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Shivang-Shrivastav/Featurization-Model-Selection-Tuning

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Featurization-Model-Selection-Tuning

Data Description: The actual concrete compressive strength (MPa) for a given mixture under a specific age (days) was determined from laboratory. Data is in raw form (not scaled). The data has 8 quantitative input variables, and 1 quantitative output variable, and 1030 instances (observations).

Domain: Cement manufacturing

Context: Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate.

Attribute Information:  Cement : measured in kg in a m3 mixture  Blast : measured in kg in a m3 mixture  Fly ash : measured in kg in a m3 mixture  Water : measured in kg in a m3 mixture  Superplasticizer : measured in kg in a m3 mixture  Coarse Aggregate : measured in kg in a m3 mixture  Fine Aggregate : measured in kg in a m3 mixture  Age : day (1~365)  Concrete compressive strength measured in MPa

Learning Outcomes:  Exploratory Data Analysis  Building ML models for regression  Hyper parameter tuning

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Feature Engineering, Cross Validation, ROC, AUC, Pipeline, Model Tuning, Hyper Parameter Tuning, Grid Search

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