E-Commerce Public Data Analysis with Python - Dicoding

E-Commerce Data Dashboard Streamlit App

Table of Contents

• Overview
• Project Structure
• Installation
• Usage
• Data Sources

Overview

This project is a data analysis and visualization project focused on e-commerce public data. It includes code for data wrangling, exploratory data analysis (EDA), and a Streamlit dashboard for interactive data exploration. This project aims to analyze data on the E-Commerce Public Dataset.

Project Structure

• dashboard/: This directory contains dashboard.py which is used to create dashboards of data analysis results.
• data/: Directory containing the raw CSV data files.
• notebook.ipynb: This file is used to perform data analysis.
• notebook_ID.ipynb: notebook.ipynb in Indonesian.
• README.md: This documentation file.

Installation

1. Clone this repository to your local machine:

git clone https://github.com/mhdhfzz/data-analyst-dicoding.git

2. Go to the project directory

cd data-analyst-dicoding

3. Install the required Python packages by running:

pip install -r requirements.txt

Usage

1. Data Wrangling: Data wrangling scripts are available in the notebook.ipynb file to prepare and clean the data.

2. Exploratory Data Analysis (EDA): Explore and analyze the data using the provided Python scripts. EDA insights can guide your understanding of e-commerce public data patterns.

3. Visualization: Run the Streamlit dashboard for interactive data exploration:

cd data-analyst-dicoding/dashboard
streamlit run dashboard.py

Access the dashboard in your web browser at http://localhost:8501.

Data Sources

The project uses E-Commerce Public Dataset from Belajar Analisis Data dengan Python's Final Project offered by Dicoding.