Machine Learning Approach for Credit Card Fraud Detection (KNN & Naïve Bayes)

2020 ◽  
Author(s):  
Darshan Kaur ◽  
Shubhpreet kaur
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Naive Bayes ◽  
Fraud Detection ◽  
Naïve Bayes ◽  
Learning Approach ◽  
Credit Card Fraud ◽  
Machine Learning Approach

scholarly journals A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data

2015 ◽  
Vol 34 (21) ◽  
pp. 2941-2957 ◽  
Author(s):  
Julian Wolfson ◽  
Sunayan Bandyopadhyay ◽  
Mohamed Elidrisi ◽  
Gabriela Vazquez-Benitez ◽  
David M. Vock ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Learning Approach ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Machine Learning Approach

scholarly journals Sentiment Analysis on Covid-19 Vaccination Tweets using Naïve Bayes and LSTM

2021 ◽  
Vol 1 (1) ◽  
pp. 57-70
Author(s):  
Ranjan Raj Aryal ◽  
Ankit Bhattarai
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The Other ◽  
Learning Approach ◽  
Asian Countries ◽  
Vaccination Programs ◽  
Machine Learning Approach ◽  
Positive Sentiment

Social media is one platform where people share their opinions and views on different topics, services, or behaviors that happen around them. Since the COVID19 pandemic that started at the end of 2019, it has been a topic on which people express their sentiments. Recently, the COVID19 vaccination programs have got a lot of responses. In this paper, we have proposed two models: one based on the machine learning approach: Naive Bayes & the other based on deep learning: LSTM, whose goal is to know the sentiment of Asian region tweets towards the vaccine through sentiment analysis. The data were extracted with the help of Twitter API from March 23, 2021, till April 2, 2021. The extraction approach contains keywords with geocoding of some of the Asian countries, especially Nepal, India and Singapore. After collecting data, some preprocessing such as removing numbers, non-English & stop words, removing special characters, and hyperlinks were done. The polarity of tweets was assigned using the Text blob library. The tweets were classified into one of the three: positive, negative, or neutral. Now the data were preprocessed with the splitting of tweets into training & testing sets. Both the models were trained & tested using 10767 unique tweets. This experiment shows that a number of people in these three countries (Nepal, India and Singapore) have positive sentiment towards the vaccine and are taking the first dose of Covid19 vaccine. At last, the accuracy of the LSTM model was found to be 7% greater than that of the Naive Bayes-based model.

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