数据集获取

# 克隆仓库（含数据与代码）
git clone https://github.com/suvhradipghosh07/EDA-on-Haberman-Cancer-Survival-Dataset.git

# 或仅下载打包文件
curl -L -o repo.zip https://github.com/suvhradipghosh07/EDA-on-Haberman-Cancer-Survival-Dataset/archive/refs/heads/master.zip
unzip repo.zip

源站 README 摘录（使用方式）

Haberman’s Cancer Survival: Visual Exploratory Data Analysis using Python (SeaBorn ,Matplotlib)

Exploratory Data Analysis (EDA) is the series of asking questions and applying statistics and visualization techniques to answer those questions and to uncover the hidden insights from the data. A case study on the cancer survival data set is done to explore the most common EDA techniques in this Repository.
<br>
<h1>Data Description</h1>
The Haberman’s survival data set contains cases from a study that was conducted between 1958 and 1970 at the University of Chicago’s Billings Hospital on the survival of patients who had undergone surgery for breast cancer.
<br>
<h2>Attribute Information:</h2>
<pre>
1.Age of patient at time of operation (numerical)
2.Patient’s year of operation (year — 1900, numerical)
3.Number of positive auxillary nodes detected (numerical)
4.Survival status (class attribute) 1 = the patient survived 5 years or longer 2 = the patient died within 5 years
Setting the objective is the key in EDA which will structure your thoughts in the entire analysis. Here the objective is to predict whether the patient will survive after 5 years or not based upon the patient’s age, year of treatment and the number of positive lymph nodes.
<h2>

1. Environment Configuration
   Import the required packages and load the data set.</h2>

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import warnings
warnings.filterwarnings("ignore")

<p>Density Plot</p>

sns.jointplot(data=df, x="Age",y="Year of Operations", kind="kde")
plt.ylabel("Cancer")
plt.show()

数据说明

哈伯曼癌症生存数据集是一个经典的临床医学数据集，记录了1958年至1970年间在美国芝加哥大学比林斯医院接受乳腺癌手术患者的生存研究数据。数据集包含306个病例，每个病例有四个属性：患者手术时的年龄、手术年份、检测到的阳性辅助淋巴结数量以及生存状态（患者是否在手术后存活超过5年）。该数据集通过回顾性临床研究构建，主要用于乳腺癌患者的生存分析和预后预测，是医学统计、机器学习在医疗领域应用的经典基准数据集。

完整仓库：github.com/suvhradipghosh07/EDA-on-Haberman-Cancer-Survival-Dataset