/Bank-Marketing-Classification-

Classification of Customers for bank marketing

Primary LanguageJupyter Notebook

Snippets of Neural Network Algorithm for Classification of customers for Bank Marketing

The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed

Objective : The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import warnings  
warnings.filterwarnings('ignore')
df = pd.read_csv('bank-additional-full.csv',sep=";")
df.head(20)
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age job marital education default housing loan contact month day_of_week ... campaign pdays previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed y
0 56 housemaid married basic.4y no no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
1 57 services married high.school unknown no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
2 37 services married high.school no yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
3 40 admin. married basic.6y no no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
4 56 services married high.school no no yes telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
5 45 services married basic.9y unknown no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
6 59 admin. married professional.course no no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
7 41 blue-collar married unknown unknown no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
8 24 technician single professional.course no yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
9 25 services single high.school no yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
10 41 blue-collar married unknown unknown no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
11 25 services single high.school no yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
12 29 blue-collar single high.school no no yes telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
13 57 housemaid divorced basic.4y no yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
14 35 blue-collar married basic.6y no yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
15 54 retired married basic.9y unknown yes yes telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
16 35 blue-collar married basic.6y no yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
17 46 blue-collar married basic.6y unknown yes yes telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
18 50 blue-collar married basic.9y no yes yes telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
19 39 management single basic.9y unknown no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no

20 rows × 21 columns

df['y'].value_counts()
no     36548
yes     4640
Name: y, dtype: int64

df.describe()
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age duration campaign pdays previous emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed
count 41188.00000 41188.000000 41188.000000 41188.000000 41188.000000 41188.000000 41188.000000 41188.000000 41188.000000 41188.000000
mean 40.02406 258.285010 2.567593 962.475454 0.172963 0.081886 93.575664 -40.502600 3.621291 5167.035911
std 10.42125 259.279249 2.770014 186.910907 0.494901 1.570960 0.578840 4.628198 1.734447 72.251528
min 17.00000 0.000000 1.000000 0.000000 0.000000 -3.400000 92.201000 -50.800000 0.634000 4963.600000
25% 32.00000 102.000000 1.000000 999.000000 0.000000 -1.800000 93.075000 -42.700000 1.344000 5099.100000
50% 38.00000 180.000000 2.000000 999.000000 0.000000 1.100000 93.749000 -41.800000 4.857000 5191.000000
75% 47.00000 319.000000 3.000000 999.000000 0.000000 1.400000 93.994000 -36.400000 4.961000 5228.100000
max 98.00000 4918.000000 56.000000 999.000000 7.000000 1.400000 94.767000 -26.900000 5.045000 5228.100000 
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 41188 entries, 0 to 41187
Data columns (total 21 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   age             41188 non-null  int64  
 1   job             41188 non-null  object 
 2   marital         41188 non-null  object 
 3   education       41188 non-null  object 
 4   default         41188 non-null  object 
 5   housing         41188 non-null  object 
 6   loan            41188 non-null  object 
 7   contact         41188 non-null  object 
 8   month           41188 non-null  object 
 9   day_of_week     41188 non-null  object 
 10  duration        41188 non-null  int64  
 11  campaign        41188 non-null  int64  
 12  pdays           41188 non-null  int64  
 13  previous        41188 non-null  int64  
 14  poutcome        41188 non-null  object 
 15  emp.var.rate    41188 non-null  float64
 16  cons.price.idx  41188 non-null  float64
 17  cons.conf.idx   41188 non-null  float64
 18  euribor3m       41188 non-null  float64
 19  nr.employed     41188 non-null  float64
 20  y               41188 non-null  object 
dtypes: float64(5), int64(5), object(11)
memory usage: 6.6+ MB

df.columns
Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',
       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',
       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',
       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],
      dtype='object')

