1. Credit card Fraud
detection using Machine
Learning With Python.
Guide : Dr Selvi Ravindran,
Assistant Professor, DIST
Members : A. Hazina Begam – 2021178023
P. Jeyadarshini - 2021178025
2. PROBLEM STATEMENT
Now a day’s online transactions have become an important and necessary part
of our lives.
It is vital that credit card companies are able to identify fraudulent credit
card transactions so that customers are not charged for items that they did
not purchase.
As frequency of transactions is increasing, number of fraudulent transactions
are also increasing rapidly.
This project intends to illustrate the modelling of a data set using machine
learning with Credit Card Fraud Detection.
This model is then used to recognize whether a new transaction is fraudulent
or not.
3. LITERATURE SURVEY
AUTHOR MECHANISM TECHNIQUES ACCURACY
S.P. Maniraj They Describe about
the algorithm
applicable on Find
Fraud Detection
Two types of Random
Forest Algorithm 91.96 % and
96.77%
respectively
Suman Arora Many supervised
Algorithms apply on
70%on training and
30% on dataset
Random forest 94.59%
95.27%
93.24%
90.87%
90.54%
94.25%
Stacking classifier
XGB classifier
SVM
Decision tree
KNN algorithms
4. OBJECTIVES
The Objective of the project is to predict the fraud and fraud less
transaction with respect to the time and amount of the transaction
using classification machine learning algorithms and statistics and
calculus.
To implement efficient fraud detection algorithms using machine-
learning techniques.
To detect 100% of the fraudulent transactions while minimizing the
incorrect fraud classifications.
