Credit-Card-Fraud-Analysis

In this kaggle project , we are presented with an imbalanced data and are asked to build a classification model to mark fraudulent transactions.

Table of Content Introduction

Preliminary Examination
    1. Performance Metric
        Receiver Operating Characteristic (ROC)
    2. Resampling Dataset
    3. Synthetic Samples
    4. Cross Validation
    5. Customized Models
        1. Logistic Regression
        2. Decision Tree
        3. Random Forest
        4. Support Vector Machine

Exploratory Analysis
    Resampling
    Resampling summary
Modeling
    1. Logistic Regression
    2. Decision Tree
    3. Random Forest
    4. Support Vector Machine
    Best Performing Models
    Parameter Tuning of Random Forest

Conclusion

KeyWords: matplotlib, pandas, sklearn, data science, classification, resampling