Jena, Ajay Kumar. Analyzing the potability of water using machine learning algorithm. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2022.
Analyzing the potability of water using machine learning algorithm
Name: Arora, Aakarsh
Organization :
KIIT. School of Computer Engineering
Email :
aakarshverma330@gmail.com
Organization :
KIIT. School of Computer Engineering
Email :
mkgourisaria2010@gmail.com
Name: Singh, Vinayak
Organization :
KIIT. School of Computer Engineering
Email :
vinayaksooryavanshi@gmail.com
Name: Jena, Ajay Kumar
Organization :
KIIT. School of Computer Engineering
Email :
ajay.bbs.in@gmail.com
LCSH:
Water quality
LCSH:
Machine learning
LCSH:
Predictive analytics
Abstract:
Human life cannot exist without water, as most
individuals are taught at a young age. According to the World
Health Organization (WHO), clean water is not readily available
for one out of every six people on the planet, which is around l
billion people. The potability of water is affected by many factors
like pH, Hardness, and various other chemnicals present in the
water. Contamination in drinking water can cause severe diseases
like Hepatitis A, Cholera, Dysentery, Typhoid, and Polio. The
paper primarily focuses on the binary classification of water
potability using various standard machine learning algorithms
like K-Nearest Neighbors r's (KVN), XG-Boost, Decision Tree,
Support Vector Machine (SVM), Logistic Regression, Gaussian
Naive Bayes, Random Forest, LightGBM, Bernoulli Naive Bayes
and Gradient Boost on selected features from the dataset by using
correlation matrix and in terms of performance, Decision Tree
outperformed all other machine learning algorithmns by achieving
an accuracy of 0.9358, F1 score of 0.9374 and AUC-ROC score of
0.9220 for classification of water potability.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2022
Modified:
2024-05-15
Issued:
2024-05-15
บทความ/Article
application/pdf
BibliograpyCitation :
In IEEE Computer Society. 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT 2022) (pp.250-256). Los Alamitos, CA : IEEE Computer Society
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
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0.03137 วินาที
Arora, Aakarsh
| Title | Contributor | Type |
|---|---|---|
| Analyzing the potability of water using machine learning algorithm
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Arora, Aakarsh;Gourisaria, Mahendra Kumar;Singh, Vinayak;Jena, Ajay Kumar | บทความ/Article |
Gourisaria, Mahendra Kumar
| Title | Contributor | Type |
|---|---|---|
| Analyzing the potability of water using machine learning algorithm
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Arora, Aakarsh;Gourisaria, Mahendra Kumar;Singh, Vinayak;Jena, Ajay Kumar | บทความ/Article | |
| Spam email assessment using machine learning and data mining approach
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Singh, Utlrisht;Singh, Vinayak;Gourisaria, Mahendra Kumar;Das, Himansu | บทความ/Article |
Singh, Vinayak
| Title | Contributor | Type |
|---|---|---|
| Analyzing the potability of water using machine learning algorithm
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Arora, Aakarsh;Gourisaria, Mahendra Kumar;Singh, Vinayak;Jena, Ajay Kumar | บทความ/Article | |
| Spam email assessment using machine learning and data mining approach
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Singh, Utlrisht;Singh, Vinayak;Gourisaria, Mahendra Kumar;Das, Himansu | บทความ/Article |
Jena, Ajay Kumar
| Title | Contributor | Type |
|---|---|---|
| Analyzing the potability of water using machine learning algorithm
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Arora, Aakarsh;Gourisaria, Mahendra Kumar;Singh, Vinayak;Jena, Ajay Kumar | บทความ/Article |