質問編集履歴
2
コードを変更しました。
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -320,11 +320,9 @@
|
|
|
320
320
|
|
|
321
321
|
#create a function to apply the output to data
|
|
322
322
|
|
|
323
|
-
import math
|
|
324
|
-
|
|
325
323
|
def predict_score(x):
|
|
326
324
|
|
|
327
|
-
return 1 / (1 +
|
|
325
|
+
return 1 / (1 + np.exp(-(x.iloc[1] * -4.4 + x.iloc[2] * 490.4 + x.iloc[3] * -1.4 + x.iloc[4] * 1.1 + 69.3)))
|
|
328
326
|
|
|
329
327
|
|
|
330
328
|
|
|
@@ -332,8 +330,6 @@
|
|
|
332
330
|
|
|
333
331
|
datrum.head(10)
|
|
334
332
|
|
|
335
|
-
|
|
336
|
-
|
|
337
333
|
#出てきたメッセージ
|
|
338
334
|
|
|
339
335
|
#<ipython-input-26-b3ac908bb86f>:4: RuntimeWarning: overflow encountered in double_scalars
|
1
ロジスティック回帰分析の部分のコードも追加しました
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -66,6 +66,214 @@
|
|
|
66
66
|
|
|
67
67
|
```Python
|
|
68
68
|
|
|
69
|
+
#元データ
|
|
70
|
+
|
|
71
|
+
***
|
|
72
|
+
|
|
73
|
+
Amount Profit Quantity Frequency
|
|
74
|
+
|
|
75
|
+
Order ID
|
|
76
|
+
|
|
77
|
+
B-25601 1429.0 -1218.0 19 8
|
|
78
|
+
|
|
79
|
+
B-25602 3889.0 975.0 22 10
|
|
80
|
+
|
|
81
|
+
B-25603 2025.0 -180.0 25 16
|
|
82
|
+
|
|
83
|
+
B-25604 222.0 22.0 11 4
|
|
84
|
+
|
|
85
|
+
B-25605 75.0 0.0 7 2
|
|
86
|
+
|
|
87
|
+
... ... ... ... ...
|
|
88
|
+
|
|
89
|
+
B-26096 1091.0 121.0 18 6
|
|
90
|
+
|
|
91
|
+
B-26097 1688.0 -504.0 23 7
|
|
92
|
+
|
|
93
|
+
B-26098 1189.0 350.0 21 6
|
|
94
|
+
|
|
95
|
+
B-26099 3417.0 859.0 15 4
|
|
96
|
+
|
|
97
|
+
B-26100 934.0 256.0 6 3
|
|
98
|
+
|
|
99
|
+
***
|
|
100
|
+
|
|
101
|
+
|
|
102
|
+
|
|
103
|
+
#優良/休眠顧客情報を追加
|
|
104
|
+
|
|
105
|
+
def Dormant(x):
|
|
106
|
+
|
|
107
|
+
if x > 0:
|
|
108
|
+
|
|
109
|
+
return 1
|
|
110
|
+
|
|
111
|
+
else:
|
|
112
|
+
|
|
113
|
+
return 0
|
|
114
|
+
|
|
115
|
+
|
|
116
|
+
|
|
117
|
+
datrum['Customer Type'] = datrum['Profit'].apply(Dormant)
|
|
118
|
+
|
|
119
|
+
datrum.head(10)
|
|
120
|
+
|
|
121
|
+
|
|
122
|
+
|
|
123
|
+
***
|
|
124
|
+
|
|
125
|
+
Amount Profit Quantity Frequency Customer Type
|
|
126
|
+
|
|
127
|
+
Order ID
|
|
128
|
+
|
|
129
|
+
B-25601 1429.0 -1218.0 19 8 0
|
|
130
|
+
|
|
131
|
+
B-25602 3889.0 975.0 22 10 1
|
|
132
|
+
|
|
133
|
+
B-25603 2025.0 -180.0 25 16 0
|
|
134
|
+
|
|
135
|
+
B-25604 222.0 22.0 11 4 1
|
|
136
|
+
|
|
137
|
+
B-25605 75.0 0.0 7 2 0
|
|
138
|
+
|
|
139
|
+
B-25606 87.0 4.0 2 2 1
|
|
140
|
+
|
|
141
|
+
B-25607 50.0 15.0 4 2 1
|
|
142
|
+
|
|
143
|
+
B-25608 2953.0 -1456.0 19 8 0
|
|
144
|
+
|
|
145
|
+
B-25609 510.0 24.0 8 4 1
|
|
146
|
+
|
|
147
|
+
B-25610 2105.0 -746.0 24 12 0
|
|
148
|
+
|
|
149
|
+
***
|
|
150
|
+
|
|
151
|
+
|
|
152
|
+
|
|
153
|
+
#テストデータをテスト用と訓練用に分ける
|
|
154
|
+
|
|
155
|
+
x_train, x_test, y_train, y_test = train_test_split(
|
|
156
|
+
|
|
157
|
+
datrum.iloc[:, 0:4],
|
|
158
|
+
|
|
159
|
+
datrum.iloc[:, 4],
|
|
160
|
+
|
|
161
|
+
test_size=0.3,
|
|
162
|
+
|
|
163
|
+
random_state=1
|
|
164
|
