annpy.py

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13 ].values

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
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])
onehotencoder = OneHotEncoder(categorical_features = [1])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]

# Splitting the dataset into the Training set and Test set
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, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

#Importing KERAS
import keras

from keras.models import Sequential
from keras.layers import Dense

classifier = Sequential()
 
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))

#Output Lyer
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))

#Compilation
classifier.compile(optimizer = 'adam', loss='binary_crossentropy',metrics= ['accuracy'])

classifier.fit(X_train,y_train, batch_size=10, epochs=100)



#Prediction
y_pred=classifier.predict(X_test)
y_pred = (y_pred > 0.5)

new_prediction = classifier.predict(sc.transform(np.array([[0.0,0,600,1,40,3,60000,2,1,1,50000]])))
new_prediction = (new_prediction > 0.5)

#Making the confusion matrix
from sklearn.metrics import confusion_matrix
cm=confusion_matrix(y_test,y_pred)