E-commerce Sales Dashboard

A professional Streamlit dashboard for analyzing e-commerce sales metrics, featuring interactive visualizations and configurable date range filtering.

Features

• Interactive Date Filtering: Select year and month range with automatic comparison to previous year
• Key Performance Indicators: Revenue, monthly growth, average order value, and total orders with trend indicators
• Revenue Analysis: Monthly trend comparison between current and previous periods
• Product Category Analysis: Top 10 categories by revenue with visual ranking
• Geographic Analysis: US choropleth map showing revenue distribution by state
• Customer Experience Metrics: Review scores and delivery time analysis

Project Structure

cc-data-analysis/
├── app.py                  # Streamlit dashboard application
├── data_loader.py          # Data loading and preparation functions
├── business_metrics.py     # Business metric calculation functions
├── EDA.ipynb              # Original exploratory data analysis
├── EDA_Refactored.ipynb   # Refactored analysis notebook
├── ecommerce_data/        # Data directory
│   ├── orders_dataset.csv
│   ├── order_items_dataset.csv
│   ├── products_dataset.csv
│   ├── customers_dataset.csv
│   ├── order_reviews_dataset.csv
│   └── order_payments_dataset.csv
└── README.md

Installation

1. Clone the repository and navigate to the project directory:

cd cc-data-analysis

2. Install dependencies using uv:

uv sync

Running the Dashboard

Start the Streamlit dashboard:

uv run streamlit run app.py

The dashboard will open in your default browser at http://localhost:8501.

Running the Jupyter Notebook

To run the refactored analysis notebook:

uv run jupyter lab EDA_Refactored.ipynb

Dashboard Layout

Header

• Title on the left
• Date filters on the right (apply globally to all visualizations):
  • Year selector (defaults to 2023)
  • Start Month selector
  • End Month selector

KPI Cards Row

Four metric cards displaying:

• Total Revenue: With year-over-year growth indicator
• Monthly Growth: Average month-over-month growth rate
• Average Order Value: With year-over-year growth indicator
• Total Orders: With year-over-year growth indicator

Trend indicators use green for positive trends and red for negative trends.

Charts Grid (2x2)

1. Revenue Trend: Line chart comparing current year (solid) vs previous year (dashed)
2. Top 10 Categories: Horizontal bar chart with blue gradient, sorted by revenue
3. Revenue by State: US choropleth map with blue color scale
4. Satisfaction vs Delivery Time: Bar chart showing average review score by delivery time bucket

Bottom Row

• Average Delivery Time: Days with trend indicator (lower is better)
• Review Score: Large score display with star rating

Module Reference

data_loader.py

Functions for data loading and preparation:

• load_raw_datasets(data_dir): Load all CSV files from directory
• prepare_sales_data(orders, order_items, status_filter): Create merged sales dataset
• filter_by_date_range(df, start_year, start_month, end_year, end_month): Filter by date range
• prepare_sales_with_products(sales_data, products): Add product categories
• prepare_sales_with_customers(sales_data, orders, customers): Add customer geography
• prepare_sales_with_reviews(sales_data, reviews): Add review scores
• calculate_delivery_time(df): Calculate delivery days

business_metrics.py

Functions for calculating business metrics:

• calculate_total_revenue(sales_data): Sum of prices
• calculate_revenue_growth(current, previous): Percentage growth
• calculate_monthly_revenue(sales_data): Revenue by month
• calculate_average_monthly_growth(sales_data): Average MoM growth
• calculate_total_orders(sales_data): Unique order count
• calculate_average_order_value(sales_data): AOV calculation
• calculate_revenue_by_category(sales_data): Category breakdown
• calculate_revenue_by_state(sales_data): Geographic breakdown
• calculate_average_review_score(sales_data): Mean review score
• calculate_average_delivery_time(sales_data): Mean delivery days
• calculate_review_by_delivery_speed(sales_data): Reviews by delivery bucket

Data Dictionary

Dataset	Column	Description
orders	order_id	Unique order identifier
orders	customer_id	Customer identifier
orders	order_status	Status (delivered, shipped, canceled, etc.)
orders	order_purchase_timestamp	Order date and time
orders	order_delivered_customer_date	Delivery date
order_items	order_item_id	Item sequence within order
order_items	product_id	Product identifier
order_items	price	Item price in USD
order_items	freight_value	Shipping cost
products	product_category_name	Product category
customers	customer_state	US state abbreviation
reviews	review_score	Rating from 1-5