1. Introduction to Artificial Intelligence

Activity 1.01: Generating All Possible Sequences of Steps in a Tic-Tac-Toe Game

Solution:

The following steps will help you to complete this activity:

1. Open a new Jupyter Notebook file.
2. Reuse the function codes of Steps 2–9 from the previous, Exercise 1.02, Creating an AI with Random Behavior for the Tic-Tac-Toe Game.
3. Create a function that maps the all_moves_from_board_list function to each element of a list of boards. This way, we will have all of the nodes of a decision tree in each depth:

   def all_moves_from_board_list(board_list, sign):
       move_list = []
       for board in board_list:
           move_list.extend(all_moves_from_board(board, sign))
       return move_list

   In the preceding code snippet, we have defined the all_moves_from_board function, which will enumerate all the possible moves from the board and add the...

2. An Introduction to Regression

Activity 2.01: Boston House Price Prediction with Polynomial Regression of Degrees 1, 2, and 3 on Multiple Variables

Solution:

1. Open a Jupyter Notebook.
2. Import the required packages and load the Boston House Prices data from sklearn into a DataFrame:

   import numpy as np
   import pandas as pd
   from sklearn import preprocessing
   from sklearn import model_selection
   from sklearn import linear_model
   from sklearn.preprocessing import PolynomialFeatures
   file_url = 'https://raw.githubusercontent.com/'\
              'PacktWorkshops/'\
              'The-Applied-Artificial-Intelligence-Workshop/'\
              'master/Datasets/boston_house_price.csv'
   df = pd.read_csv(file_url)

   The output of df is as follows:

   Figure 2.28: Output displaying the dataset

   Earlier in...

3. An Introduction to Classification

Activity 3.01: Increasing the Accuracy of Credit Scoring

Solution:

1. Open a new Jupyter Notebook file and execute all the steps from the previous exercise, Exercise 3.04, K-Nearest Neighbors Classification in Scikit-Learn.
2. Import neighbors from sklearn:

   from sklearn import neighbors

3. Create a function called fit_knn that takes the following parameters: k, p, features_train, label_train, features_test, and label_test. This function will fit KNeighborsClassifier with the training set and print the accuracy score for the training and testing sets, as shown in the following code snippet:

   def fit_knn(k, p, features_train, label_train, \
               features_test, label_test):
       classifier = neighbors.KNeighborsClassifier(n_neighbors=k, p=p)
       classifier.fit(features_train, label_train)
       return classifier.score...

4. An Introduction to Decision Trees

Activity 4.01: Car Data Classification

Solution:

1. Open a new Jupyter Notebook file.
2. Import the pandas package as pd:

   import pandas as pd

3. Create a new variable called file_url that will contain the URL to the raw dataset:

   file_url = 'https://raw.githubusercontent.com/'\
              'PacktWorkshops/'\
              'The-Applied-Artificial-Intelligence-Workshop/'\
              'master/Datasets/car.csv'

4. Load the data using the pd.read_csv() method.:

   df = pd.read_csv(file_url) 

5. Print the first five rows of df:

   df.head()

   The output will be as follows:

   Figure 4.13: The first five rows of the dataset

6. Import the preprocessing module from sklearn:

   from sklearn import preprocessing

7. Create a function called encode() that takes a DataFrame...

5. Artificial Intelligence: Clustering

Activity 5.01: Clustering Sales Data Using K-Means

Solution:

1. Open a new Jupyter Notebook file.
2. Load the dataset as a DataFrame and inspect the data:

   import pandas as pd
   file_url = 'https://raw.githubusercontent.com/'\
              'PacktWorkshops/'\
              'The-Applied-Artificial-Intelligence-Workshop/'\
              'master/Datasets/'\
              'Sales_Transactions_Dataset_Weekly.csv'
   df = pd.read_csv(file_url)
   df

   The output of df is as follows:

   Figure 5.18: Output showing the contents of the dataset

   If you look at the output, you will notice that our dataset contains 811 rows, with each row representing a product. It also contains 107 columns, with the first column...

6. Neural Networks and Deep Learning

Activity 6.01: Finding the Best Accuracy Score for the Digits Dataset

Solution:

1. Open a new Jupyter Notebook file.
2. Import tensorflow.keras.datasets.mnist as mnist:

   import tensorflow.keras.datasets.mnist as mnist

3. Load the mnist dataset using mnist.load_data() and save the results into (features_train, label_train), (features_test, label_test):

   (features_train, label_train), \
   (features_test, label_test) = mnist.load_data()

4. Print the content of label_train:

   label_train

   The expected output is this:

   array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)

   The label column contains numeric values that correspond to the 10 handwritten digits: 0 to 9.

5. Print the shape of the training set:

   features_train.shape

   The expected output is this:

   (60000, 28, 28)

   The training set is composed of 60,000 observations of shape 28 by 28. We will need to flatten the input for our neural network.

6. Print the shape of the testing set:

   features_test.shape

   The expected...