Application of AutoKeras for Deep Learning: Notebook in Python for Beginners to Professionals

AutoKeras Project – A Guide to build a Classification Model in Python

AutoKeras for Beginners - A Guide to build a Classification Model in Python

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# Suppress warnings in Jupyter Notebooks
import warnings
warnings.filterwarnings("ignore")

import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')

import numpy as np
import pandas as pd
import tensorflow as tf
import autokeras as ak

print(ak.__version__)

import logging
tf.get_logger().setLevel(logging.ERROR)

1.0.16

In this notebook, we will learn how to build a Classification Model in Python using AutoKeras package.

Python Codes

Load Dataset (Titanic Dataset) and EDA

TRAIN_DATA_URL = "https://storage.googleapis.com/tf-datasets/titanic/train.csv"
TEST_DATA_URL = "https://storage.googleapis.com/tf-datasets/titanic/eval.csv"

train_file_path = tf.keras.utils.get_file("train.csv", TRAIN_DATA_URL)
test_file_path = tf.keras.utils.get_file("eval.csv", TEST_DATA_URL)

# x_train as pandas.DataFrame, y_train as pandas.Series
x_train = pd.read_csv(train_file_path)
print(type(x_train))  # pandas.DataFrame

y_train = x_train.pop("survived")
print(type(y_train))  # pandas.Series

# You can also use pandas.DataFrame for y_train.
y_train = pd.DataFrame(y_train)
print(type(y_train))  # pandas.DataFrame

# Preparing testing data.
x_test = pd.read_csv(test_file_path)
y_test = x_test.pop("survived")

<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.frame.DataFrame'>

print(); print(x_train.shape)
print(); print(x_train.head())
print(); print(y_train.shape)
print(); print(y_train.head())

(627, 9)

      sex   age  n_siblings_spouses  parch     fare  class     deck  \
0    male  22.0                   1      0   7.2500  Third  unknown   
1  female  38.0                   1      0  71.2833  First        C   
2  female  26.0                   0      0   7.9250  Third  unknown   
3  female  35.0                   1      0  53.1000  First        C   
4    male  28.0                   0      0   8.4583  Third  unknown   

   embark_town alone  
0  Southampton     n  
1    Cherbourg     n  
2  Southampton     y  
3  Southampton     n  
4   Queenstown     y  

(627, 1)

   survived
0         0
1         1
2         1
3         1
4         0

import pandas_profiling
x_train.profile_report()