df['y'].value_counts()
no     36548
yes     4640
Name: y, dtype: int64

Preprocessing the data

df["job"] = df["job"].astype('category')
df["marital"] = df["marital"].astype('category')
df["education"] = df["education"].astype('category')
df["default"] = df["default"].astype('category')
df["housing"] = df["housing"].astype('category')
df["loan"] = df["loan"].astype('category')
df["contact"] = df["contact"].astype('category')
df["month"] = df["month"].astype('category')
df["day_of_week"] = df["day_of_week"].astype('category')
df["poutcome"] = df["poutcome"].astype('category')
df["y"] = df["y"].astype('category')
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 41188 entries, 0 to 41187
Data columns (total 21 columns):
 #   Column          Non-Null Count  Dtype   
---  ------          --------------  -----   
 0   age             41188 non-null  int64   
 1   job             41188 non-null  category
 2   marital         41188 non-null  category
 3   education       41188 non-null  category
 4   default         41188 non-null  category
 5   housing         41188 non-null  category
 6   loan            41188 non-null  category
 7   contact         41188 non-null  category
 8   month           41188 non-null  category
 9   day_of_week     41188 non-null  category
 10  duration        41188 non-null  int64   
 11  campaign        41188 non-null  int64   
 12  pdays           41188 non-null  int64   
 13  previous        41188 non-null  int64   
 14  poutcome        41188 non-null  category
 15  emp.var.rate    41188 non-null  float64 
 16  cons.price.idx  41188 non-null  float64 
 17  cons.conf.idx   41188 non-null  float64 
 18  euribor3m       41188 non-null  float64 
 19  nr.employed     41188 non-null  float64 
 20  y               41188 non-null  category
dtypes: category(11), float64(5), int64(5)
memory usage: 3.6 MB

from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
df['y'] = labelencoder.fit_transform(df['y'])

Upsampling the dependent variable

from sklearn.utils import resample

# Separate majority and minority classes
df_majority = df[df.y==0]
df_minority = df[df.y==1]
 
# Upsample minority class
df_minority_upsampled = resample(df_minority, 
                                 replace=True,     # sample with replacement
                                 n_samples=36548,    # to match majority class
                                 random_state=123) # reproducible results
 
# Combine majority class with upsampled minority class
df_upsampled = pd.concat([df_majority, df_minority_upsampled])
 
# Display new class counts
df_upsampled.y.value_counts()
1    36548
0    36548
Name: y, dtype: int64

df_upsampled
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age job marital education default housing loan contact month day_of_week ... campaign pdays previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed y
0 56 housemaid married basic.4y no no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 0
1 57 services married high.school unknown no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 0
2 37 services married high.school no yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 0
3 40 admin. married basic.6y no no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 0
4 56 services married high.school no no yes telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
36577 33 management divorced university.degree no yes no cellular jun wed ... 1 999 0 nonexistent -2.9 92.963 -40.8 1.260 5076.2 1
37277 38 admin. single high.school no yes yes cellular aug mon ... 1 999 0 nonexistent -2.9 92.201 -31.4 0.884 5076.2 1
9157 52 self-employed married basic.4y unknown no yes telephone jun fri ... 2 999 0 nonexistent 1.4 94.465 -41.8 4.967 5228.1 1
36369 25 admin. single university.degree no no no cellular jun tue ... 1 999 0 nonexistent -2.9 92.963 -40.8 1.262 5076.2 1
40263 32 admin. single university.degree no no no cellular jul tue ... 1 999 0 nonexistent -1.7 94.215 -40.3 0.893 4991.6 1

73096 rows × 21 columns

df1 = df.drop(columns = ['y'])
features = ['age','duration','campaign','pdays','previous','emp.var.rate','cons.price.idx','cons.conf.idx','euribor3m','nr.employed']
from sklearn.preprocessing import StandardScaler,Normalizer
sc = StandardScaler()
Norm = Normalizer()
df1 = sc.fit_transform(df_upsampled[features])
#X = Norm.fit_transform(X[features])
df_upsampled.dtypes
age                  int64
job               category
marital           category
education         category
default           category
housing           category
loan              category
contact           category
month             category
day_of_week       category
duration             int64
campaign             int64
pdays                int64
previous             int64
poutcome          category
emp.var.rate       float64
cons.price.idx     float64
cons.conf.idx      float64
euribor3m          float64
nr.employed        float64
y                    int32
dtype: object

