-
Notifications
You must be signed in to change notification settings - Fork 0
/
diabetes_logisticregression.py
207 lines (158 loc) · 5.96 KB
/
diabetes_logisticregression.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
# -*- coding: utf-8 -*-
"""diabetes_LogisticRegression
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1tLmkiYXqfLBnG2UvYUwrfsFnWJAYJgZJ
"""
from google.colab import drive
drive.mount('/content/drive')
dosya_yolu = '/content/drive/MyDrive/Colab Notebooks/diabetes/diabetes.csv'
# Gerekli kütüphaneleri yükleme
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score, accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report
import seaborn as sns
import numpy as np
# Veri setini yükleme
df = pd.read_csv(dosya_yolu)
# Veri setini görüntüleme
df.head()
# Veri seti kolon isimleri
df.columns
# Veri seti kolon veritipleri
df.info()
# satır ve sütun sayıları
df.shape
# kolonlara ait istatistiki bilgiler
df.describe()
# kolonlarda boş değerler var mı
df.isnull().sum()
# 1 ve 0 lardan oluşan sonuç kümem de bulunan 0 ve 1 lerin sayısal grafiği
sns.countplot(x='Outcome',data=df,palette=['b','g'])
# veri setindeki değişkenler arasındaki ilişkileri görselleştirmek
sns.pairplot(data=df,hue='Outcome')
plt.show()
# Özellikler ve hedef değişkeni ayırma
X = df.drop(["Outcome"], axis=1)
y = df["Outcome"]
# Eğitim ve test setlerine ayırma
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# LogisticRegression algoritmasını uygulama
model=LogisticRegression()
# Modeli eğitme
model.fit(X_train, y_train)
# test verilerindeki girdiler için sınıf tahminleri yapmak
y_predict=model.predict(X_test)
y_predict
# confusion matris
cm=confusion_matrix(y_test,y_predict)
cm
# confusion matris renkli tablosu
sns.heatmap(pd.DataFrame(cm),annot=True, cmap="Greens")
# Modelin doğruluğu
accuracy=accuracy_score(y_test,y_predict)
print("Accuracy : ",round(accuracy,2)*100,'%')
# Performans metriklerinin hesaplanması
accuracy = accuracy_score(y_test, y_predict)
precision = precision_score(y_test, y_predict)
recall = recall_score(y_test, y_predict)
f1 = f1_score(y_test, y_predict)
# Performans metriklerini ekrana yazdırma
print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1 Score:", f1)
# ROC eğrisi ve AUC değeri
y_pred_prob = model.predict_proba(X_test)[:, 1]
fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)
roc_auc = roc_auc_score(y_test, y_pred_prob)
# ROC grafiği çizimi
plt.plot(fpr, tpr, label='ROC Curve (AUC = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic')
plt.legend(loc="lower right")
plt.show()
# Örnek bir girdi için sonuç tahmini
example_input = [8,99,84,0,0,35.4,0.388,50] #0
predicted_class = model.predict([example_input])[0]
print("Tahmin edilen sınıf: ", predicted_class)
# Örnek bir girdi için sonuç tahmini
example_input = [6,148,72,35,0,33.6,0.627,50] #1
predicted_class = model.predict([example_input])[0]
print("Tahmin edilen sınıf: ", predicted_class)
# Örnek bir girdi için sonuç tahmini
example_input = [5,166,72,19,175,25.8,0.587,51] #1
predicted_class = model.predict([example_input])[0]
print("Tahmin edilen sınıf: ", predicted_class)
# Logistic Regression "newton-cg" yöntemi için model oluşturma ve parametrelerini belirleme
model = LogisticRegression(penalty='l2', C=1.0, solver='newton-cg', max_iter=100)
# Modeli eğit
model.fit(X_train, y_train)
# Test veri seti üzerinde tahmin yap
y_pred = model.predict(X_test)
# Performans metriklerinin hesaplanması
accuracy = accuracy_score(y_test, y_predict)
precision = precision_score(y_test, y_predict)
recall = recall_score(y_test, y_predict)
f1 = f1_score(y_test, y_predict)
# Performans metriklerini ekrana yazdırma
print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1 Score:", f1)
# Logistic Regression "liblinear" yöntemi için model oluşturma ve parametrelerini belirleme
model = LogisticRegression(penalty='l2', C=1.0, solver='liblinear', max_iter=100)
# Modeli eğit
model.fit(X_train, y_train)
# Test veri seti üzerinde tahmin yap
y_pred = model.predict(X_test)
# Performans metriklerinin hesaplanması
accuracy = accuracy_score(y_test, y_predict)
precision = precision_score(y_test, y_predict)
recall = recall_score(y_test, y_predict)
f1 = f1_score(y_test, y_predict)
# Performans metriklerini ekrana yazdırma
print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1 Score:", f1)
# Logistic Regression "sag" yöntemi için model oluşturma ve parametrelerini belirleme
model = LogisticRegression(penalty='l2', C=1.0, solver='sag', max_iter=100)
# Modeli eğit
model.fit(X_train, y_train)
# Test veri seti üzerinde tahmin yap
y_pred = model.predict(X_test)
# Performans metriklerinin hesaplanması
accuracy = accuracy_score(y_test, y_predict)
precision = precision_score(y_test, y_predict)
recall = recall_score(y_test, y_predict)
f1 = f1_score(y_test, y_predict)
# Performans metriklerini ekrana yazdırma
print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1 Score:", f1)
# Logistic Regression "saga" yöntemi için model oluşturma ve parametrelerini belirleme
model = LogisticRegression(penalty='l2', C=1.0, solver='saga', max_iter=100)
# Modeli eğit
model.fit(X_train, y_train)
# Test veri seti üzerinde tahmin yap
y_pred = model.predict(X_test)
# Performans metriklerinin hesaplanması
accuracy = accuracy_score(y_test, y_predict)
precision = precision_score(y_test, y_predict)
recall = recall_score(y_test, y_predict)
f1 = f1_score(y_test, y_predict)
# Performans metriklerini ekrana yazdırma
print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1 Score:", f1)