导入数据集¶

In [26]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')

加载 CSV 文件并执行数据分析¶

In [27]:
df = pd.read_csv('diabetes.csv')
print("Dataset loaded succesfully !!")

Dataset loaded succesfully !!
In [28]:
#前10行数据
df.head(10)
Out[28]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
0 6 148 72 35 0 33.6 0.627 50 1
1 1 85 66 29 0 26.6 0.351 31 0
2 8 183 64 0 0 23.3 0.672 32 1
3 1 89 66 23 94 28.1 0.167 21 0
4 0 137 40 35 168 43.1 2.288 33 1
5 5 116 74 0 0 25.6 0.201 30 0
6 3 78 50 32 88 31.0 0.248 26 1
7 10 115 0 0 0 35.3 0.134 29 0
8 2 197 70 45 543 30.5 0.158 53 1
9 8 125 96 0 0 0.0 0.232 54 1
In [29]:
#此数据集中的行数和列数
df.shape
Out[29]:
(768, 9)
In [30]:
#数据的统计测量
df.describe()
Out[30]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
count 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000
mean 3.845052 120.894531 69.105469 20.536458 79.799479 31.992578 0.471876 33.240885 0.348958
std 3.369578 31.972618 19.355807 15.952218 115.244002 7.884160 0.331329 11.760232 0.476951
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.078000 21.000000 0.000000
25% 1.000000 99.000000 62.000000 0.000000 0.000000 27.300000 0.243750 24.000000 0.000000
50% 3.000000 117.000000 72.000000 23.000000 30.500000 32.000000 0.372500 29.000000 0.000000
75% 6.000000 140.250000 80.000000 32.000000 127.250000 36.600000 0.626250 41.000000 1.000000
max 17.000000 199.000000 122.000000 99.000000 846.000000 67.100000 2.420000 81.000000 1.000000
In [31]:
# 检查“OutCome”的输出计数
df['Outcome'].value_counts()
Out[31]:
0    500
1    268
Name: Outcome, dtype: int64

0 --> 非糖尿病

1 --> 糖尿病

In [32]:
# 两种情况的平均值
df.groupby('Outcome').mean()
Out[32]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age
Outcome
0 3.298000 109.980000 68.184000 19.664000 68.792000 30.304200 0.429734 31.190000
1 4.865672 141.257463 70.824627 22.164179 100.335821 35.142537 0.550500 37.067164
In [33]:
# 分离数据和标签
X = df.iloc[:, :-1].values
y = df.iloc[:, -1].values
In [34]:
print(X)

[[  6.    148.     72.    ...  33.6     0.627  50.   ]
 [  1.     85.     66.    ...  26.6     0.351  31.   ]
 [  8.    183.     64.    ...  23.3     0.672  32.   ]
 ...
 [  5.    121.     72.    ...  26.2     0.245  30.   ]
 [  1.    126.     60.    ...  30.1     0.349  47.   ]
 [  1.     93.     70.    ...  30.4     0.315  23.   ]]
In [35]:
print(y)

[1 0 1 0 1 0 1 0 1 1 0 1 0 1 1 1 1 1 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0
 1 1 1 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0
 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1
 1 0 0 1 1 1 0 0 0 1 0 0 0 1 1 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 1 0 1 0 1 0 0 0 0 0
 1 1 1 1 1 0 0 1 1 0 1 0 1 1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 1 1 1 1 0 1 1 1 1
 0 0 0 0 0 1 0 0 1 1 0 0 0 1 1 1 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0
 1 0 1 0 0 1 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 1 1 0 0
 1 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 0 1 0 0 1 0 1 0 1 1 1 0 0 1 0 1 0 0 0 1
 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 1 1 0 0 1 0 0 1 0 0 1
 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 1 0 0 1 0 1 1 0 1 0 1 0 1
 0 1 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1
 1 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1
 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0
 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0
 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0 1 0 1 0 1 0
 1 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 1 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0
 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 1 1 1 1 0
 1 1 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 1 1
 0 0 0 1 0 1 1 0 0 1 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1
 1 0 0 1 0 0 1 0 1 1 1 0 0 1 1 1 0 1 0 1 0 1 0 0 0 0 1 0]

