Hyperparameter_Tuning_NN.py

# -*- coding: utf-8 -*-
"""HyperParameter_for_NN.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1j8j0Jdi32aadwMA84qReIHVbLI6i0U65
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

df = pd.read_csv('/content/customer_churn_dataset-training-master.csv')

df.head()

df.shape

df.isnull().sum()

df = df.drop(['CustomerID'], axis=1)

df.head()

df = df.dropna()

df.isnull().sum()

df.info()

cat_col = []
num_col = []

for col in df.columns:
  if df[col].dtype == 'object':
    cat_col.append(col)
  else:
    num_col.append(col)

from sklearn.preprocessing import StandardScaler, LabelEncoder

label_encoders = {}

for col in cat_col:
    label_encoders[col] = LabelEncoder()
    df[col] = label_encoders[col].fit_transform(df[col])

label_encoders

df.head()

X = df.iloc[:,:-1]
X

y = df['Churn']
y

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)

SD = StandardScaler()

X_train = SD.fit_transform(X_train)
X_test = SD.transform(X_test)

!pip install keras-tuner

from tensorflow import keras
from tensorflow.keras import layers
import keras_tuner
from kerastuner.tuners import RandomSearch

def build_model(hp):
  model = keras.Sequential()
  for i in range(hp.Int('num_layers', 2,20)):
    model.add(layers.Dense(units = hp.Int('units_' + str(i),
                                          min_value = 16,
                                          max_value = 512,
                                          step = 32),
                           activation = 'relu'))
    model.add(layers.Dense(1, activation='sigmoid'))

    model.compile(
        optimizer = keras.optimizers.Adam(
            hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),
        loss = 'binary_crossentropy',
        metrics = ['accuracy'])

    return model

tuner = RandomSearch(
    build_model,
    objective = 'val_accuracy',
    max_trials = 5,
    executions_per_trial = 3,
    directory = '/content/Hyperparameter',
    project_name = 'HyperParameter_Tuning'
)

tuner.search_space_summary()

tuner.search(
    X_train,
    y_train,
    epochs = 10,
    validation_data = (X_test, y_test)
)

tuner.results_summary()

#This python code is helpful for hyperparameter tuning of NN.