Dataset research report
Pima research report
A reproducible data report with schema notes, generated chart evidence, suggested follow-up questions, and export-ready Helix queries.
Executive Summary
pima The pima dataset from the UCI ML repository. Predict diabetes of a patient. Configurations and tasks Configuration Task Description pima Binary classification Does the patient have diabetes? Usage from datasets import load_dataset dataset = load_dataset("mstz/pima")["train"]
Research Context
Pima: 500 rows by 9 columns. These exploratory charts are generated automatically from the data - open the dataset in Helix to ask your own questions.
Data Profile
Chart Evidence
These views are generated from the dataset profile. Each chart is paired with a Helix query so it can be opened, adjusted, and exported.
number_of_pregnancies vs plasma_glucose_concentration
Relationship between number_of_pregnancies and plasma_glucose_concentration.
Open and export this chartDistribution of number_of_pregnancies
Histogram of number_of_pregnancies values.
Open and export this chartCorrelation of numeric columns
Pearson correlation between numeric columns.
Open and export this chartFollow-Up Queries
Preview Rows
| # | number_of_pregnanciesinteger | plasma_glucose_concentrationfloat | diastolic_blood_pressurefloat | triceps_thicknessfloat | serum_insulinfloat | bmifloat | diabetes_pedigreefloat | agefloat |
|---|---|---|---|---|---|---|---|---|
| 1 | 6 | 148 | 72 | 35 | 0 | 33.6 | 0.627 | 50 |
| 2 | 1 | 85 | 66 | 29 | 0 | 26.6 | 0.351 | 31 |
| 3 | 8 | 183 | 64 | 0 | 0 | 23.3 | 0.672 | 32 |
| 4 | 1 | 89 | 66 | 23 | 94 | 28.1 | 0.167 | 21 |
| 5 | 0 | 137 | 40 | 35 | 168 | 43.1 | 2.288 | 33 |
| 6 | 5 | 116 | 74 | 0 | 0 | 25.6 | 0.201 | 30 |
Data Dictionary
- number_of_pregnancies numeric
- plasma_glucose_concentration numeric
- diastolic_blood_pressure numeric
- triceps_thickness numeric
- serum_insulin numeric
- bmi numeric
- diabetes_pedigree numeric
- age numeric
- has_diabetes numeric
Method And Limits
- Load the catalog entry and preview rows from the processed dataset file.
- Infer numeric, categorical, time, and location fields from real columns.
- Generate a small set of defensive Plotly chart specifications from that profile.
- Expose each chart idea as a query link so the report can be rerun or exported in Helix.
This report is intentionally reproducible. It uses the local catalog metadata and generated chart specifications rather than claiming external conclusions beyond the dataset.