Group project - E-Commerce_fraud_Detections

Exploratory Data Analysis, Correlations, Data Structures and distributions, Hypothesis Testing, Pattern Findings

Project members: Roshni, Ria, Muneeb, Mahalel, Ricardo

The topic selected by our Project team 8 is surrounding fraudulent e-commerce transactions With two data sets merged, we filtered out the legitimate transactions to analyze the fraud transactions among a vast variety of categories: Age, transaction amount, payment method etc.

dataset link https://drive.google.com/drive/folders/1ccKb0ESci_SzjoR8hq1Q32lmTw7k3iO1?usp=sharing
presentation link https://docs.google.com/presentation/d/1t24rhCi3UyfUCSmVbIffjm-Nd1amloydEnNq3K1J7rM/edit?usp=sharing

Columns Head

Exploratory Analysis, Findings, Patterns, Corelations And hypothesis Testing

We have provided visualizations which include:

- Fraud vs Regular transactions

- Number of customers by product category and age group

- Fraud Transactions vs Account Age Days and hour of the day

- Tree map for hour of day the fraud transactions took place

- box plots for fraud and non fraud transactions

• bar chart for number of customers by product category and age group
• plots outlining payment methods used in fraud transactions

Device Used

Statistics

Hypothesis Testing For Device Used "Mobile Category"

Please note that our notebook includes other various visuals we considered in our analysis which did not make the final presentation