Data Analysis Projects

This repository contains a collection of data analysis projects. Each subfolder in this repository represents a separate project with its own code, datasets, and documentation.

Projects Overview

01 Market Basket Analysis

• Description: Analyzes online retail transaction data to uncover customer behavior patterns, product performance, transaction trends, and product associations using association rule mining.
• Key Features:
  • Comprehensive data cleaning and quality assessment
  • Exploratory data analysis with advanced visualizations
  • Customer behavior and spending pattern analysis
  • Product performance and catalog insights
  • Market basket analysis using association rules
  • Cross-selling and bundling recommendations
  • Seasonal and geographical trend analysis
• Technologies: Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, NetworkX, Jupyter Notebook
• Dataset: Online retail transaction data with 541,909 records spanning December 2010 - December 2011
• Instructions: See the README in the 01 Market Basket Analysis folder for detailed setup and execution instructions.

02 Customer Lifetime Value (CLV) Analysis

• Description: Calculates and predicts customer value over time, helping companies identify high-value customers and tailor their marketing strategies.
• Status: In Development
• Dataset: OnlineRetail.csv
• Instructions: See the README in the 02 Customer Lifetime Value (CLV) Analysis folder for more details.

Future Projects

• Project 03: Geographical Sales Performance Dashboard
  Description: Provides a detailed visual analysis of sales data by region, enabling the identification of market trends and growth opportunities.

• Project 04: Social Media Campaign Analysis
  Description: Examines social media engagement and sentiment to evaluate campaign performance and inform future digital marketing efforts.

Additional projects will be added over time.

Setup and Execution

Prerequisites

• Python 3.12 or higher
• uv (Python package installer and virtual environment manager) - Install from astral.sh/uv
• Git for cloning the repository

General Setup Steps

1. Clone the Repository

   git clone https://your.repository.url.git
   cd Data-Analysis-Projects

2. Navigate to a Specific Project Folder

   cd "01 Market Basket Analysis"
   # or
   cd "02 Customer Lifetime Value (CLV) Analysis"

3. Set up Environment using uv

   # Create a virtual environment
   uv venv
   
   # Activate the virtual environment
   # On macOS/Linux:
   source .venv/bin/activate
   # On Windows (PowerShell):
   # .\.venv\Scripts\Activate.ps1
   
   # Install dependencies
   uv pip install -r requirements.txt

4. Launch Jupyter Notebook

   jupyter notebook main.ipynb
   # or for JupyterLab:
   jupyter lab main.ipynb

5. Run the Analysis

   • Open the main.ipynb notebook in your Jupyter environment
   • Run cells individually or use "Restart Kernel and Run All Cells" for complete execution
   • Follow project-specific instructions in each folder's README

Directory Structure

Data-Analysis-Projects/
├── 01 Market Basket Analysis/
│   ├── main.ipynb                 # Main analysis notebook
│   ├── README.md                  # Project documentation
│   ├── Report.md                  # Detailed findings report
│   ├── requirements.txt           # Python dependencies
│   ├── pyproject.toml            # Project configuration
│   ├── uv.lock                   # Dependency lock file
│   ├── .python-version           # Python version specification
│   ├── .gitignore                # Git ignore rules
│   └── onlineretail/             # Dataset folder
│       ├── OnlineRetail.csv      # Original dataset
│       └── OnlineRetail_Cleaned.csv # Processed dataset
├── 02 Customer Lifetime Value (CLV) Analysis/
│   ├── main.ipynb                 # Main analysis notebook
│   ├── README.md                  # Project documentation
│   ├── requirements.txt           # Python dependencies
│   ├── pyproject.toml            # Project configuration
│   ├── uv.lock                   # Dependency lock file
│   ├── .python-version           # Python version specification
│   └── onlineretail/             # Dataset folder
│       └── OnlineRetail.csv      # Dataset
└── README.md                     # This file

Key Technologies Used

• Python Libraries: pandas, numpy, matplotlib, seaborn, plotly, networkx, scikit-learn
• Development Environment: Jupyter Notebook/Lab
• Package Management: uv
• Visualization: Interactive plots with Plotly, statistical plots with Seaborn
• Analysis Techniques: Association rule mining, statistical analysis, data visualization

Project Highlights

Market Basket Analysis Results

• Identified strong product associations for cross-selling opportunities
• Analyzed customer behavior patterns across different time periods and countries
• Generated actionable business recommendations for marketing and inventory management
• Created comprehensive visualizations including network graphs, heatmaps, and interactive plots

License

This repository is open-sourced under the MIT License.

Contributing

Contributions are welcome! Please follow these guidelines:

• Open an issue to discuss potential changes
• Submit pull requests with clear descriptions
• Include proper documentation for new projects
• Ensure code quality and reproducibility

When adding new projects:

1. Create a dedicated folder with a descriptive name
2. Include a detailed README with setup instructions
3. Add requirements.txt with all dependencies
4. Update this main README to include the new project

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Happy analyzing! 📊