first_ann_try.py

# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%
import numpy as np
import pandas as pd 
import keras
import matplotlib.pyplot as plt


# %%
# Load Dataset - Which gives data of a person with class label whether the customer will leave the bank or not
dataset = pd.read_csv('Bank_Customer_Prediction.csv')


# %%
# Differentiate class label y from entire dataset
x = dataset.iloc[:,3:13].values
y = dataset.iloc[:,13].values


# %%
from sklearn.preprocessing import LabelEncoder, OneHotEncoder


# %%
# Label Encoding for the dataset's categorical data (here geography column)
labelEncoder_X_1 = LabelEncoder()
x[:,1] = labelEncoder_X_1.fit_transform(x[:,1])


# %%
# Label Encoding for the dataset's categorical data (here gender column)
labelEncoder_X_2 = LabelEncoder()
x[:,2] = labelEncoder_X_2.fit_transform(x[:,2])


# %%
#One Hot Encoding for Geography column
from sklearn.compose import ColumnTransformer
col = ColumnTransformer(transformers = [('encoder', OneHotEncoder(),[1])],remainder='passthrough')
x = np.array(col.fit_transform(x))


# %%
# Splitting data into training and testing 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)


# %%
# Normalizing data / scaling data
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train  = sc.fit_transform(x_train)
x_test = sc.transform(x_test)


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


# %%
# building classifier blueprint.
classifier = Sequential()


# %%
# Input and First Hidden layer
classifier.add(Dense(units = 6, activation = 'relu'))


# %%
# Second Hidden layer
classifier.add(Dense(units = 6, activation = 'relu'))


# %%
# Third Hidden layer
classifier.add(Dense(units = 1, activation = 'sigmoid'))


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


# %%
# Training ANN
classifier.fit(x_train, y_train, batch_size = 32, epochs = 100)


# %%
# testing using test data
y_pred = classifier.predict(x_test)


# %%
# Setting a threshold of 0.5
y_pred = (y_pred>0.5)


# %%
# Final Comparison
final_comparison = np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1)


# %%
# print(final_comparison)


# %%
# building Confusion Matrix
from sklearn.metrics import confusion_matrix, accuracy_score
conf_mtx = confusion_matrix(y_test, y_pred)
# print(conf_mtx)
print("\n\nTesting Accuracy : ",accuracy_score(y_test, y_pred)*100,"%", "\n\n")


# %%
# Testing using a user provided new pattern
# print(classifier.predict(sc.transform([[1, 0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]])))