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HTML + CSS + JavaScript【实战案例】 实现动画导航栏效果

一、实现效果

  实现心血管疾病的预测准确率70%以上

二、数据集介绍

 数据共计70000条,其中心血管疾病患者人数为34979,未患病人数为35021。数据特征属性12个分别为如下所示:生理指标(性别、年龄、体重、身高等)、 医疗检测指标(血压、血糖、胆固醇水平等)和患者提供的主观信息(吸烟、饮酒、运动等):

age年龄
gender性别 1女性, 2 男性
height身高
weight 体重
ap_hi收缩压
ap_lo 舒张压
cholesterol胆固醇 1:正常; 2:高于正常; 3:远高于正常

gluc 葡萄糖,1:正常; 2:高于正常; 3:远高于正常

smoke 病人是否吸烟 alco 酒精摄入量

active 体育活动

cardio 有无心血管疾病,0:无;1:有

数据来源;http://idatascience.cn/

三、实现步骤

3.1 数据导入与分析

# 导入需要的工具包
import pandas as pd # data processing
import numpy as np
import matplotlib.pyplot as plt
#matplotlib inline
import seaborn as sns  # plot

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report,confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import warnings
warnings.filterwarnings("ignore")
import random


data = pd.read_csv('E: /心脏疾病预测分析/cardio_train.csv',sep=',')
data.drop(columns=['id'],inplace=True)
data.head()

 

 

 相关性分析:

correlations = data.corr()['cardio'].drop('cardio') #drop默认删除行
print(correlations)

 

 

3.2  划分数据集(训练数据集、测试数据集、验证数据集)

# 切分数据集
np.random.seed(1)#便于调试代码(设置种子-保证执行代码样本及结果一致--稳定复现结果)
# 获取当前随机状态
state = random.getstate()
# 获取随机种子
seed = state[1][0]

msk = np.random.rand(len(data))<0.85
df_train_test = data[msk]# 筛选出59450个随机样本
df_val = data[~msk]#剩下的随机样本--用作验证数据集

X = df_train_test.drop('cardio',axis=1)#删除最后一列,只包含样本特征
y = df_train_test['cardio']#样本对应的标签
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=70)#调用的训练和测试数据集样本划分函数

3.3  数据标准化

# 数据标准化
scale = StandardScaler()
scale.fit(X_train)
X_train_scaled = scale.transform(X_train)
X_train_ = pd.DataFrame(X_train_scaled,columns=data.columns[:-1])#添加列名,除去最后一列名(标签)

scale.fit(X_test)
X_test_scaled = scale.transform(X_test)
X_test_ = pd.DataFrame(X_test_scaled,columns=data.columns[:-1])

 3.4  特征选择

逻辑回归默认的算法为:lbfgs,L2正则化项。

模型的具体参数信息:


#特征选择
def feat_select(threshold):
    abs_cor = correlations.abs()
    features = abs_cor[abs_cor > threshold].index.tolist()
return features
def model(mod,X_tr,X_te):
mod.fit(X_tr,y_train)
pred = mod.predict(X_te)
print('Model score = ',mod.score(X_te,y_test)*100,'%')#子集准确性
# 逻辑回归
 #筛选出合适的阈值
lr = LogisticRegression()
#lr = LogisticRegression(penalty='l2', solver='saga')
# lr = LogisticRegression(solver='newton-cholesky')
# lr = LogisticRegression(solver='sag')
# lr = LogisticRegression(solver='newton-cg')

threshold = [0.001,0.002,0.005,0.01,0.02,0.05,0.06,0.08,0.1]
for i in threshold:
    print("Threshold is {}".format(i))
    feature_i = feat_select(i)
    X_train_i = X_train[feature_i]#训练集
    X_test_i = X_test[feature_i]#测试集
    model(lr,X_train_i,X_test_i)
feat_final = feat_select(0.005)# 筛选出重要特征,列表
print(feat_final)

 3.5  预测及结果评估

#验证数据集的标准化
X_val = np.asanyarray(df_val[feat_final])#删除最后一列,只包含样本特征  --转换为数组
y_val = np.asanyarray(df_val['cardio']) #--转换为数组

scale.fit(X_val)
X_val_scaled = scale.transform(X_val)
X_val_ = pd.DataFrame(X_val_scaled,columns=df_val[feat_final].columns)

#逻辑回归预测
lr.fit(X_train,y_train)
pred = lr.predict(X_val_)
#结果评估
print('Confusion Matrix =\n',confusion_matrix(y_val,pred))
print('\n',classification_report(y_val,pred))
lr.get_params()

 参考:

   sklearn.linear_model.LogisticRegression — scikit-learn 1.2.2 documentation

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