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Competition - predicting concrete strength
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  • Can you predict the strength of concrete?

    📖 Background

    You work in the civil engineering department of a major university. You are part of a project testing the strength of concrete samples.

    Concrete is the most widely used building material in the world. It is a mix of cement and water with gravel and sand. It can also include other materials like fly ash, blast furnace slag, and additives.

    The compressive strength of concrete is a function of components and age, so your team is testing different combinations of ingredients at different time intervals.

    The project leader asked you to find a simple way to estimate strength so that students can predict how a particular sample is expected to perform.

    💾 The data

    The team has already tested more than a thousand samples (source):

    Compressive strength data:
    • "cement" - Portland cement in kg/m3
    • "slag" - Blast furnace slag in kg/m3
    • "fly_ash" - Fly ash in kg/m3
    • "water" - Water in liters/m3
    • "superplasticizer" - Superplasticizer additive in kg/m3
    • "coarse_aggregate" - Coarse aggregate (gravel) in kg/m3
    • "fine_aggregate" - Fine aggregate (sand) in kg/m3
    • "age" - Age of the sample in days
    • "strength" - Concrete compressive strength in megapascals (MPa)

    Acknowledgments: I-Cheng Yeh, "Modeling of strength of high-performance concrete using artificial neural networks," Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808 (1998).

    df <- readr::read_csv('data/concrete_data.csv', show_col_types = FALSE)

    💪 Challenge

    Provide your project leader with a formula that estimates the compressive strength. Include:

    1. The average strength of the concrete samples at 1, 7, 14, and 28 days of age.
    2. The coefficients , ... , to use in the following formula:

    🧑‍⚖️ Judging criteria

    This is a community-based competition. The top 5 most upvoted entries will win.

    The winners will receive DataCamp merchandise.

    ✅ Checklist before publishing

    • Rename your workspace to make it descriptive of your work. N.B. you should leave the notebook name as notebook.ipynb.
    • Remove redundant cells like the judging criteria, so the workbook is focused on your work.
    • Check that all the cells run without error.

    ⌛️ Time is ticking. Good luck!