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Applied Supervised Learning with Python

Applied Supervised Learning with Python

By : Benjamin Johnston, Ishita Mathur
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Applied Supervised Learning with Python

Applied Supervised Learning with Python

5 (1)
By: Benjamin Johnston, Ishita Mathur
Buy this Book

Overview of this book

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!
Table of Contents (9 chapters)
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Applied Supervised Learning with Python
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Applied Supervised Learning with Python
Preface
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Preface
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1
Python Machine Learning Toolkit
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Python Machine Learning Toolkit
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Introduction
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Supervised Machine Learning
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Jupyter Notebooks
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pandas
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Data Quality Considerations
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Summary
2
Exploratory Data Analysis and Visualization
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Exploratory Data Analysis and Visualization
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Introduction
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Summary Statistics and Central Values
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Missing Values
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Distribution of Values
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Relationships within the Data
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Summary
3
Regression Analysis
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Regression Analysis
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Introduction
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Regression and Classification Problems
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Linear Regression
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Multiple Linear Regression
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Autoregression Models
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Summary
4
Classification
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Classification
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Introduction
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Linear Regression as a Classifier
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Logistic Regression
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Classification Using K-Nearest Neighbors
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Classification Using Decision Trees
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Summary
5
Ensemble Modeling
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Ensemble Modeling
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Introduction
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Overfitting and Underfitting
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Bagging
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Boosting
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Summary
6
Model Evaluation
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Model Evaluation
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Introduction
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Evaluation Metrics
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Splitting the Dataset
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Performance Improvement Tactics
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Summary
1
Appendix
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Appendix
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Chapter 1: Python Machine Learning Toolkit
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Chapter 2: Exploratory Data Analysis and Visualization
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Chapter 3: Regression Analysis
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Chapter 4: Classification
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Chapter 5: Ensemble Modeling
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Chapter 6: Model Evaluation

Chapter 1: Python Machine Learning Toolkit

Activity 1: pandas Functions

Solution

  1. Open a new Jupyter notebook.

  2. Use pandas to load the Titanic dataset:

    import pandas as pd

df = pd.read_csv('titanic.csv')

    Use the head() function on the dataset as follows:

    # Have a look at the first 5 sample of the data
df.head()

    The output will be as follows:

    Figure 1.65: First five rows

    Use the describe function as follows:

    df.describe(include='all')

    The output will be as follows:

    Figure 1.66: Output of describe()

  3. We don't need the Unnamed: 0 column. We can remove the column without using the del command, as follows:

    df = df[df.columns[1:]] # Use the columns
df.head()

    The output will be as follows:

    Figure 1.67: First five rows after deleting the Unnamed: 0 column

  4. Compute the mean, standard deviation, minimum, and maximum values for the columns of the DataFrame without using describe:

    df.mean()

Fare        33.295479
Pclass       2.294882
Age         29.881138
Parch        0.385027
SibSp        0.498854
Survived     0.383838
dtype: float64


df.std()

Fare        51.758668
Pclass       0.837836
Age         14.413493
Parch        0.865560
SibSp        1.041658
Survived     0.486592
dtype: float64


df.min()

Fare        0.00
Pclass      1.00
Age         0.17
Parch       0.00
SibSp       0.00
Survived    0.00
dtype: float64


df.max()

Fare        512.3292
Pclass        3.0000
Age          80.0000
Parch         9.0000
SibSp         8.0000
Survived      1.0000
dtype: float64

  5. What about the 33, 66, and 99% quartiles? Use the quantile method as follows:

    df.quantile(0.33)

Fare         8.559325
Pclass       2.000000
Age         23.000000
Parch        0.000000
SibSp        0.000000
Survived     0.000000
Name: 0.33, dtype: float64

df.quantile(0.66)

Fare        26.0
Pclass       3.0
Age         34.0
Parch        0.0
SibSp        0.0
Survived     1.0
Name: 0.66, dtype: float64


df.quantile(0.99)

Fare        262.375
Pclass        3.000
Age          65.000
Parch         4.000
SibSp         5.000
Survived      1.000
Name: 0.99, dtype: float64

  6. How many passengers were from each class? Let's see, using the groupby method:

    class_groups = df.groupby('Pclass')
for name, index in class_groups:
    print(f'Class: {name}: {len(index)}')

Class: 1: 323
Class: 2: 277
Class: 3: 709

  7. How many passengers were from each class? You can find the answer by using selecting/indexing methods to count the members of each class:

    for clsGrp in df.Pclass.unique():
    num_class = len(df[df.Pclass == clsGrp])
    print(f'Class {clsGrp}: {num_class}')

Class 3: 709
Class 1: 323
Class 2: 277

    The answers to Step 6 and Step 7 do match.

  8. Determine who the eldest passenger in third class was:

    third_class = df.loc[(df.Pclass == 3)]

third_class.loc[(third_class.Age == third_class.Age.max())]

    The output will be as follows:

    Figure 1.68: Eldest passenger in third class

  9. For a number of machine learning problems, it is very common to scale the numerical values between 0 and 1. Use the agg method with Lambda functions to scale the Fare and Age columns between 0 and 1:

    fare_max = df.Fare.max()
age_max = df.Age.max()

df.agg({
    'Fare': lambda x: x / fare_max, 
    'Age': lambda x: x / age_max,
}).head()

    The output will be as follows:

    Figure 1.69: Scaling numerical values between 0 and 1

  10. There is one individual in the dataset without a listed Fare value:

    df_nan_fare = df.loc[(df.Fare.isna())]
df_nan_fare

    This is the output:

    Figure 1.70: Individual without a listed Fare value

    Replace the NaN values of this row in the main DataFrame with the mean Fare value for those corresponding with the same class and Embarked location using the groupby method:

    embarked_class_groups = df.groupby(['Embarked', 'Pclass'])

indices = embarked_class_groups.groups[(df_nan_fare.Embarked.values[0], df_nan_fare.Pclass.values[0])]
mean_fare = df.iloc[indices].Fare.mean()
df.loc[(df.index == 1043), 'Fare'] = mean_fare
df.iloc[1043]

    The output will be as follows:

    Cabin                      NaN
Embarked                     S
Fare                   14.4354
Pclass                       3
Ticket                    3701
Age                       60.5
Name        Storey, Mr. Thomas
Parch                        0
Sex                       male
SibSp                        0
Survived                   NaN
Name: 1043, dtype: object
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Applied Supervised Learning with Python
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