Brazilian E-Commerce Analysis by Haikal Assyauqi

Overview

This project is a comprehensive analysis of Brazilian E-Commerce data sourced from the Olist platform. Conducted as part of the IDCamp Data Analysis program, the analysis spans transactions from 2016 to 2018. The dataset encompasses various dimensions such as orders, customers, products, and sellers. Out of the eight available tables, the analysis focuses on four key ones:

• Orders
• Customers
• Order Items
• Products

Key Objectives

1. Identify patterns and trends in the e-commerce data.
2. Provide actionable insights to improve business strategies.
3. Visualize key metrics through an interactive dashboard.

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Prerequisites

Ensure you have the following tools installed before running the project:

pip install jupyter notebook pandas matplotlib seaborn streamlit

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Installation & Setup

1. Clone the Repository

Clone the project repository to your local machine:

git clone https://github.com/Haikalin/Streamlit_Data.git

2. Navigate to the Project Directory

Change the current directory to the project folder:

cd Streamlit_Data

3. Install Dependencies

Install all required Python packages:

pip install -r requirements.txt

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Running the Project

Run Jupyter Notebook

To explore the analysis and run individual cells:

Click on the notebook.ipynb file and click "Run" on each cell or "Run All" to execute the entire notebook.

Run Streamlit Dashboard Locally

For an interactive dashboard:

streamlit run dashboard/dashboard.py

Access the Online Dashboard

Alternatively, view the dashboard hosted online:

Online Dashboard

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Project Structure

The repository is structured as follows:

.
Streamlit_Data
    │   notebook.ipynb
    │   README.md
    │   requirements.txt
    │   url.txt
    │   
    ├───dashboard
    │       dashboard.py
    │       main_data.csv
    │       
    └───dataset
            customers_dataset.csv
            orders_dataset.csv
            order_items_dataset.csv
            sellers_dataset.csv

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Tech Stack

The following technologies and libraries were used in this project:

• Python 3.x: Core programming language.
• Pandas: Data manipulation and analysis.
• Matplotlib: Visualization library for static plots.
• Seaborn: Statistical data visualization.
• Streamlit: Framework for building the interactive dashboard.

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Features

1. Descriptive Analysis: Summary statistics for orders, products, and customers.
2. Trend Analysis: Time-series analysis to identify key patterns.
3. Product Insights: Breakdown of product categories, prices, and sales.
4. Customer Behavior: Analysis of purchase frequency and locations.
5. Interactive Dashboard: Dynamic visualizations for deeper insights.

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Author

This project was developed by Haikal Assyauqi as part of the IDCamp Data Analysis program.

For inquiries or collaboration, feel free to reach out via GitHub: Haikalin.

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Future Enhancements

• Incorporate additional tables from the dataset for deeper insights.
• Implement machine learning models for predictive analytics.
• Expand dashboard functionalities with advanced visualizations.