X = df1
X
array([[ 1.30597253, -0.35023047, -0.56714014, ...,  0.71345043,
         1.00007526,  0.6361662 ],
       [ 1.38947829, -0.65995294, -0.56714014, ...,  0.71345043,
         1.00007526,  0.6361662 ],
       [-0.28063699, -0.44701874, -0.56714014, ...,  0.71345043,
         1.00007526,  0.6361662 ],
       ...,
       [ 0.97194947, -0.01838495, -0.14484697, ..., -0.29610014,
         1.05829719,  1.06349164],
       [-1.28270616,  0.0811687 , -0.56714014, ..., -0.10914633,
        -0.90272338, -0.68612382],
       [-0.69816581,  0.38536042, -0.56714014, ..., -0.01566943,
        -1.0980315 , -1.66056403]])

y = df_upsampled.iloc[:,-1]
y
0        0
1        0
2        0
3        0
4        0
        ..
36577    1
37277    1
9157     1
36369    1
40263    1
Name: y, Length: 73096, dtype: int32

from sklearn.preprocessing import OneHotEncoder
onehotencoder = OneHotEncoder(handle_unknown='ignore')
X = onehotencoder.fit_transform(X).toarray()
X
array([[1., 0., 1., ..., 0., 1., 0.],
       [1., 0., 1., ..., 0., 1., 0.],
       [1., 0., 1., ..., 0., 1., 0.],
       ...,
       [1., 0., 1., ..., 0., 0., 1.],
       [1., 0., 1., ..., 0., 1., 0.],
       [1., 0., 1., ..., 0., 1., 0.]])

from sklearn.decomposition import PCA
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(X)
principalDf = pd.DataFrame(data = principalComponents, columns = ['principal component 1', 'principal component 2'])
X1 = principalDf
X1
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principal component 1 principal component 2
0 0.309215 2.643028
1 0.303732 2.641658
2 0.313466 2.647056
3 0.304437 2.645482
4 0.309007 2.642508
... ... ...
73091 0.294339 0.282391
73092 0.302462 0.284689
73093 -1.613393 -0.368437
73094 0.290398 0.284411
73095 0.257403 0.219830

73096 rows × 2 columns

#from sklearn.preprocessing import LabelEncoder
#labelencoder = LabelEncoder()
#y = labelencoder.fit_transform(y)
y
0        0
1        0
2        0
3        0
4        0
        ..
36577    1
37277    1
9157     1
36369    1
40263    1
Name: y, Length: 73096, dtype: int32

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X1, y, test_size = 0.3, random_state = 0)
y_train.shape[0]
51167

X_train.shape[0]
51167

import keras
from keras.models import Sequential
from keras.layers import Dense
# Initialising the ANN
classifier = Sequential()
# Relu
classifier.add(Dense(units = 64, kernel_initializer = 'uniform', activation = 'relu', input_dim = 2))
# Adding the second hidden layer
classifier.add(Dense(units = 64, kernel_initializer = 'uniform', activation = 'relu'))