数据标准化¶

In [36]:
# 进行标准化
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
scaled_X = sc.fit_transform(X)
In [37]:
scaled_X
Out[37]:
array([[ 0.63994726,  0.84832379,  0.14964075, ...,  0.20401277,
         0.46849198,  1.4259954 ],
       [-0.84488505, -1.12339636, -0.16054575, ..., -0.68442195,
        -0.36506078, -0.19067191],
       [ 1.23388019,  1.94372388, -0.26394125, ..., -1.10325546,
         0.60439732, -0.10558415],
       ...,
       [ 0.3429808 ,  0.00330087,  0.14964075, ..., -0.73518964,
        -0.68519336, -0.27575966],
       [-0.84488505,  0.1597866 , -0.47073225, ..., -0.24020459,
        -0.37110101,  1.17073215],
       [-0.84488505, -0.8730192 ,  0.04624525, ..., -0.20212881,
        -0.47378505, -0.87137393]])
In [38]:
# 将scaled_X替换为X
X = scaled_X
In [39]:
X
Out[39]:
array([[ 0.63994726,  0.84832379,  0.14964075, ...,  0.20401277,
         0.46849198,  1.4259954 ],
       [-0.84488505, -1.12339636, -0.16054575, ..., -0.68442195,
        -0.36506078, -0.19067191],
       [ 1.23388019,  1.94372388, -0.26394125, ..., -1.10325546,
         0.60439732, -0.10558415],
       ...,
       [ 0.3429808 ,  0.00330087,  0.14964075, ..., -0.73518964,
        -0.68519336, -0.27575966],
       [-0.84488505,  0.1597866 , -0.47073225, ..., -0.24020459,
        -0.37110101,  1.17073215],
       [-0.84488505, -0.8730192 ,  0.04624525, ..., -0.20212881,
        -0.47378505, -0.87137393]])

训练测试分割¶

将数据集拆分为训练集和测试集

In [40]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,stratify=y, random_state=2)
In [41]:
# 让我们检查一下形状
print(X.shape,X_train.shape, X_test.shape)

(768, 8) (614, 8) (154, 8)

模型选择¶

我们的任务是预测疾病，但有许多机器学习分类算法。我们不能直接使用其中一种算法，所以我们需要先制作pipeline并找到合适的算法。

In [42]:
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from xgboost import XGBClassifier
from sklearn.cluster import KMeans
from sklearn.naive_bayes import GaussianNB
from sklearn.pipeline import Pipeline
from sklearn.decomposition import PCA
In [43]:
# 线性回归
pipeline_lr = Pipeline([('scaler1', StandardScaler()),
                        ('pca1', PCA(n_components=2)),
                        ('lr', LogisticRegression())])

# 支持向量分类器
pipeline_svc = Pipeline([('scaler2', StandardScaler()),
                        ('pca2', PCA(n_components=2)),
                        ('svc', SVC(kernel='linear'))])

# 决策树分类器
pipeline_dt = Pipeline([('scaler3', StandardScaler()),
                        ('pca3', PCA(n_components=2)),
                        ('dt', DecisionTreeClassifier())])

# 随机森林分类器
pipeline_rf = Pipeline([('scaler4', StandardScaler()),
                        ('pca4', PCA(n_components=2)),
                        ('rf', RandomForestClassifier(n_estimators=200))])

# K 均值
pipeline_km = Pipeline([('scaler5', StandardScaler()),
                        ('pca5', PCA(n_components=2)),
                        ('km', KMeans(n_clusters=2, random_state=0))])

# 朴素贝叶斯 - GaussianNB
pipeline_gnb = Pipeline([('scaler6', StandardScaler()),
                        ('pca6', PCA(n_components=2)),
                        ('gnb', GaussianNB())])

# XGBoost 分类器
pipeline_xgb = Pipeline([('scaler7', StandardScaler()),
                        ('pca7', PCA(n_components=2)),
                        ('xgb', XGBClassifier())])

