🎲 BetWise: Data Pipeline & Analytics Project 💡

Welcome to the BetWise repository! This project encompasses the design and implementation of a robust Data Pipeline for handling large-scale analytics and Data Analytics focusing on sports betting trends across multiple websites. The repository is structured in two key stages:

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🚀 Project Overview

📁 Stage 1: Data Pipeline Design 🏗️

In this stage, we design and implement a data pipeline to process customer information across multiple locations. The pipeline automates the ETL/ELT process to facilitate seamless data transfer from the Operations Center to the Analytics Center.

Scenario:
A new customer signed a contract and requires us to process their data and perform analytics to deliver valuable business insights. The analytics team has no direct access to the Operations Center, and the data must be replicated daily in batch mode using a custom API. The pipeline handles daily transfers of high-volume transactional data, ensuring secure and scalable processing.

🔹 Pipeline Flow: Data moves from Location 1 (Operations Center) to Location 2 (Analytics Center) via a secured API that allows pulling 5,000 records per call. The pipeline is designed to automate this flow and ensure data integrity.
🔹 Technologies Used: AWS for cloud infrastructure, custom ETL/ELT strategies, secured data replication protocols.

📁 Stage 2: Data Analytics 📊

This stage focuses on Data Analytics for a client in the sports betting industry. The analysis was conducted on a dataset containing transaction details, including player bets, odds, outcomes, and more.

Key Insights:

High Proportion of Losses in Live Bets

Impact of Betting Odds on Outcomes

Consistent Currency Usage

Player Preferences in Bet Types

Peak Betting Hours

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📂 Repository Structure

The repository is divided into two main stages:

└── 📁betWork
    └── 📁Stage_1_Data_Pipeline_Design
        └── 📁assets
            └── data_pipeline_architecture.png  # Pipeline architecture diagram
            └── data_pipeline_diagram.png       # Pipeline process flow
        └── 📁docs
            └── Stage_1_Case_1.md               # Problem statement for Stage 1
            └── stage1_pipeline_solution.pdf    # Full solution documentation in PDF
    └── 📁Stage_2_Data_Analytics
        └── 📁assets
            └── PL1.png                         # Insight #1: Visualization
            └── PL2.png                         # Insight #2: Visualization
            └── PL3.png                         # Insight #3: Visualization
            └── PL4.png                         # Insight #4: Visualization
            └── PL5.png                         # Insight #5: Visualization
        └── 📁docs
            └── Stage_2_Case_2.md               # Problem statement for Stage 2
        └── 📁src
            └── data.csv                        # Raw betting data for analysis
            └── data.zip                        # Compressed data for faster access
            └── Stage_2_Case_2_Betting_Data_Analysis_PHP.pdf  # Full analysis report in PDF
            └── Stage_2_Case_2_Betting_Data_Analysis.ipynb    # Jupyter Notebook with all code and visualizations
    └── README.md                                # You're here! 😊

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🛠️ Technologies & Tools Used

• Cloud Platform: AWS (preferred)
• Programming Languages: Python (for Data Analytics).
• Tools: Jupyter Notebooks & Plotly.

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🚧 How to Use this Repository

1. Stage 1: Data Pipeline Design

   • Docs: Navigate to the docs folder inside Stage_1_Data_Pipeline_Design to find the problem statement (Stage_1_Case_1.md) and full solution (stage1_pipeline_solution.pdf).
   • Visuals: Explore the pipeline architecture and process flow diagrams located in the assets folder:
     • data_pipeline_architecture.png: Architecture diagram
     • data_pipeline_diagram.png: Process flow diagram

2. Stage 2: Data Analytics

   • Source Code: Run the Jupyter notebook (Stage_2_Case_2_Betting_Data_Analysis.ipynb) in the src folder to reproduce the analysis and insights.
   • Data: The raw data for analysis is provided as data.csv, and a compressed version is available as data.zip.
   • Docs: Review the problem statement (Stage_2_Case_2.md) located in the docs folder for Stage 2.
   • Analysis Report PHP: A full analysis report PHP is also provided as Stage_2_Case_2_Betting_Data_Analysis_PHP.pdf.
   • Visuals: View the insights visualizations in the assets folder:
     • PL1.png to PL5.png: Graphical representations of key insights.

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🏆 Key Features

• 📈 Detailed Data Analytics: Advanced analysis of player behavior, betting outcomes, and market trends.
• 🔄 Automated Data Pipelines: Efficient and secure ETL/ELT processes that streamline data replication.
• 🎨 Beautiful Visualizations: Eye-catching, interactive plots to convey insights clearly and engagingly.