Credit-Card-Fraud-Detection-using-ML-models

This dataset is used to detect the credit card fraud detection. This is a classification problem. This is an imbalanced dataset based on target variable. So In this Project, I will use encoding and decoding techniques to balanced dataset. These are various techniques as follows -

• Logistics Regression
• Random Forest
• Decision Tree
• NaiveBias
• KNN

About Dataset¶

Digital payments are evolving, but so are cyber criminals.

According to the Data Breach Index, more than 5 million records are being stolen on a daily basis, a concerning statistic that shows - fraud is still very common both for Card-Present and Card-not Present type of payments.

In today’s digital world where trillions of Card transaction happens per day, detection of fraud is challenging. This Dataset sourced by some unnamed institute.

Feature Explanation:

distance_from_home - the distance from home where the transaction happened.

distance_from_last_transaction - the distance from last transaction happened.

ratio_to_median_purchase_price - Ratio of purchased price transaction to median purchase price.

repeat_retailer - Is the transaction happened from same retailer.

used_chip - Is the transaction through chip (credit card).

used_pin_number - Is the transaction happened by using PIN number.

online_order - Is the transaction an online order.

fraud - Is the transaction fraudulent.

Ref :https://www.kaggle.com/datasets/dhanushnarayananr/credit-card-fraud

Project Pipeline:

The project pipeline can be briefly summarized in the following steps:

Data Understanding:

Here, we need to load the data and understand the features present in it. This would help us choose the features that we will need for your final model.

Exploratory data analytics (EDA):

Normally, in this step, we need to perform univariate and bivariate analyses of the data, followed by feature transformations, if necessary. For the current data set, because Gaussian variables are used, we do not need to perform Z-scaling. However, you can check if there is any skewness in the data and try to mitigate it, as it might cause problems during the model-building phase.

Train/Test Split:

Now we are familiar with the train/test split, which we can perform in order to check the performance of our models with unseen data. Here, for validation, we can use the k-fold cross-validation method. We need to choose an appropriate k value so that the minority class is correctly represented in the test folds.

Model-Building/Hyperparameter Tuning:

This is the final step at which we can try different models and fine-tune their hyperparameters until we get the desired level of performance on the given dataset. We should try and see if we get a better model by the various sampling techniques.

Model Evaluation:

We need to evaluate the models using appropriate evaluation metrics. Note that since the data is imbalanced it is is more important to identify which are fraudulent transactions accurately than the non-fraudulent. We need to choose an appropriate evaluation metric which reflects this business goal.

Guided by Shalini kumari

Submitted by Shalinee Kumari