# K-最近邻
pipeline_knb = Pipeline([('scaler8', StandardScaler()),
                        ('pca8', PCA(n_components=2)),
                        ('knb', KNeighborsClassifier())])
In [44]:
#管道列表
pipelines = [pipeline_svc, pipeline_dt, pipeline_gnb, pipeline_km, pipeline_lr, pipeline_xgb, pipeline_knb, pipeline_rf]
In [45]:
best_accuracy = 0.0
best_classifier = 0
best_pipeline = ""

pipe_dict = {
    0:'Logistic Regression',
    1:"Support Vector Classifier",
    2:"Decision Tree",
    3:"Random Forest Classifier",
    4:"KMeans",
    5:"Naive Bayes",
    6:"XGBoostClassifier",
    7:"K-Nearest"
}
In [46]:
for pipe in pipelines:
  pipe.fit(X_train, y_train)
In [47]:
for i, model in enumerate(pipelines):
  print('{} Test Accuracy: {}\n'.format(pipe_dict[i], model.score(X_test, y_test)))

Logistic Regression Test Accuracy: 0.7012987012987013

Support Vector Classifier Test Accuracy: 0.6168831168831169

Decision Tree Test Accuracy: 0.6948051948051948

Random Forest Classifier Test Accuracy: -335.34699890879193

KMeans Test Accuracy: 0.7012987012987013

Naive Bayes Test Accuracy: 0.6493506493506493

XGBoostClassifier Test Accuracy: 0.6948051948051948

K-Nearest Test Accuracy: 0.6818181818181818
In [48]:
for i, model in enumerate(pipelines):
  if model.score(X_test, y_test) > best_accuracy:
    best_accuracy = model.score(X_test, y_test)
    best_pipeline = model
    best_classifier = i

print("Classifier with best accuracy: {}".format(pipe_dict[best_classifier]))

Classifier with best accuracy: Logistic Regression
  • 比较所有可能的分类算法后，我们可以看到 Logistic 回归 具有最佳准确性

训练模型¶

好的，现在我们要构建机器学习模型，运行管道后，我们将使用 Logistic Regressoin 因为我们的输出是二进制的，即 0 和 1（根据我们的管道，在这种情况下逻辑回归是最好的）

逻辑回归

In [49]:
model  = LogisticRegression()
model.fit(X_train,y_train)
Out[49]:
LogisticRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression()

模型评估¶

让我们检查一下上述模型的分类报告、混淆矩阵和准确度得分

In [50]:
#导入工具
from sklearn.metrics import classification_report, accuracy_score, confusion_matrix
In [51]:
# 预测类
y_pred = model.predict(X_test)

分类报告

In [52]:
print("CLASSIFICATION REPORT: ")
print(classification_report(y_test, y_pred))

CLASSIFICATION REPORT: 
              precision    recall  f1-score   support

           0       0.77      0.89      0.83       100
           1       0.72      0.52      0.60        54

    accuracy                           0.76       154
   macro avg       0.75      0.70      0.72       154
weighted avg       0.75      0.76      0.75       154

准确度分数

In [53]:
#训练数据的准确率得分
X_train_pred = model.predict(X_train)
training_data_accuracy = accuracy_score(X_train_pred,y_train )*100
In [54]:
print("ACCURACY OF TRAINING DATA: ",training_data_accuracy.round(2))

ACCURACY OF TRAINING DATA:  78.5
In [55]:
#测试数据的准确率分数
X_test_pred = model.predict(X_test)
testing_data_accuracy = accuracy_score(X_test_pred,y_test)*100
In [56]:
print("ACCURACY OF TESTING DATA: ",testing_data_accuracy.round(2))

ACCURACY OF TESTING DATA:  75.97

混淆矩阵

In [57]:
# 混淆矩阵
cm = confusion_matrix(y_test, y_pred)
In [58]:
#可视化混淆矩阵
import seaborn as sns
sns.heatmap(cm,annot=True)
Out[58]:
<Axes: >

建立预测系统¶

In [59]:
# input_data = (4,110,92,0,0,37.6,0.191,30) 不是糖尿病患者
input_data = (5,166,72,19,175,25.8,0.587,51)  # diabetic

# 将数据输入numpy数组
input_data_as_numpy_array = np.asarray(input_data)

# 在我们预测一个实例时重塑数组
input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

# 标准化输入数据
std_data = sc.transform(input_data_reshaped)
# 打印（标准数据）

prediction = model.predict(std_data) # imp line
# 打印（预测）

if (prediction[0]==0):
  print('The person is not diabetic')
else:
  print('The person is diabetic')

The person is diabetic