心脏病预测（SVM模型）

题目

Solve the heart disease problem

Here is a small dataset provided by the Cleveland Clinic Foundation for Heart Disease, which are several hundred rows in the CSV. Each row describes a patient, and each column describes an attribute.

Using this information to predict whether a patient has heart disease, which in this dataset is a binary classification task.

Remember, the most important things is preprocessing the data and transform to feature column.

代码

数据预处理

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

//读取数据
URL = 'https://storage.googleapis.com/applied-dl/heart.csv'
df = pd.read_csv(URL)

//结合数据集信息， 我们可以得到age,trestbpd,chol,thalach,oldpeak均为Numerical类型，不用处理。 sex,fbs,exang,target均为二分类数值，不用处理。 剩下的cp,restecg,slope,ca，thal均为多分类数值，需要数据预处理。

//拆分属性的值
a = pd.get_dummies(df['cp'], prefix = "cp")
b = pd.get_dummies(df['restecg'], prefix = "restecg")
c = pd.get_dummies(df['slope'], prefix = "slope")
d = pd.get_dummies(df['ca'], prefix = "ca")
e = pd.get_dummies(df['thal'], prefix = "thal")
df = pd.concat([df, a, b, c, d, e], axis = 1)
df = df.drop(columns = ['cp', 'restecg', 'slope', 'ca', 'thal'])
df.head(5)
//提取XY值
Y = df.target.values
X = df.drop(['target'], axis = 1)

//数据标准化
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

sc = StandardScaler()
sc.fit(X)
X = sc.transform(X)
//拆分为训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(X, Y)

建立SVM模型

from sklearn.svm import SVC

svm = SVC(random_state = 1)
svm.fit(x_train, y_train)

acc = svm.score(x_test, y_test)*100
print("Test Accuracy of SVM Algorithm: {:.2f}%".format(acc))