+
|
|
165
|
+
)
|
|
166
|
+
|
|
167
|
+
|
|
168
|
+
|
|
169
|
+
#データを標準化
|
|
170
|
+
|
|
171
|
+
scl = StandardScaler()
|
|
172
|
+
|
|
173
|
+
scl.fit(x_train) #学習用データで標準化
|
|
174
|
+
|
|
175
|
+
x_train_std = scl.transform(x_train)
|
|
176
|
+
|
|
177
|
+
x_test_std = scl.transform(x_test)
|
|
178
|
+
|
|
179
|
+
|
|
180
|
+
|
|
181
|
+
clf = LogisticRegression(C=1e5)
|
|
182
|
+
|
|
183
|
+
clf.fit(x_train_std, y_train)#訓練データから学習を行う
|
|
184
|
+
|
|
185
|
+
print( "正解率:{:.2f}%".format(accuracy_score(y_test, clf.predict(x_test_std)) * 100 ))
|
|
186
|
+
|
|
187
|
+
|
|
188
|
+
|
|
189
|
+
***
|
|
190
|
+
|
|
191
|
+
正解率:100.00%
|
|
192
|
+
|
|
193
|
+
***
|
|
194
|
+
|
|
195
|
+
|
|
196
|
+
|
|
197
|
+
print('回帰係数:', clf.coef_)
|
|
198
|
+
|
|
199
|
+
***
|
|
200
|
+
|
|
201
|
+
回帰係数: [[ -4.35525949 490.44187802 -1.42342501 1.0620863 ]]
|
|
202
|
+
|
|
203
|
+
***
|
|
204
|
+
|
|
205
|
+
print('切片:', clf.intercept_)
|
|
206
|
+
|
|
207
|
+
***
|
|
208
|
+
|
|
209
|
+
切片: [69.31399488]
|
|
210
|
+
|
|
211
|
+
***
|
|
212
|
+
|
|
213
|
+
|
|
214
|
+
|
|
215
|
+
x = datrum[['Amount', 'Profit', 'Quantity', 'Frequency']]
|
|
216
|
+
|
|
217
|
+
y = datrum['Customer Type']
|
|
218
|
+
|
|
219
|
+
print('決定係数:', clf.score(x, y))
|
|
220
|
+
|
|
221
|
+
|
|
222
|
+
|
|
223
|
+
***
|
|
224
|
+
|
|
225
|
+
決定係数: 0.998
|
|
226
|
+
|
|
227
|
+
***
|
|
228
|
+
|
|
229
|
+
|
|
230
|
+
|
|
231
|
+
#学習効果の検証
|
|
232
|
+
|
|
233
|
+
X_test = datrum.iloc[:, 0:4]
|
|
234
|
+
|
|
235
|
+
y_predict = clf.predict(X_test)
|
|
236
|
+
|
|
237
|
+
|
|
238
|
+
|
|
239
|
+
print('検証結果:', y_predict)
|
|
240
|
+
|
|
241
|
+
|
|
242
|
+
|
|
243
|
+
***
|
|
244
|
+
|
|
245
|
+
検証結果: [0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 1
|
|
246
|
+
|
|
247
|
+
1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0
|
|
248
|
+
|
|
249
|
+
0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
|
250
|
+
|
|
251
|
+
0 0 1 1 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
|
|
252
|
+
|
|
253
|
+
0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1
|
|
254
|
+
|
|
255
|
+
1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
|
|
256
|
+
|
|
257
|
+
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
|
|
258
|
+
|
|
259
|
+
1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
|
|
260
|
+
|
|
261
|
+
1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 0 0
|
|
262
|
+
|
|
263
|
+
1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1
|
|
264
|
+
|
|
265
|
+
1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1
|
|
266
|
+
|
|
267
|
+
1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1
|
|
268
|
+
|
|
269
|
+
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
|
|
270
|
+
|
|
271
|
+
1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1]
|
|
272
|
+
|
|
273
|
+
***
|
|
274
|
+
|
|
275
|
+
|
|
276
|
+
|
|
69
277
|
#Create an empty column
|
|
70
278
|
|
|
71
279
|
import numpy as np
|