• Vibhooti jain
• Abhishek Dwivedi

Due to the theatrical increase of fraud which results in loss of dollars worldwide each year, several modern techniques in detecting fraud are persistently evolved and applied to many business fields. Fraud detection involves monitoring the activities of populations of users in order to estimate, perceive or avoid undesirable behaviour. Undesirable behaviour is a broad term including delinquency, fraud, intrusion, and account defaulting. This paper we proposed an classification techniques which takes dataset of credit card transactions containing 284,807 transactions records and these techniques has capability to process the numerical and categorical datasets. In these we can take decision tree and random forest classifier and the classification result of these model is then compared the performance measure of these models .

data mining, random forest, decision tree, machine learning, classification, comparative analysis, credit card fraud

"Credit Card Fraud Detection in Transaction By Using Decision Tree and Random Forest Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.332-336, May-2019, Available :http://www.jetir.org/papers/JETIR1905I49.pdf

"Credit Card Fraud Detection in Transaction By Using Decision Tree and Random Forest Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp332-336, May-2019, Available at : http://www.jetir.org/papers/JETIR1905I49.pdf