Application_Fraud_Detection

Application Fraud Detection Project

Overview: Applied complet CRISP-DM process in this fraud detection project on financial product applicaiton data. For executive summary and each stage of the work please read the final report pdf.

Table of Content:

1. Data:

• raw data: 'applications data.csv'
• train test data: 'train_test.csv'
• out of time validation data: 'oot.csv'

2.DQR:

• a simple data quality report for the raw data

3.Candidate Variables:

• feature enginnering process on creating expert variables
• two version of notebooks (3200 variables & 400 variables)
• did not include working dataset due to file sizes but dataset can be obtained from running the notebook

4. Feature Selection

• top 80 candidate variables selection
• applied ks score and fraud detection rate (fdr) as measure of goodness
• two version of notebooks (2700 variables & 400 variables)

5. Final Features

• finalized 30 variables for model building
• applied multiple classification model algorithms to obtain initial performance results
• applied hyperparameter tuning on the top performance model (gradient boosting model)
• executed ensemble stacked modeling

6. Result Reports

• fine tuned final gradient boosting model and obtained training results
• obtained final training, testing, validation data results