Build Your First Data Science Project with Streamlit: Beginner's Guide
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In this video, I build a comprehensive seller statistics dashboard for CheckOutMyCards (COMC) using Python and Streamlit. As a card collector and seller, I needed better insights than what COMC provides, so I created a custom analytics tool that transforms basic CSV exports into actionable business intelligence.
We start by extracting transaction data from COMC's seller history spreadsheets and build a full-featured dashboard that tracks sales performance across multiple dimensions. The app includes dynamic date range filters for both sold and acquired dates, price range selectors, and category breakdowns by sport including baseball, basketball, football, hockey, and non-sports like Pokemon.
The dashboard visualizes key metrics including sports distribution, decade analysis of card years, and quick stats showing manually added cards versus flipped inventory. I also implement advanced profit analysis by quantile, days-to-sale metrics, markup percentages, and annualized returns broken down by category. The project involves extensive data cleaning with regular expressions to extract player names and years from messy text fields, handling null values for cards added versus purchased, and calculating true profitability after accounting for COMC's insertion and processing fees.
By the end of the video, you'll understand how to build interactive data dashboards with Streamlit, process real-world messy CSV data with Pandas, create meaningful visualizations with Matplotlib, and develop practical business intelligence tools that solve actual problems. This is a perfect example of applying data science skills to a personal hobby and creating genuine value from basic spreadsheet exports.
TIMESTAMPS
00:00 Combining Data Science with Card Collecting
01:42 The Problem with COMC Seller Stats
03:02 Project Goals and Features
05:17 Early Project Design and Schema
06:40 Reviewing the COMC Dashboard
09:53 Starting the Code Setup
14:00 Building the Sold Date Range Selector
18:40 Creating the Acquired Date Filter
23:20 Sales Price Selector Implementation
27:00 Added vs Flipped Card Filter
31:40 Category Selector with Multiple Columns
40:00 Sports Breakdown Visualization
44:00 Decade Distribution Graph
52:00 Quick Stats Column Implementation
59:20 Calculating Profit and Markup
1:05:00 Extracting Years from Set Names
1:12:00 Days to Sale Calculations
1:16:30 Stats Breakdown by Sport
1:20:40 Top 10 Selling Players Feature
1:25:00 Advanced Profit Quantiles
1:27:00 Final Code Review and Wrap-up
OTHER SOCIALS:
Ryan’s LinkedIn: / ryan-p-nolan
Matt’s LinkedIn: / matt-payne-ceo
Twitter/X: https://x.com/RyanMattDS
Who is Ryan
Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF.
Who is Matt
Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One.
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