# Adding the output layer
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
# Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
#Relu activation function Model fit
classifier_fit = classifier.fit(X_train, y_train,validation_data=(X_test,y_test), batch_size = 128, epochs = 100)
Train on 51167 samples, validate on 21929 samples
Epoch 1/100
51167/51167 [==============================] - 1s 14us/step - loss: 0.6066 - accuracy: 0.6958 - val_loss: 0.5812 - val_accuracy: 0.6988
Epoch 2/100
51167/51167 [==============================] - 0s 10us/step - loss: 0.5804 - accuracy: 0.7062 - val_loss: 0.5764 - val_accuracy: 0.7070
Epoch 3/100
51167/51167 [==============================] - 0s 9us/step - loss: 0.5782 - accuracy: 0.7076 - val_loss: 0.5749 - val_accuracy: 0.7091
Epoch 4/100
51167/51167 [==============================] - 0s 9us/step - loss: 0.5766 - accuracy: 0.7084 - val_loss: 0.5770 - val_accuracy: 0.7087
Epoch 5/100
51167/51167 [==============================] - 0s 9us/step - loss: 0.5743 - accuracy: 0.7085 - val_loss: 0.5715 - val_accuracy: 0.7066
Epoch 6/100
51167/51167 [==============================] - 0s 10us/step - loss: 0.5722 - accuracy: 0.7076 - val_loss: 0.5689 - val_accuracy: 0.7072
Epoch 7/100
51167/51167 [==============================] - 0s 9us/step - loss: 0.5689 - accuracy: 0.7088 - val_loss: 0.5660 - val_accuracy: 0.7087
Epoch 8/100
51167/51167 [==============================] - 0s 9us/step - loss: 0.5653 - accuracy: 0.7108 - val_loss: 0.5637 - val_accuracy: 0.7133
Epoch 9/100
51167/51167 [==============================] - 0s 10us/step - loss: 0.5624 - accuracy: 0.7181 - val_loss: 0.5606 - val_accuracy: 0.7227
Epoch 10/100
51167/51167 [==============================] - 0s 10us/step - loss: 0.5601 - accuracy: 0.7215 - val_loss: 0.5626 - val_accuracy: 0.7270
Epoch 11/100
51167/51167 [==============================] - 0s 9us/step - loss: 0.5583 - accuracy: 0.7239 - val_loss: 0.5572 - val_accuracy: 0.7223
Epoch 12/100
51167/51167 [==============================] - 0s 9us/step - loss: 0.5577 - accuracy: 0.7243 - val_loss: 0.5563 - val_accuracy: 0.7225
Epoch 13/100
51167/51167 [==============================] - 1s 10us/step - loss: 0.5565 - accuracy: 0.7254 - val_loss: 0.5567 - val_accuracy: 0.7232
Epoch 14/100
51167/51167 [==============================] - 0s 9us/step - loss: 0.5561 - accuracy: 0.7244 - val_loss: 0.5579 - val_accuracy: 0.7281
Epoch 15/100
51167/51167 [==============================] - 1s 10us/step - loss: 0.5557 - accuracy: 0.7253 - val_loss: 0.5579 - val_accuracy: 0.7258
Epoch 16/100
51167/51167 [==============================] - 0s 10us/step - loss: 0.5548 - accuracy: 0.7270 - val_loss: 0.5562 - val_accuracy: 0.7295
Epoch 17/100
51167/51167 [==============================] - 0s 10us/step - loss: 0.5548 - accuracy: 0.7273 - val_loss: 0.5545 - val_accuracy: 0.7249
Epoch 18/100
51167/51167 [==============================] - 0s 10us/step - loss: 0.5546 - accuracy: 0.7277 - val_loss: 0.5553 - val_accuracy: 0.7237
Epoch 19/100
51167/51167 [==============================] - 0s 10us/step - loss: 0.5543 - accuracy: 0.7282 - val_loss: 0.5546 - val_accuracy: 0.7267
Epoch 20/100
51167/51167 [==============================] - 1s 10us/step - loss: 0.5546 - accuracy: 0.7293 - val_loss: 0.5554 - val_accuracy: 0.7277
Epoch 21/100
51167/51167 [==============================] - 1s 10us/step - loss: 0.5544 - accuracy: 0.7290 - val_loss: 0.5559 - val_accuracy: 0.7257
Epoch 22/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5543 - accuracy: 0.7294 - val_loss: 0.5543 - val_accuracy: 0.7297
Epoch 23/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5542 - accuracy: 0.7291 - val_loss: 0.5544 - val_accuracy: 0.7302
Epoch 24/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5540 - accuracy: 0.7286 - val_loss: 0.5585 - val_accuracy: 0.7203
Epoch 25/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5542 - accuracy: 0.7294 - val_loss: 0.5538 - val_accuracy: 0.7272
Epoch 26/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5535 - accuracy: 0.7283 - val_loss: 0.5549 - val_accuracy: 0.7287
Epoch 27/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5537 - accuracy: 0.7299 - val_loss: 0.5544 - val_accuracy: 0.7302
Epoch 28/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5531 - accuracy: 0.7304 - val_loss: 0.5547 - val_accuracy: 0.7257
Epoch 29/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5534 - accuracy: 0.7299 - val_loss: 0.5539 - val_accuracy: 0.7304
Epoch 30/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5537 - accuracy: 0.7304 - val_loss: 0.5532 - val_accuracy: 0.7304
