🛍️ 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.

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📌 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.

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📁 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

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🗄️ 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

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🧹 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)

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📊 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

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🛠️ 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

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🚀 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

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📂 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

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Dashboard URL

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

ShopNile — Data Analysis Portfolio Project