This repository contains Jupyter Notebooks related to fraud detection, data streaming, and real-time data visualization. These notebooks cover various aspects of processing, analyzing, and modeling data to address fraudulent transactions in eCommerce and other contexts.

1. Analysing Fraudulent Transaction Data.ipynb

   • Purpose: Exploratory data analysis (EDA) of fraudulent transaction datasets.
   • Key Components:
     • Analyzing patterns in fraudulent transactions.
     • Visualizing data distributions and key features.
     • Libraries used: pandas, matplotlib, seaborn.

2. Building Models for eCommerce Fraud Detection.ipynb

   • Purpose: Building and evaluating machine learning models for fraud detection.
   • Key Components:
     • Preprocessing data for model training.
     • Training and evaluating models such as Logistic Regression, Random Forest, etc.
     • Libraries used: scikit-learn, numpy, pandas.

3. Producing the Data.ipynb

   • Purpose: Simulating and producing data streams for analysis.
   • Key Components:
     • Generating mock data for fraud scenarios.
     • Producing data using streaming technologies.
     • Libraries used: faker, pandas.

4. Consuming Data Using Kafka and Visualise.ipynb

   • Purpose: Consuming data streams and visualizing results.
   • Key Components:
     • Setting up Kafka consumers to read data streams.
     • Visualizing the processed data for insights.
     • Libraries used: kafka-python, matplotlib.

5. Streaming Application Using Spark Structured Streaming.ipynb

   • Purpose: Building a streaming application for real-time data processing.
   • Key Components:
     • Setting up Spark Structured Streaming.
     • Processing streaming data in real-time.
     • Libraries used: pyspark.

• Python 3.x
• Jupyter Notebook or Google Colab
• Required Python libraries:
  • pandas, numpy, matplotlib, seaborn
  • scikit-learn, faker, kafka-python, pyspark

1. Clone the repository:

   git clone <repository-url>

2. Navigate to the project directory:

   cd <repository-folder>

3. Install the required libraries:

   pip install -r requirements.txt

• Open the notebooks in Jupyter or any compatible environment (e.g., Google Colab).
• Follow the instructions within each notebook to execute the cells in sequence.

The datasets used in this project are too large to include in the repository. Please email me at [your-email@example.com] to request access to the datasets.

This project is licensed under GNU (General Public License). See the LICENSE file for details.

• Python documentation
• Open-source libraries used in the project
• Kafka and Spark community resources