Epoch 31/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5529 - accuracy: 0.7315 - val_loss: 0.5538 - val_accuracy: 0.7304
Epoch 32/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5530 - accuracy: 0.7308 - val_loss: 0.5528 - val_accuracy: 0.7298
Epoch 33/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5527 - accuracy: 0.7309 - val_loss: 0.5539 - val_accuracy: 0.7294
Epoch 34/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5527 - accuracy: 0.7312 - val_loss: 0.5539 - val_accuracy: 0.7276
Epoch 35/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5523 - accuracy: 0.7311 - val_loss: 0.5539 - val_accuracy: 0.7309
Epoch 36/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5523 - accuracy: 0.7312 - val_loss: 0.5554 - val_accuracy: 0.7242
Epoch 37/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5521 - accuracy: 0.7317 - val_loss: 0.5539 - val_accuracy: 0.7275
Epoch 38/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5522 - accuracy: 0.7311 - val_loss: 0.5525 - val_accuracy: 0.7308
Epoch 39/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5524 - accuracy: 0.7305 - val_loss: 0.5523 - val_accuracy: 0.7296
Epoch 40/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5522 - accuracy: 0.7310 - val_loss: 0.5531 - val_accuracy: 0.7307
Epoch 41/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5519 - accuracy: 0.7316 - val_loss: 0.5527 - val_accuracy: 0.7302
Epoch 42/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5519 - accuracy: 0.7317 - val_loss: 0.5535 - val_accuracy: 0.7285
Epoch 43/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5523 - accuracy: 0.7314 - val_loss: 0.5539 - val_accuracy: 0.7304
Epoch 44/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5519 - accuracy: 0.7315 - val_loss: 0.5550 - val_accuracy: 0.7295
Epoch 45/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5520 - accuracy: 0.7316 - val_loss: 0.5521 - val_accuracy: 0.7308
Epoch 46/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5518 - accuracy: 0.7314 - val_loss: 0.5523 - val_accuracy: 0.7287
Epoch 47/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5518 - accuracy: 0.7319 - val_loss: 0.5521 - val_accuracy: 0.7294
Epoch 48/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5514 - accuracy: 0.7323 - val_loss: 0.5528 - val_accuracy: 0.7279
Epoch 49/100
51167/51167 [==============================] - 1s 14us/step - loss: 0.5517 - accuracy: 0.7315 - val_loss: 0.5527 - val_accuracy: 0.7312
Epoch 50/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5517 - accuracy: 0.7321 - val_loss: 0.5532 - val_accuracy: 0.7312
Epoch 51/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5519 - accuracy: 0.7315 - val_loss: 0.5547 - val_accuracy: 0.7314
Epoch 52/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5518 - accuracy: 0.7311 - val_loss: 0.5518 - val_accuracy: 0.7299
Epoch 53/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5517 - accuracy: 0.7320 - val_loss: 0.5518 - val_accuracy: 0.7287
Epoch 54/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5517 - accuracy: 0.7317 - val_loss: 0.5526 - val_accuracy: 0.7306
Epoch 55/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5514 - accuracy: 0.7315 - val_loss: 0.5551 - val_accuracy: 0.7223
Epoch 56/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5512 - accuracy: 0.7325 - val_loss: 0.5519 - val_accuracy: 0.7284
Epoch 57/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5512 - accuracy: 0.7311 - val_loss: 0.5531 - val_accuracy: 0.7306
Epoch 58/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5513 - accuracy: 0.7324 - val_loss: 0.5519 - val_accuracy: 0.7291
Epoch 59/100
51167/51167 [==============================] - 1s 15us/step - loss: 0.5512 - accuracy: 0.7321 - val_loss: 0.5546 - val_accuracy: 0.7250
Epoch 60/100
51167/51167 [==============================] - 1s 15us/step - loss: 0.5514 - accuracy: 0.7324 - val_loss: 0.5513 - val_accuracy: 0.7307
Epoch 61/100
51167/51167 [==============================] - 1s 15us/step - loss: 0.5513 - accuracy: 0.7318 - val_loss: 0.5510 - val_accuracy: 0.7299
Epoch 62/100
51167/51167 [==============================] - 1s 15us/step - loss: 0.5511 - accuracy: 0.7317 - val_loss: 0.5532 - val_accuracy: 0.7273
Epoch 63/100
51167/51167 [==============================] - 1s 14us/step - loss: 0.5513 - accuracy: 0.7316 - val_loss: 0.5516 - val_accuracy: 0.7311
Epoch 64/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5512 - accuracy: 0.7328 - val_loss: 0.5512 - val_accuracy: 0.7301
Epoch 65/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5510 - accuracy: 0.7323 - val_loss: 0.5530 - val_accuracy: 0.7305
