The Evolution of LEGO - 5 Decade Journey

🚀 Project Excerpt: An Immersive LEGO Journey 🧱

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.

🔍 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.

📈 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! 🌟 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:

   • 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.