Credit-Card-Fraud-Detection

Machine learning models to automatically predict credit card frauds

Dataset

The dataset for this project was obtained from kaggle website.
[https://www.kaggle.com/mlg-ulb/creditcardfraud]

Requirements

• Python 3.6
• Pandas and Numpy
• Keras 2.1.6
• TensorFlow 1.8
• Sklearn Library 0.19.1
• Python's Matplotlib (2.2.2) for Visualization

Exploratory Data Analysis

   1. Checking the target class. (0 - No Fraud  1 - Fraud).

   2. Checking Time of Transaction vs Amount of Transaction for each class ((0 - No Fraud  1 - Fraud)

   3.  Amount per Transaction for each class (0 - No Fraud  1 - Fraud)

   4. Correlation Matrix between different features of the dataset

   3. Describing the dataset 

Data Preprocessing

1. Feature Elimination
2. Data Normalization
3. Balancing the skewed dataset using
   3.1 Undersampling technique
   3.2 Oversampling using SMOTE technique

Machine Learning Models

Used Sklearn, TensorFlow and Keras libraries to built the following models in Spyder IDE.

1. Autoencoder Artificial Neural Networks
2. Random Forest
3. Logistic Rregression

Performace Evaluation

1. 5 fold cross Validation
2. Confusion Matrix
3. Precision - Recall Curves
4. Cohen's Kappa Statistic
5. AUC - ROC curves

Results

1. Autoencoders - Artificial Neural Network

   1.1 Model Loss for Autoencoders  -  Plot of Loss vs Epoch of training and testing data

Epoch = 88 , Batch_size = 32

   1.2 Error Distribution of Autoencoders for each class (0 - No Fraud  1 - Fraud).

              reconstruction error with no fraud         reconstruction error with fraud

  1.3 AUC - ROC  and Confusion Matrix for Autoencoders Neural Nets

  1.3  Precision- Recall Curves for Autoencoders Neural Nets

2. Random Forest

     2.1  AUC - ROC for Random Forest

   2.2  Precision- Recall Curves for Random Forest

   2.3 Confusion Matrix for Random Forest

3. Logistic Rregression

     3.1  AUC - ROC for Logistic Rregression

     3.2  Precision- Recall Curves for Logistic Rregression

       3.3  Confusion Matrix for Logistic Rregression