Tool used and programming language
i] Anaconda ii] Jupyter Notebook iii] Python3
#Problem-Statement
Blossom Bank also known as BB PLC is a multinational financial services group, that offers retail and investment banking, pension management, asset management and payments services, headquartered in London, UK.
Customers are vulnerable to transaction fraud everyday. On this basis, the bank is in a dire need for a fraud detection model to help predict transaction crime. A constant reoccurrence of fraudulent incident will diminish the bank customer’s trust and affect the productivity of the bank. Hence, detecting and preventing fraud is important to the Bank.
I cleaned, checked out for missing values and performed exploratory data analysis using python to: a) Visualize relationships between the label and some key features b) Explore correlations c) Conduct univariate and multivariate analysis.
I also performed feature engineering by: a) Encoding categorical variables b) Created new features from existing features where necessary, depending on insights from Exploratory Data Analysis
I modelled the selection, training, and validation of data, trained and tested at least 2 supervised learning model which includes: Decision Tree and Linear Regression model
Data Dictionary
The below column reference:
• step: represents a unit of time where 1 step equals 1 hour
• type: type of online transaction
• amount: the amount of the transaction
• nameOrig: customer starting the transaction
• oldbalanceOrg: balance before the transaction
• newbalanceOrig: balance after the transaction
• nameDest: recipient of the transaction
• oldbalanceDest: initial balance of recipient before the transaction
• newbalanceDest: the new balance of the recipient after the transaction
• isFraud: fraud transaction