Epoch 66/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5508 - accuracy: 0.7323 - val_loss: 0.5509 - val_accuracy: 0.7300
Epoch 67/100
51167/51167 [==============================] - 1s 14us/step - loss: 0.5509 - accuracy: 0.7322 - val_loss: 0.5511 - val_accuracy: 0.7312
Epoch 68/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5507 - accuracy: 0.7325 - val_loss: 0.5516 - val_accuracy: 0.7298
Epoch 69/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5507 - accuracy: 0.7325 - val_loss: 0.5508 - val_accuracy: 0.7312
Epoch 70/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5507 - accuracy: 0.7321 - val_loss: 0.5509 - val_accuracy: 0.7311
Epoch 71/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5508 - accuracy: 0.7326 - val_loss: 0.5512 - val_accuracy: 0.7308
Epoch 72/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5507 - accuracy: 0.7332 - val_loss: 0.5530 - val_accuracy: 0.7275
Epoch 73/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5508 - accuracy: 0.7321 - val_loss: 0.5510 - val_accuracy: 0.7325
Epoch 74/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5507 - accuracy: 0.7328 - val_loss: 0.5509 - val_accuracy: 0.7310
Epoch 75/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5505 - accuracy: 0.7328 - val_loss: 0.5524 - val_accuracy: 0.7303
Epoch 76/100
51167/51167 [==============================] - 1s 14us/step - loss: 0.5506 - accuracy: 0.7335 - val_loss: 0.5523 - val_accuracy: 0.7306
Epoch 77/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5503 - accuracy: 0.7335 - val_loss: 0.5504 - val_accuracy: 0.7322
Epoch 78/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5502 - accuracy: 0.7330 - val_loss: 0.5501 - val_accuracy: 0.7321
Epoch 79/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5501 - accuracy: 0.7322 - val_loss: 0.5517 - val_accuracy: 0.7316
Epoch 80/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5499 - accuracy: 0.7331 - val_loss: 0.5501 - val_accuracy: 0.7321
Epoch 81/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5501 - accuracy: 0.7332 - val_loss: 0.5512 - val_accuracy: 0.7320
Epoch 82/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5500 - accuracy: 0.7339 - val_loss: 0.5497 - val_accuracy: 0.7315
Epoch 83/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5498 - accuracy: 0.7331 - val_loss: 0.5520 - val_accuracy: 0.7250
Epoch 84/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5497 - accuracy: 0.7335 - val_loss: 0.5504 - val_accuracy: 0.7310
Epoch 85/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5498 - accuracy: 0.7335 - val_loss: 0.5501 - val_accuracy: 0.7311
Epoch 86/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5495 - accuracy: 0.7340 - val_loss: 0.5498 - val_accuracy: 0.7309
Epoch 87/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5493 - accuracy: 0.7346 - val_loss: 0.5506 - val_accuracy: 0.7325
Epoch 88/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5493 - accuracy: 0.7345 - val_loss: 0.5494 - val_accuracy: 0.7316
Epoch 89/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5498 - accuracy: 0.7337 - val_loss: 0.5496 - val_accuracy: 0.7322
Epoch 90/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5493 - accuracy: 0.7344 - val_loss: 0.5512 - val_accuracy: 0.7238
Epoch 91/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5494 - accuracy: 0.7339 - val_loss: 0.5508 - val_accuracy: 0.7291
Epoch 92/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5495 - accuracy: 0.7336 - val_loss: 0.5502 - val_accuracy: 0.7303
Epoch 93/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5492 - accuracy: 0.7339 - val_loss: 0.5488 - val_accuracy: 0.7327
Epoch 94/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5493 - accuracy: 0.7348 - val_loss: 0.5497 - val_accuracy: 0.7315
Epoch 95/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5493 - accuracy: 0.7347 - val_loss: 0.5497 - val_accuracy: 0.7312
Epoch 96/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5494 - accuracy: 0.7339 - val_loss: 0.5494 - val_accuracy: 0.7348
Epoch 97/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5493 - accuracy: 0.7347 - val_loss: 0.5490 - val_accuracy: 0.7324
Epoch 98/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5490 - accuracy: 0.7340 - val_loss: 0.5491 - val_accuracy: 0.7329
Epoch 99/100
51167/51167 [==============================] - 1s 12us/step - loss: 0.5491 - accuracy: 0.7348 - val_loss: 0.5497 - val_accuracy: 0.7326
Epoch 100/100
51167/51167 [==============================] - 1s 13us/step - loss: 0.5489 - accuracy: 0.7354 - val_loss: 0.5509 - val_accuracy: 0.7320

