Haberman生存数据集
Haberman生存数据集是用于乳腺癌患者5年生存预测的表格数据集，包含306个样本和4个特征。
基本信息
资源简介
Haberman生存数据集
【数据集背景】
本数据集源站标题为《sayanta1999/Exploratory-Data-Analysis-on-Haberman-Dataset》。源站首次发布：2020-03-11。
【数据内容】
源站原始描述：Exploratory-Data-Analysis-on-Haberman-Dataset Exploratory Data Analysis on Haberman''s Dataset About the dataset: 1) "status"=1 means patient has survived 5 years or more "status"=2 means patient had died before 5 years. 2) "nodes" refers to number of positive axillary nodes 3) There are 306 data points, 4 features (including class,ie, "status") 4) There are 225 data points of status=1 and 162 data points of status=2 5) No Nan Values present in the dataset Visualisations Done using: 1) Distributi…
【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/sayanta1999/Exploratory-Data-Analysis-on-Haberman-Dataset
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/sayanta1999/Exploratory-Data-Analysis-on-Haberman-Dataset.git
curl -L -o repo.zip https://github.com/sayanta1999/Exploratory-Data-Analysis-on-Haberman-Dataset/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
Exploratory-Data-Analysis-on-Haberman-Dataset
Exploratory Data Analysis on Haberman’'s Dataset
About the dataset:
- “status”=1 means patient has survived 5 years or more “status”=2 means patient had died before 5 years.
- “nodes” refers to number of positive axillary nodes
- There are 306 data points, 4 features (including class,ie, “status”)
- There are 225 data points of status=1 and 162 data points of status=2
- No Nan Values present in the dataset
Visualisations Done using: - Distribution Plots
- Probability Distribution Funtion Plot and Cummulative Distribution Function Plot
- Box-Plots
- Violin Plots
- Pair-Plots
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/sayanta1999/Exploratory-Data-Analysis-on-Haberman-Dataset
需要用于商业化训练或医疗器械注册（NMPA/FDA）？
公开数据往往缺乏完整的病理回证与专家标注。千方医数集依托陕川双基地与超十家三甲医院直连，提供完全合规、去标识化、支持注册级质控的专病数据集交易服务。
