The Evolution of LEGO - 5 Decade Journey

Tools used in this project
The Evolution of LEGO - 5 Decade Journey

Tableau Dashboard

About this project

šŸš€ Project Excerpt: An Immersive LEGO Journey šŸ§±

Embark on a captivating LEGO adventure through my latest project, featuring an insightful overview dashboard and a detailed analysis of year-over-year growth rates for retail prices, pieces, and minifigs.

šŸ” Overview Dashboard Highlights:

  • Total US retail prices, pieces, minifigs, and released sets.
  • Top five performers in categories like category, theme group, theme, subtheme, and Lego Name.
  • Performance parameters include US retail price, pieces, minifigs, and total released sets.
  • Price and age distribution insights, revealing trends like the 0-100 dollar pricing range and age preference of 4-7.
  • A compelling 53-year trend analysis showcasing the continuous growth of LEGO data from 1970 to 2022.
  • Correlation exploration between price and pieces based on category, uncovering positive correlations, with normal and extended categories showing the strongest bond.

šŸ“ˆ Year-over-Year Growth Analysis:

  • A comprehensive examination of increasing and decreasing trends over the years.
  • Overall, a slight but consistent upward trajectory in LEGO data from 1970 to 2022.

Step into the world of LEGO, where data unfolds a story of growth, trends, and correlations! šŸŒŸ Your feedback and thoughts on this immersive LEGO journey are highly valued!

Data Cleaning Process

  1. Missing Value Check for Original Tables:

    • Checked the original tables, [dbo].[lego_sets] and [dbo].[Website Data], for missing values in various columns, such as year, subtheme, themeGroup, pieces, minifigs, and US_retailPrice.
  2. Downloading Data from the LEGO Website and Joining:

    • Downloaded additional data from the LEGO website to supplement and fill in missing values in the original tables.
    • Created a new table named Firstlego_sets by joining data from [dbo].[lego_sets] (denoted as F) and [dbo].[Website Data] (denoted as S) using a LEFT JOIN based on the set_id.
    • Merged columns like subtheme, pieces, minifigs, and US_retailPrice by filling in missing values in F with corresponding values from S using the ISNULL function.
  3. Missing Value Check for New Table:undefined

    • Checked the newly created table, Firstlego_sets, for missing values in key columns such as year, subtheme, pieces, minifigs, and US_retailPrice.
  4. Imputation of Missing Values:

    • Imputed missing values in agerange_min with the average value where it was NULL.
    • Imputed missing values in Minifigs with the median value where it was NULL.
    • Imputed missing values in US_retailPrice with the average value where it was NULL.
    • Imputed missing values in Pieces with the median value where it was NULL.
  5. Categorization and Imputation:

    • Updated missing values in Subtheme with 'No Subtheme' where it was NULL.
    • Updated missing values in Themegroup with 'LEGO Universe' where it was NULL.
  6. Data Exploration:

    • Explored the cleaned data by ordering it based on the Year in ascending order.

The cleaning process involved a preliminary step of downloading additional data from the LEGO website to enhance the completeness of the dataset before performing the actual data cleaning steps.


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