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BMW Sales Dataset Summary (2010–2024): Descriptive Insights

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Collins Odhiambo

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Overview

The dataset contains 50,000 records with 11 attributes that describe BMW’s sales, pricing, and model characteristics across multiple regions and fuel technologies between 2010 and 2024. The absence of missing values indicates high data integrity — ideal for advanced statistical and predictive modeling.

Dataset Composition

Attribute Type Description Example Values
Model Categorical (11 unique) BMW models (Series 1–8, X-series, i-series) “X5”, “3 Series”, “i4”
Year Numeric Model year (2010–2024) 2018
Region Categorical (6 unique) Global market regions Asia, Europe, Africa, North America
Color Categorical (6 unique) Dominant car colors Black, White, Blue, Red
Fuel_Type Categorical (4 unique) Type of fuel technology Petrol, Diesel, Hybrid, Electric
Transmission Categorical (2 unique) Transmission mode Automatic, Manual
Engine_Size_L Numeric Engine capacity in liters 1.5 – 5.0 L
Mileage_KM Numeric Cumulative mileage (usage) 3 – 199,996 km
Price_USD Numeric Retail or resale price in USD 30,000 – 119,998
Sales_Volume Numeric Annual sales volume per model 100 – 9,999
Sales_Classification Categorical (2 unique) Market performance tier High, Low

Key Statistical Highlights

Metric Mean Std. Dev. Interpretation
Year 2017 ±4.32 The Data covers both early ICE and modern EV years, enabling trend analysis across transition phases.
Engine Size (L) 3.25 ±1.01 BMW’s lineup remains performance-oriented, averaging 3.2L engines.
Mileage (KM) 100,307 ±57,942 Balanced dataset: includes new and moderately used cars, good for depreciation or wear analysis.
Price (USD) 75,034 ±25,998 Typical luxury bracket; interquartile range (~$52K–$98K) confirms premium positioning.
Sales Volume 5,067 ±2,856 Average annual unit sales per record indicate strong global brand activity.

Interpretation & Analytical Value

  1. Data Completeness:
    Zero missing values make this dataset highly reliable for predictive modeling (e.g., price prediction, sales forecasting).

  2. Balanced Representation:
    With multiple models, colors, and fuel types equally distributed, it supports comparative market analytics without heavy bias.

  3. Temporal Coverage:
    The range (2010–2024) captures BMW’s transition from combustion engines to hybrid and electric, providing a perfect lens to study technological evolution and market adaptation.

  4. Policy & Industry Relevance:
    Governments and automotive economists can use such structured data to:

    • Assess green mobility adoption.

    • Track market penetration of EVs.

    • Support fiscal policy design (e.g., carbon taxation, import duties).

Acknowledgments

  • Data Source: BMW Sales Data (2010–2024), processed within the DatalytIQs Academy Analytics Framework.

  • Technical Tools: Python (pandas, matplotlib, seaborn, scipy.stats).

  • Contributors:

    • Collins Odhiambo Owino — Lead Analyst & Author, DatalytIQs Academy

    • Kaggle Open Data Contributors — Data structure and design inspiration

    • BMW Group Market Insights — Contextual reference for interpretation

Author’s Note

Written by Collins Odhiambo Owino
Founder & Lead Researcher, DatalytIQs Academy
Empowering learners and professionals in Mathematics, Economics, and Finance through data-driven insight.

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