Data Science: Credit Card Fraud Detection – Model Building

In this course I will cover, how to develop a Credit Card Fraud Detection model to categorize a transaction as Fraud or Legitimate with very high accuracy using different Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model.

This course will walk you through the initial data exploration and understanding, data analysis, data preparation, model building and evaluation. We will explore RepeatedKFold, StratifiedKFold, Random Oversampler, SMOTE, ADASYN concepts and then use multiple ML algorithms to create our model and finally focus into one which performs the best on the given dataset.

I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.

Task 1  :  Installing Packages.

Task 2  :  Importing Libraries.

Task 3  :  Loading the data from source.

Task 4  :  Understanding the data

Task 5  :  Checking the class distribution of the target variable

Task 6  :  Finding correlation and plotting Heat Map

Task 7  :  Performing Feature engineering.

Task 8  :  Train Test Split

Task 9 :   Plotting the distribution of a variable

Task 10 :  About Confusion Matrix, Classification Report, AUC–ROC