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train.py
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train.py
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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import keras
from keras.models import Sequential
from keras.layers import Dense
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
dataset = pd.read_csv('Churn_Modelling.csv')
# Build matrix of features and matrix of target variable
# Exclude first two colums (0, 1)
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values
# Dynamically encode different labels
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
labelencoder_X_2 = LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
# Create dummy variable
onehotencoder = OneHotEncoder(categorical_features = [1])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]
# Split data into training and testing data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
# Standardize scaling
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Initialize neural network
classifier = Sequential()
# Add input layer and the first hidden layer
classifier.add(Dense(output_dim = 6, init = 'uniform', activation =
'relu', input_dim = 11))
# Add second hidden layer
classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu'))
# Add output layer
classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))
# Compile neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# fit the model
classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 100)
# Predict test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)
# Create the confusion matrix
cm = confusion_matrix(y_test, y_pred)