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🛍️ ShopNile — Data Analysis & Business Intelligence Project

Your One-Stop Online Marketplace
A full data pipeline project: from raw messy data to an interactive business dashboard.

📌 Project Overview

ShopNile is a comprehensive data analysis project for a fictional online marketplace. It covers the full data pipeline — from raw messy data to a fully interactive business dashboard — demonstrating real-world skills in data cleaning, transformation, and visualization.

📁 Project Structure

ShopNile/
│
├── raw_data/
│   ├── customers_messy.csv
│   ├── orders_messy.csv
│   ├── payments_messy.csv
│   └── products_messy.csv
│
├── clean_data/
│   ├── Customers_Clean.csv
│   ├── Orders_Clean.csv
│   ├── Payments_Clean.csv
│   └── Products_Clean.csv
│
├── ShopNile_Cleaning.ipynb   # Data cleaning pipeline
├── ShopNile_Analysis.py      # Streamlit dashboard
└── ShopNile_Logo.png

🗄️ Data Model

The project follows a Star Schema:

Table Type Key Column Records
Customers_Clean Dimension customer_id 500
Products_Clean Dimension product_id 120
Orders_Clean Fact order_id 3,000
Payments_Clean Fact order_id 3,050

Relationships:

  • Orders → Customers via customer_id
  • Orders → Products via product_id
  • Orders → Payments via order_id

🧹 Data Cleaning — ShopNile_Cleaning.ipynb

Key cleaning steps applied to all four raw datasets:

  • ✅ Removed duplicate rows and fixed inconsistent primary/foreign keys
  • ✅ Handled missing values through imputation or removal
  • ✅ Standardized date formats across all date columns
  • ✅ Corrected data type mismatches (e.g. numeric fields stored as strings)
  • ✅ Fixed invalid categorical values (e.g. payment status, city names)
  • ✅ Removed/capped outliers in numeric columns (price, amount_paid, income)
  • ✅ Normalized text fields (name casing, trimming whitespace)

📊 Dashboard — ShopNile_Analysis.py

Built with Streamlit and Plotly Express.

KPI Cards

Metric Description
💰 Total Revenue Sum of all payments
📦 Total Orders Count of unique orders
👥 Total Customers Count of unique customers

Charts

Chart Type Description
Sales by Category Pie Chart Revenue breakdown across product categories
Sales by City Bar Chart Revenue ranked by customer city
Sales by Payment Method Pie Chart Revenue split by payment method
Sales Over Time Line Chart Quarterly revenue trend
Top 10 Products Bar Chart Highest-revenue products by name

Interactivity

  • 🔽 Category Slicer — filters all charts and KPIs by product category

🛠️ Tech Stack

Tool Purpose
Python 3 Core programming language
Pandas Data manipulation and cleaning
Jupyter Notebook Interactive data cleaning documentation
Streamlit Web-based dashboard framework
Plotly Express Interactive chart rendering

🚀 How to Run

1. Install dependencies

pip install streamlit pandas plotly

2. Place the clean CSV files inside a folder named clean_data/

3. Run the dashboard

streamlit run ShopNile_Analysis.py

4. Open your browser and go to → http://localhost:8501

📂 Dataset Overview

Column Table Description
customer_id Customers Unique customer identifier
name, age, city Customers Customer demographics
signup_date, income Customers Registration and financial info
product_id Products Unique product identifier
product_name, category Products Product details
price, stock Products Pricing and inventory
order_id Orders Unique order identifier
quantity, discount Orders Order details
order_date, payment_method Orders Transaction info
amount_paid, payment_status Payments Payment details

Dashboard URL

https://shopnile-analysis-using-python-h9bbtrwmjconbq49mfkc8f.streamlit.app

ShopNile — Data Analysis Portfolio Project