The Supervised Learning Workshop - Second Edition

By : Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur

The Supervised Learning Workshop - Second Edition

By: Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur

Overview of this book

Would you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society. Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you’ll learn how supervised learning, when used with Python, can produce efficient predictive models. Starting with the fundamentals of supervised learning, you’ll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you’ll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you’ll grasp advanced ensemble techniques such as boosting and random forests. Finally, you’ll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks. By the end of this book, you’ll have the skills you need to work on your real-life supervised learning Python projects.

Preface

About the Book

1. Fundamentals

Introduction

Data Quality Considerations

Summary

Free Chapter

2. Exploratory Data Analysis and Visualization

Introduction

Exploratory Data Analysis (EDA)

Summary Statistics and Central Values

Missing Values

Distribution of Values

Relationships within the Data

Summary

3. Linear Regression

Introduction

Regression and Classification Problems

Linear Regression

Multiple Linear Regression

Summary

4. Autoregression

Introduction

Autoregression Models

Summary

5. Classification Techniques

Introduction

Ordinary Least Squares as a Classifier

Logistic Regression

Classification Using K-Nearest Neighbors

Classification Using Decision Trees

Artificial Neural Networks

Summary

6. Ensemble Modeling

Introduction

One-Hot Encoding

Overfitting and Underfitting

Bagging

Bootstrapping

Boosting

Stacking

Summary

7. Model Evaluation

Introduction

Importing the Modules and Preparing Our Dataset

Evaluation Metrics

Splitting a Dataset

Performance Improvement Tactics

Summary

Appendix

1. Fundamentals

2. Exploratory Data Analysis and Visualization

3. Linear Regression

4. Autoregression

5. Classification Techniques

6. Ensemble Modeling

7. Model Evaluation

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1. Fundamentals

Activity 1.01: Implementing Pandas Functions

Open a new Jupyter notebook.

Use pandas to load the Titanic dataset:

import pandas as pd
df = pd.read_csv(r'../Datasets/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 1.26: First five rows
Use the describe function as follows:
```
df.describe(include='all')
```
The output will be as follows:
Figure 1.27: Output of describe()
We do not need the Unnamed: 0 column. We can remove the column without using the del command, as follows:
```
del df['Unnamed: 0']
df = df[df.columns[1:]] # Use the columns
df.head()
```
The output will be as follows:
Figure 1.28: First five rows after deleting the Unnamed: 0 column
Compute the mean, standard deviation, minimum, and maximum values for the columns of the DataFrame without using describe:
```
df.mean()
```
The output will be as follows:
Figure 1.29: Output...

The Supervised Learning Workshop - Second Edition

By : Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur

The Supervised Learning Workshop - Second Edition

By: Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur

Overview of this book

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1. Fundamentals

Activity 1.01: Implementing Pandas Functions