Business Intelligence E-Commerce Project

Project Overview

The Business Intelligence E-Commerce Project is designed to process, analyze, and extract valuable insights from e-commerce data. This project covers data cleaning, transformation, and analysis, culminating in a star schema data warehouse. The insights support decision-making processes for optimizing business operations.

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business-intelligence-ecommerce
├──data
│   ├──processed
│   │   ├──cleaned_events_chunks
│   │   │   ├──cleaned_events_chunk_1.csv
│   │   │   ├──cleaned_events_chunk_2.csv
│   │   │   ├──cleaned_events_chunk_3.csv
│   │   │   └──cleaned_events_chunk_4.csv
│   │   ├──cleaned_distribution_centers.csv
│   │   ├──cleaned_inventory_items.csv
│   │   ├──cleaned_order_items.csv
│   │   ├──cleaned_orders.csv
│   │   ├──cleaned_products.csv
│   │   └──cleaned_users.csv
│   └──raw
│   │   ├──events_chunks
│   │   │   ├──events_1.csv
│   │   │   ├──events_2.csv
│   │   │   ├──events_3.csv
│   │   │   └──events_4.csv
│   │   ├──distribution_centers.csv
│   │   ├──inventory_items.csv
│   │   ├──order_items.csv
│   │   ├──orders.csv
│   │   ├──products.csv
│   │   └──users.csv
├──src
│   ├──analysis
│   │   ├──association.py
│   │   ├──clustering.py
│   │   ├──Elbow_Silhouette.png
│   │   ├──kmeans.png
│   │   └──user_data_with_clusters.csv
│   ├──ETL
│   │   ├──insights.ipynb
│   │   ├──pipeline.ipynb
│   │   ├──visualisation.pbix
│   │   └──visualisations.ipynb
│   └──Warehouse
│   │   ├──schema.pdf
│   │   └──schema.sql
├──README.md
└──requirements.txt

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Features

1. Data Processing:

   • Raw data stored in data/raw/.
   • Cleaned and transformed data stored in data/processed/.

2. ETL Pipeline:

   • Found in src/ETL/pipeline.ipynb.
   • Automates data extraction, transformation, and loading into a warehouse-ready format.

3. Data Warehouse Schema:

   • Star schema SQL scripts available in src/Warehouse/schema.sql.

4. Analysis:

   • Clustering analysis performed in src/analysis/clustering.py.
   • Data visualizations in src/ETL/visualisations.ipynb.

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Requirements

The project depends on the following Python packages:

• pandas
• numpy
• matplotlib
• seaborn
• geopandas
• shapely

Install all required packages using:

pip install -r requirements.txt

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How to Run

1. Clone the repository:

   git clone https://github.com/Albaforce/business-intelligence-ecommerce.git
   cd business-intelligence-ecommerce

2. Install dependencies:

   pip install -r requirements.txt

3. Execute the ETL pipeline:

   • Open src/ETL/pipeline.ipynb in Jupyter Notebook and run all cells.

4. Run the analysis scripts:

   • For clustering analysis, execute src/analysis/clustering.py.

5. Access the data warehouse schema:

   • SQL scripts for creating and populating the star schema are available in src/Warehouse/schema.sql.

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Data

The data directory includes:

• Raw Data: Unprocessed CSV files in data/raw/.
• Processed Data: Cleaned and transformed datasets in data/processed/.

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Contribution Guidelines

1. Fork the repository and create a new branch for your feature or bug fix.
2. Follow proper coding standards and comment your code.
3. Submit a pull request with detailed explanations.

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Acknowledgments

• Inspiration and support from the open-source community.
• Datasets used for analysis sourced from simulated e-commerce activities.
• dataset link : https://www.kaggle.com/datasets/mustafakeser4/looker-ecommerce-bigquery-dataset?select=users.csv