My Dream

I have always had a deep love for my local antique shop, which was unique in that it exclusively sold military antiques. The shop also had its own museum and a vibrant, one-of-a-kind atmosphere. I volunteered there every weekend for years, learning about the history behind each piece and connecting with the militaria community. Unfortunately, the shop is no longer in business, but that doesn’t mean my passion for antiques has faded. While I may now explore antiques and follow trends in a less traditional way, my love for the craft and my appreciation for the stories these objects hold remains strong.

Main Objective

The military antiques market has many overpriced or bad deals. I need a data-driven resource to determine whether a price is fair by analyzing trends, product popularity, and marketplace differences.

Objectives:

  • Identify price drivers – What factors cause some items to be priced higher than others? (e.g., rarity, condition, seller reputation).

  • Analyze price trends – Are prices increasing or decreasing over time?

  • Determine product popularity – Which military antiques sell the most?

  • Compare marketplace pricing – Are some websites or sellers consistently more expensive?

  • Assess deal fairness – What constitutes a "good deal" based on historical data?

Key Performance Indicators:

  • Product prices over time – Measure price changes across categories.

  • Turnover rate (time to sell) – Faster sales could indicate high demand.

  • Product specifics – Age, condition, manufacturer, historical significance.

  • Marketplace pricing comparison – Identify which platforms have the best deals.

  • Seller reputation & reviews – Are higher-priced sellers more trustworthy?

  • Auction results vs. retail prices – Compare prices in auctions vs. fixed-price listings.

The Main Components

  • Scour the Internet

    Before I can start with the process of analyzing my data, there has to be data worth analyzing. Collecting, cleaning, and updating data is no small feat. This section dives into the hard work of gathering meaningful data from the web.

    Part One

  • Decipher the Data

    Once collected, the data needs to be cleaned and analyzed. For it to hold any value, we must extract meaning and insights.

    Part Two

  • View the Data

    With a robust pipeline and meaningful analysis in place, the final step is ensuring the data is accessible and impactful through clear presentation.

    Part Three