Project 6: Customer Data Dashboard with Streamlit

Build an interactive dashboard to visualize and analyze customer data using Streamlit.

Difficulty

Beginner

Time Estimate

2-3 days

Skills You'll Practice

• Streamlit
• Data Visualization
• Interactive Dashboards
• Data Analysis
• User Interface Design

Learning Objectives

By completing this project, you will learn to:

• Build interactive web applications with Streamlit
• Create dynamic visualizations
• Implement user input and filtering
• Design user-friendly dashboards
• Deploy Streamlit applications
• Work with real-world customer data

Prerequisites

Before starting, you should have completed:

• Module 00: Prerequisites (Python Basics)
• Module 01: Python for Data Science (Pandas, Matplotlib, Seaborn)
• Basic understanding of data visualization

Dataset

Option 1: E-commerce Customer Dataset

• E-commerce Customer Data
• Contains customer demographics, purchase history, and behavior

Option 2: Customer Churn Dataset

• Telco Customer Churn
• Customer information and churn status

Option 3: Create Your Own

• Use any customer-related dataset from Kaggle or UCI ML Repository

Project Steps

Step 1: Setup and Data Loading

• Install Streamlit: pip install streamlit
• Load customer dataset
• Explore data structure
• Clean and preprocess data

Step 2: Basic Dashboard Structure

• Create main Streamlit app file (app.py)
• Set up page configuration
• Add title and description
• Create sidebar for navigation

Step 3: Data Overview Section

• Display dataset summary
• Show basic statistics
• Display data sample
• Add data shape information

Step 4: Interactive Visualizations

• Create charts with user controls:
  • Bar charts for categorical data
  • Line charts for trends
  • Scatter plots for relationships
  • Histograms for distributions
• Add filters and dropdowns
• Implement date range selectors

Step 5: Analysis Sections

• Customer segmentation visualization
• Purchase behavior analysis
• Geographic distribution (if available)
• Time-based trends

Step 6: Advanced Features

• Add download functionality for filtered data
• Implement search/filter capabilities
• Create comparison views
• Add insights and summary statistics

Step 7: Deployment (Optional)

• Deploy to Streamlit Cloud
• Or deploy to Heroku/AWS
• Share your dashboard

Code Structure

project-06-customer-dashboard/
├── README.md
├── app.py                    # Main Streamlit application
├── data/
│   └── customer_data.csv     # Dataset
├── notebooks/
│   └── data_exploration.ipynb
├── requirements.txt
└── .streamlit/
    └── config.toml           # Streamlit configuration

Example Code Skeleton

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Page configuration
st.set_page_config(
    page_title="Customer Dashboard",
    layout="wide"
)

# Load data
@st.cache_data
def load_data():
    df = pd.read_csv('data/customer_data.csv')
    return df

df = load_data()

# Sidebar
st.sidebar.title("Filters")
# Add filters here

# Main content
st.title("Customer Data Dashboard")
st.markdown("Interactive dashboard for customer analysis")

# Overview section
st.header("Data Overview")
col1, col2, col3, col4 = st.columns(4)
col1.metric("Total Customers", len(df))
col2.metric("Total Revenue", f"${df['revenue'].sum():,.0f}")
# Add more metrics

# Visualizations
st.header("Customer Analysis")
# Add charts here

# Run with: streamlit run app.py

Key Streamlit Components to Use

• st.title(), st.header(), st.subheader() - Text elements
• st.dataframe(), st.table() - Display data
• st.plotly_chart(), st.pyplot() - Visualizations
• st.selectbox(), st.slider(), st.multiselect() - User inputs
• st.columns() - Layout
• st.sidebar - Sidebar elements
• st.cache_data - Data caching

Extensions

1. Add Machine Learning Predictions

   • Integrate a simple classification model
   • Show predictions in the dashboard
   • Add model performance metrics

2. Real-time Data Updates

   • Connect to a database
   • Auto-refresh functionality
   • Live data updates

3. Multi-page Dashboard

   • Use st.sidebar for navigation
   • Create separate pages for different analyses
   • Use st.session_state for state management

4. Advanced Visualizations

   • Interactive Plotly charts
   • Maps with st.map() or folium
   • 3D visualizations

5. Export Functionality

   • Download filtered data as CSV
   • Export charts as images
   • Generate PDF reports

Evaluation Criteria

Your dashboard should:

• Load and display data correctly
• Have clear, intuitive navigation
• Include multiple interactive visualizations
• Allow users to filter and explore data
• Be visually appealing and well-organized
• Include meaningful insights
• Have proper error handling

Resources

• Streamlit Documentation
• Streamlit Gallery
• Streamlit Cheat Sheet
• Plotly with Streamlit

Tips for Success

1. Start Simple: Begin with basic visualizations, then add interactivity
2. Use Caching: Use @st.cache_data for data loading to improve performance
3. Organize Well: Use columns and containers for better layout
4. Test Interactively: Run streamlit run app.py and test as you build
5. Keep It Fast: Optimize data loading and processing
6. Make It Beautiful: Use Streamlit's built-in styling options

Next Steps

After completing this project:

• Try deploying your dashboard
• Add more advanced features
• Build dashboards for other datasets
• Move to intermediate projects that use Streamlit for ML applications

Ready to build? Start by setting up your environment and loading your dataset!