糖尿病数据集（皮马印第安人）
包含768个Pima印第安女性样本的糖尿病预测数据集，8个数值特征，二分类标签。
基本信息
资源简介
糖尿病数据集（皮马印第安人）
【数据集背景】
本数据集源站标题为《SerdarTafrali/Machine_Learning_Pipeline_on_Diabetes_Dataset》。源站首次发布：2022-11-22。
【数据内容】
源站原始描述：Machine Learning Pipeline on Diabetes Dataset # Machine Learning Pipeline on Diabetes Dataset ### Business Problem: Developing a machine learning model that can predict whether people have diabetes when their characteristics are specified. ### Dataset Story: The dataset is part of the large dataset held at the National Institutes of Diabetes-Digestive-Kidney Diseases in the USA. Data used for diabetes research on Pima Indian women aged 21 and over living in Phoenix, the 5th largest city of the S…
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://github.com/SerdarTafrali/Machine_Learning_Pipeline_on_Diabetes_Dataset
【使用注意】
· 源站最后修改于 2022-11-30，引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
git clone https://github.com/SerdarTafrali/Machine_Learning_Pipeline_on_Diabetes_Dataset.git
curl -L -o repo.zip https://github.com/SerdarTafrali/Machine_Learning_Pipeline_on_Diabetes_Dataset/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
Machine Learning Pipeline on Diabetes Dataset
Business Problem:
Developing a machine learning model that can predict whether people have diabetes when their characteristics are specified.
Dataset Story:
The dataset is part of the large dataset held at the National Institutes of Diabetes-Digestive-Kidney Diseases in the USA. Data used for diabetes research on Pima Indian women aged 21 and over living in Phoenix, the 5th largest city of the State of Arizona in the USA. It consists of 768 observations and 8 numerical independent variables. The target variable is specified as “outcome”; 1 indicates positive diabetes test result, 0 indicates negative.
Variables:
Pregnancies: Number of pregnancies Glucose: Glucose. BloodPressure: Blood pressure. SkinThickness: Skin Thickness Insulin: Insulin. BMI: Body mass index. DiabetesPedigreeFunction: A function that calculates our probability of having diabetes based on our ancestry. Age: Age (years) Outcome: Information whether the person has diabetes or not. Have the disease (1) or not (0))
Project Stages:
- Exploratory Data Analysis
- Data Preprocessing
- Model & Prediction
- Model Evaluation
- Model Validation: Holdout
- Model Validation: 10-Fold Cross Validation
- Prediction for A New Observation
数据加载示例（表格/文本类）
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
完整仓库：github.com/SerdarTafrali/Machine_Learning_Pipeline_on_Diabetes_Dataset
数据缺失？
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