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29 lines
1.5 KiB
29 lines
1.5 KiB
1. Objective:
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We want to predict whether patients suffer from diabetes based on other body metrics like age and blood pressure.
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2. License: Free to use with citation: Lichman, M. (2013). UCI Machine Learning Repository http://archive.ics.uci.edu/ml. Irvine, CA: University of California, School of Information and Computer Science.
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3. Data Source:
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http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/
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4. Data Set Information:
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The dataset contains 8 attributes (aside from the presence of diabetes) for 768 individuals. "Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage."
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5. Field Meanings (quoted verbatim from the source above):
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1. number_pregnant: Number of times pregnant
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2. glucose_concentration: Plasma glucose concentration a 2 hours in an oral glucose tolerance test
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3. blood_pressure: Diastolic blood pressure (mm Hg)
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4. skin_thickness: Triceps skin fold thickness (mm)
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5. serum_insulin: 2-Hour serum insulin (mu U/ml)
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6. BMI: Body mass index (weight in kg/(height in m)^2)
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7. diabetes_pedigree: Diabetes pedigree function
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8. age: Age (years)
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9. response: Class variable (0 for no diabetes or 1 for diabetes)
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6. Parameter Selection:
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A. Dashboard: Predict Categorical Fields
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Settings:
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1) Search: | inputlookup diabetes.csv
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2) Field to predict: response
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3) Fields to use: BMI, age, blood_pressure, diabetes_pedigree, glucose_concentration, number_pregnant
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