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\nClassification and Regression: Concrete Strength\n

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\nThis case study is to determine the factors (inputs) that have correlation to the concrete compressive strength (output).0:00 Introduction2:35 Download Jupyter Notebook4:00 Import Machine Learning Packages and Data7:33 Part 1: Data...\nThis case study is to determine the factors (inputs) that have correlation to the concrete compressive strength (output).

0:00 Introduction
2:35 Download Jupyter Notebook
4:00 Import Machine Learning Packages and Data
7:33 Part 1: Data Visualization
23:48 Part 2: Classification
38:17 Part 3: Regression
52:58 Summary

Visualization and exploration in Python reveals an understanding of the data and informs decisions about how data can be applied to predict strength. The data is used to determine data diversity, relationships, missing data, bad data, and factors that are important for regression. The second section is classification of low and high strength concrete with supervised learning methods. A final part is a regression to determine the concrete strength (MPa). Concrete strength is affected by factors such as water to cement ratio, raw material quality, the ratio of coarse or fine aggregate, concrete age, concrete compaction, temperature, relative humidity, and other factors during the curing of the concrete. The data includes the following information for 1030 concrete samples. \n
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Resource Details

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Educator Rating
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Media Length
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54:11
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Grade
10th - Higher Ed
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Subjects
Social Studies & History
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Media Type
Instructional Videos
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Source:
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\nAPMonitor.com\n\n
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Date
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2021
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Language
English
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Audiences
For Teacher Use
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Fine Print: Educational Use
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Concepts

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standard deviation, linear regression, scale, classification
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Additional Tags

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machine learning, engineer, regression, prediction, estimate, segregate, split, overview, python, test set, fly ash, super plasticizer, bar chart, heat map, neural network, decision tree, nearest neighbors, fine aggregate, confusion matrix, false positives, false negatives, higher low, bit bigger, coarse aggregate, compressive strength, single plot, joint plot, logistic regression, low strength, data set, imbalanced data, data frame, diagonal elements, standard scaler, input features, pair plot, pandas profiling
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Classroom Considerations

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Best For: Explaining a topic
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