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π Project Excerpt: An Immersive LEGO Journey π§±
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.
π Overview Dashboard Highlights:
π Year-over-Year Growth Analysis:
Step into the world of LEGO, where data unfolds a story of growth, trends, and correlations! π Your feedback and thoughts on this immersive LEGO journey are highly valued!
Data Cleaning Process
Missing Value Check for 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
.Downloading Data from the LEGO Website and Joining:
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
.subtheme
, pieces
, minifigs
, and US_retailPrice
by filling in missing values in F
with corresponding values from S
using the ISNULL
function.Missing Value Check for New Table:
Firstlego_sets
, for missing values in key columns such as year
, subtheme
, pieces
, minifigs
, and US_retailPrice
.Imputation of Missing Values:
agerange_min
with the average value where it was NULL.Minifigs
with the median value where it was NULL.US_retailPrice
with the average value where it was NULL.Pieces
with the median value where it was NULL.Categorization and Imputation:
Subtheme
with 'No Subtheme' where it was NULL.Themegroup
with 'LEGO Universe' where it was NULL.Data Exploration:
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.