Ramkrishna Kamble, Rishabh Gupta & Raghav Gupta
Credit Card Fraud Detection with Machine Learning is a process of data investigation and the development of a model that will provide the best results in revealing and preventing fraudulent transactions.
Goals for the project:-
1) Understanding the distribution of data that is provided to us.
2) Understand common mistaked made with imbalanced datasets.
3) Determine the Classifiers we are going to use and decide which one has a higher accuracy.
4) Create a Neural Network and compare the accuracy to our best classifier.
Simillar project -https://www.kaggle.com/code/janiobachmann/credit-fraud-dealing-with-imbalanced-datasets/notebook
Prerequisites: N/A. Knowledge of basic python is plus.
|Week 1-2||Reading up on Python and basic of Machine Learning|
|Week 3||Understanding Data, Precocessing, EDA|
|Week 4||Dealing with imbalanced data|
|Week 5||Testing Classifiers like logistic regression, Randomforest, SVM, Naive bayes, etc.|
|Week 6||Final Neural networks testing|