# list all data in history
print(classifier_fit.history.keys())
dict_keys(['val_loss', 'val_accuracy', 'loss', 'accuracy'])

# summarize history for accuracy
plt.plot(classifier_fit.history['accuracy'])
plt.plot(classifier_fit.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

png

# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix,classification_report
cm = confusion_matrix(y_test, y_pred)
print(cm)
[[9332 1513]
 [4363 6721]]

target_names = ['0', '1']
print(classification_report(y_test, y_pred,target_names=target_names))
print(cm)
              precision    recall  f1-score   support

           0       0.68      0.86      0.76     10845
           1       0.82      0.61      0.70     11084

    accuracy                           0.73     21929
   macro avg       0.75      0.73      0.73     21929
weighted avg       0.75      0.73      0.73     21929

[[9332 1513]
 [4363 6721]]

from sklearn.metrics import roc_curve,auc
#from sklearn import metrics
def roc_auc(y_test,y_pred):
    sns.set()
    fpr, tpr, thresholds = roc_curve(y_test, y_pred)
    roc_auc = auc(fpr,tpr)
    plt.title('ROC Curve')
    plt.plot(fpr, tpr, 'b',label='AUC = %0.3f'% roc_auc)
    plt.legend()
    plt.plot([0,1],[0,1],'r--')
    plt.xlim([-0.1,1.0])
    plt.ylim([-0.1,1.01])
    plt.ylabel('True Positive Rate')
    plt.xlabel('False Positive Rate')
    plt.show()
roc_auc(y_pred,y_test)

png