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https://github.com/sarita-joshi/credit-card-fraud-detection-spark


https://github.com/sarita-joshi/credit-card-fraud-detection-spark

apache-spark big-data cassandra fraud-detection hadoop kafka kafka-streams python spark streaming streamlit

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README 

        

# Credit Card Fraud Detection using Spark ML



Course - CPSC 531 Advanced Database Management



Developers -

- Sarita Joshi

- Hritika Phule



## Overview

This project demonstrates a robust implementation of a real-time credit card fraud detection system using Apache Spark ML, integrated with Apache Kafka, Zookeeper, and Cassandra for data streaming, processing, and storage, respectively. A live dashboard for reporting and analytics is created using Python Streamlit.



## Technologies Used

![Tech stack](https://github.com/Sarita-Joshi/Credit-Card-Fraud-Detection-Spark/blob/develop/pictures/tech_stack.png?raw=true)



Each component plays a crucial role in the system:

- **Data Generation**: Python Script

- **Data Streaming**: Apache Kafka + Zookeeper

- **Model Training**: PySpark 

- **Data Processing**: Spark and Python Script

- **Data Storage**: Cassandra

- **Data Reporting and Analytics**: Streamlit Python Live Dashboard



## Data



Dataset link - https://www.kaggle.com/datasets/kartik2112/fraud-detection



Dataset generator - https://github.com/namebrandon/Sparkov_Data_Generation/



The dataset was generated using Sparkov Data Generation | Github tool created by Brandon Harris.  

The simulator has certain pre-defined list of merchants, customers and transaction categories. And then using a python library called "faker", and with the number of customers, merchants that you mention during simulation, an intermediate list is created.



Datset size- 1GB



## Info



| #   | Column                 | Non-Null Count    | Dtype  |

|-----|------------------------|-------------------|--------|

| 0   | trans_date_trans_time  | 14814000 non-null | object |

| 1   | cc_num                 | 14814000 non-null | int64  |

| 2   | merchant               | 14814000 non-null | object |

| 3   | category               | 14814000 non-null | object |

| 4   | amt                    | 14814000 non-null | float64|

| 5   | first                  | 14814000 non-null | object |

| 6   | last                   | 14814000 non-null | object |

| 7   | gender                 | 14814000 non-null | object |

| 8   | street                 | 14814000 non-null | object |

| 9   | city                   | 14814000 non-null | object |

| 10  | state                  | 14814000 non-null | object |

| 11  | zip                    | 14814000 non-null | int64  |

| 12  | lat                    | 14814000 non-null | float64|

| 13  | long                   | 14814000 non-null | float64|

| 14  | city_pop               | 14814000 non-null | int64  |

| 15  | job                    | 14814000 non-null | object |

| 16  | dob                    | 14814000 non-null | object |

| 17  | trans_num              | 14814000 non-null | object |

| 18  | unix_time              | 14814000 non-null | int64  |

| 19  | merch_lat              | 14814000 non-null | float64|

| 20  | merch_long             | 14814000 non-null | float64|

| 21  | is_fraud               | 14814000 non-null | int64  |



## System Architecture

![System Architecture](https://github.com/Sarita-Joshi/Credit-Card-Fraud-Detection-Spark/blob/develop/pictures/arch.png?raw=true)



## Installation

Ensure all dependencies and required technologies are installed. Refer to official documentation for Apache Spark, Kafka, Cassandra, and Python for detailed installation instructions.



To get started with this project, you will need to set up your environment with Apache Spark, Apache Kafka, Zookeeper, and Cassandra. Below are detailed steps and links to resources for installing and configuring each component.



### Prerequisites

-   Java 8

-   Python 3.11



### Apache Spark



1.  **Download Apache Spark**:

    

    -   Download the latest version of Apache Spark from [Apache Spark Downloads](https://spark.apache.org/downloads.html).

    -   Choose a package type that supports Hadoop.

2.  **Installation**:

  

    -   Unzip the downloaded Spark package to a directory of your choice, e.g., `C:\spark`.

3.  **Environment Variables**:

    

    -   Add `C:\spark\bin` to the PATH environment variable.

    -   Set `SPARK_HOME` to `C:\spark`.



### Apache Kafka + Zookeeper



Apache Kafka requires Zookeeper for coordination.



1.  **Download Apache Kafka**:

    

    -   Kafka can be downloaded from [Kafka Downloads](https://kafka.apache.org/downloads). Select the binary files that include the Scala version you prefer.

2.  **Installation**:

    

    -   Extract the Kafka binaries to your preferred location, e.g., `C:\kafka`.

3.  **Configuration**:

    

    -   Edit the Kafka configuration to set up data directories. Detailed configuration steps can be found in the Kafka documentation.

4.  **Environment Variables**:

    

    -   Add `C:\kafka\bin` to the PATH environment variable.



### Cassandra



1.  **Download Apache Cassandra**:

    

    -   Download it from [Apache Cassandra Downloads](http://cassandra.apache.org/download/).

    -  Ensure that the installed version contains .bat files for windows.

2.  **Installation**:

    

    -   Extract Cassandra into a directory, e.g., `C:\cassandra`.

3.  **Environment Setup**:

    

    -   Set `CASSANDRA_HOME` to `C:\cassandra`.

    -   Add `C:\cassandra\bin` to the PATH environment variable.



## Workflow

The development and implementation of this project follow these steps:



### 1. Start environment 

Start zookeeper, kafka and cassandra using below commands.



- Start zookeeper

`C:/kafka/kafka_2.12-3.7.0/bin/windows/zookeeper-server-start.bat C:/kafka/kafka_2.12-3.7.0/config/zookeeper.`



- Start kafka

`C:/kafka/kafka_2.12-3.7.0/bin/windows/kafka-server-start.bat C:/kafka/kafka_2.12-3.7.0/config/server.properties`



- Start cassandra

First navingate to the bin directory in cassandra and write below commands.

`cd C:/cassandra/bin`



`cassandra`



### 2. Data Cleaning and Preprocessing

Input data is cleaned and preprocessed to ensure quality and compatibility for model training, including feature encoding and normalization.



### 3. Model Training and Evaluation

A Random Forest model is trained using Spark ML, evaluated for accuracy, and the model is saved locally.



### 4. Data Producer Code

A Python script generates random credit card transactions and publishes them to a Kafka topic.



### 5. Data Processing

The trained model processes transactions from the Kafka queue in real-time to predict fraud, and results are stored in Cassandra.



### 6. Reporting

A Streamlit dashboard displays real-time data, graphs, and analytics, pulling data directly from Cassandra.



## Challenges

1. System Compatibility issues

2. Synthetic Data generation

3. Cassandra setup - Memory allocation issues