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

Customer Reviews

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7. Model Evaluation

Activity 7.01: Final Test Project

Import the relevant libraries:

import pandas as pd 
import numpy as np 
import json 
%matplotlib inline 
import matplotlib.pyplot as plt 
from sklearn.preprocessing import OneHotEncoder 
from sklearn.model_selection import RandomizedSearchCV, train_test_split
from sklearn.ensemble import GradientBoostingClassifier 
from sklearn.metrics import (accuracy_score, precision_score, \
recall_score, confusion_matrix, precision_recall_curve)

Read the breast-cancer-data.csv dataset:

data = pd.read_csv('../Datasets/breast-cancer-data.csv')
data.info()

Let's separate the input data (X) and the target (y):

X = data.drop(columns=['diagnosis'])
y = data['diagnosis'].map({'malignant': 1, 'benign': 0}.get).values

Split the dataset into training and test sets:

X_train, X_test, \
y_train, y_test = train_test_split(X, y, \
         ...

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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7. Model Evaluation

Activity 7.01: Final Test Project