It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Here you will find the easiest method for detection of fraud credit card.
The number of credit card owners is projected close to 1.2 billion by 2022. To ensure security of credit card transactions, it is essential to monitor fradualent activities. Credit card companies shall be able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. A credit card dataset contains a mix of fraud as well as non-fraudulent transactions and the target is to predict if a given test transaction is fraudulent or not. Algorithms to be used: Since the target variable is categorical, the problem can be solved with a line of machine learning algorithms as — Logistic regression Decision trees Neural network or so.
I have use the data from kaggle (link is here) -> https://www.kaggle.com/isaikumar/creditcardfraud
Credit Card Fraud Detection using Logistic Regression.Throughout the financial sector, machine learning algorithms are being devel- oped to detect fraudulent transactions. I have build and deployed the following machine learning algorithms Logistic Regression us- ing the Confusion Metrics and Sea born. Furthermore, using met- rics we will investigate why the classification accuracy for these algorithms can be misleading.
Python, Jupyter Notebook, Pandas, Numpy, Sea Born, Machine Learning (Logistic Regression & Decision Trees)