机器学习的医疗乳腺癌数据的乳腺癌疾病预测

项目视频讲解：基于机器学习的医疗乳腺癌数据的乳腺癌疾病预测 完整代码数据分享_哔哩哔哩_bilibili

效果演示：

 代码：

#第一步！导入我们需要的工具
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns 
%matplotlib inline
from sklearn.linear_model import LogisticRegression 
from sklearn.model_selection import train_test_split 
from sklearn.model_selection import KFold 
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import svm
from sklearn import metrics
import warnings
warnings.filterwarnings('ignore')

data.drop("Unnamed: 32",axis=1,inplace=True)
data.drop("id",axis=1,inplace=True)
data.columns

features_mean= list(data.columns[1:11])
features_se= list(data.columns[11:20])
features_worst=list(data.columns[21:31])
print(features_mean)
print("-----------------------------------")
print(features_se)
print("------------------------------------")
print(features_worst)

data['diagnosis']=data['diagnosis'].map({'M':1,'B':0})
data.describe() 

corr = data[features_mean].corr()
plt.figure(figsize=(14,14))
sns.heatmap(corr, cbar = True,  square = True, annot=True, fmt= '.2f',annot_kws={'size': 15},
           xticklabels= features_mean, yticklabels= features_mean,
           cmap= 'coolwarm')

 

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