心脏病数据集-18171
包含1025条记录的心脏病检测表格数据集，涵盖年龄、性别、血压等临床变量。
基本信息
资源简介
该数据集用于检测心脏病，包含年龄、性别、胸痛类型、血压等临床变量，共1025条记录，属于表格数据，可用于分类或预测任务。
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/micky225/Heart-Disease-Detector.git
curl -L -o repo.zip https://github.com/micky225/Heart-Disease-Detector/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
AI and Data Documentation
Prediction Model Documentation
Overview
This Python script contains code for developing a machine learning model to detect heart disease based on a given dataset. The model leverages various Python libraries for data preprocessing, feature engineering, model training, and evaluation.
Purpose
The purpose of this model is to predict the presence or absence of heart disease in individuals based on a set of features. Early detection of heart disease can significantly improve patient outcomes by enabling timely interventions and treatments.
Packages Used
The following packages are imported and used within the script:a
- warnings: Python module for controlling warning messages.
- numpy (np): Fundamental package for scientific computing with Python.
- pandas (pd): Data manipulation and analysis library.
- matplotlib.pyplot (plt): Plotting library for creating static, animated, and interactive visualizations.
- seaborn (sns): Data visualization library based on matplotlib, providing a high-level interface for drawing attractive statistical graphics.
- matplotlib.colors.ListedColormap: Module for creating a colormap from a list of colors.
- sklearn.model_selection.train_test_split: Function for splitting arrays or matrices into random train and test subsets.
- scipy.stats.boxcox: Function for computing the Box-Cox power transformation.
- sklearn.pipeline.make_pipeline: Function for constructing a pipeline from the given estimators.
- sklearn.preprocessing.StandardScaler: Class for standardization, scaling features by removing the mean and scaling to unit variance.
- sklearn.neighbors.KNeighborsClassifier: K-Nearest Neighbors classifier implementation.
- sklearn.svm.SVC: Support Vector Classification implementation.
- sklearn.model_selection.GridSearchCV: Grid search with cross-validation for optimizing hyperparameters of a classifier.
- sklearn.model_selection.StratifiedKFold: Stratified K-Folds cross-validator.
- sklearn.metrics.classification_report: Function for building a text report showing the main classification metrics.
- sklearn.metrics.accuracy_score: Function for computing the accuracy classification score.
- sklearn.tree.DecisionTreeClassifier: Decision Tree classifier implementation.
- sklearn.ensemble.RandomForestClassifier: Random Forest classifier implementation.
- sklearn.linear_mo
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))
数据缺失？
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⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
