Book Image

The Data Science Workshop - Second Edition

By : Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare
5 (1)
Book Image

The Data Science Workshop - Second Edition

5 (1)
By: Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare

Overview of this book

Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities. The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search. Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch. By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently.

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Table of Contents (16 chapters)
Preface
About the Book
1
1. Introduction to Data Science in Python
1. Introduction to Data Science in Python
Introduction
Application of Data Science
Overview of Python
Python for Data Science
Scikit-Learn
Summary
Free Chapter
2
2. Regression
2. Regression
Introduction
Simple Linear Regression
Multiple Linear Regression
Conducting Regression Analysis Using Python
Multiple Regression Analysis
Assumptions of Regression Analysis
Explaining the Results of Regression Analysis
Summary
3
3. Binary Classification
3. Binary Classification
Introduction
Understanding the Business Context
Feature Engineering
Data-Driven Feature Engineering
Correlation Matrix and Visualization
Summary
4
4. Multiclass Classification with RandomForest
4. Multiclass Classification with RandomForest
Introduction
Training a Random Forest Classifier
Evaluating the Model's Performance
Maximum Depth
Minimum Sample in Leaf
Maximum Features
Summary
5
5. Performing Your First Cluster Analysis
5. Performing Your First Cluster Analysis
Introduction
Clustering with k-means
Interpreting k-means Results
Choosing the Number of Clusters
Initializing Clusters
Calculating the Distance to the Centroid
Standardizing Data
Summary
6
6. How to Assess Performance
6. How to Assess Performance
Introduction
Splitting Data
Assessing Model Performance for Regression Models
Assessing Model Performance for Classification Models
The Confusion Matrix
Receiver Operating Characteristic Curve
Area Under the ROC Curve
Saving and Loading Models
Summary
7
7. The Generalization of Machine Learning Models
7. The Generalization of Machine Learning Models
Introduction
Overfitting
Underfitting
Data
Random State
Cross-Validation
cross_val_score
LogisticRegressionCV
Hyperparameter Tuning with GridSearchCV
Hyperparameter Tuning with RandomizedSearchCV
Model Regularization with Lasso Regression
Ridge Regression
Summary
8
8. Hyperparameter Tuning
8. Hyperparameter Tuning
Introduction
What Are Hyperparameters?
Finding the Best Hyperparameterization
Tuning Using Grid Search
GridSearchCV
Random Search
Summary
9
9. Interpreting a Machine Learning Model
9. Interpreting a Machine Learning Model
Introduction
Linear Model Coefficients
RandomForest Variable Importance
Variable Importance via Permutation
Partial Dependence Plots
Local Interpretation with LIME
Summary
10
10. Analyzing a Dataset
10. Analyzing a Dataset
Introduction
Exploring Your Data
Analyzing Your Dataset
Analyzing the Content of a Categorical Variable
Summarizing Numerical Variables
Visualizing Your Data
Boxplots
Summary
11
11. Data Preparation
11. Data Preparation
Introduction
Handling Row Duplication
Converting Data Types
Handling Incorrect Values
Handling Missing Values
Summary
12
12. Feature Engineering
12. Feature Engineering
Introduction
13
13. Imbalanced Datasets
13. Imbalanced Datasets
Introduction
Understanding the Business Context
Challenges of Imbalanced Datasets
Strategies for Dealing with Imbalanced Datasets
Generating Synthetic Samples
Summary
14
14. Dimensionality Reduction
14. Dimensionality Reduction
Introduction
Creating a High-Dimensional Dataset
Strategies for Addressing High-Dimensional Datasets
Comparing Different Dimensionality Reduction Techniques
Summary
15
15. Ensemble Learning
15. Ensemble Learning
Introduction
Ensemble Learning
Simple Methods for Ensemble Learning
Advanced Techniques for Ensemble Learning
Summary

Introduction

The previous chapter provided a primer to Python programming and an overview of the data science field. Data science is a relatively young multidisciplinary field of study. It draws its concepts and methods from the traditional fields of statistics, computer science, and the broad field of artificial intelligence (AI), especially the subfield of AI called machine learning:

Figure 2.1: The data science models

Figure 2.1: The data science models

As you can see in Figure 2.1, data science aims to make use of both structured and unstructured data, develop models that can be effectively used, make predictions, and also derive insights for decision making.

A loose description of structured data will be any set of data that can be conveniently arranged into a table that consists of rows and columns. This kind of data is normally stored in database management systems.

Unstructured data, however, cannot be conveniently stored in tabular form – an example of such a dataset is a text document. To achieve the objectives of data science, a flexible programming language that effectively combines interactivity with computing power and speed is necessary. This is where the Python programming language meets the needs of data science and, as mentioned in Chapter 1, Introduction to Data Science in Python, we will be using Python in this book.

The need to develop models to make predictions and to gain insights for decisionmaking cuts across many industries. Data science is, therefore, finding uses in many industries, including healthcare, manufacturing and the process industries in general, the banking and finance sectors, marketing and e-commerce, the government, and education.

In this chapter, we will be specifically be looking at regression, which is one of the key methods that is used regularly in data science, in order to model relationships between variables, where the target variable (that is, the value you're looking for) is a real number.

Consider a situation where a real estate business wants to understand and, if possible, model the relationship between the prices of property in a city and knowing the key attributes of the properties. This is a data science problem and it can be tackled using regression.

This is because the target variable of interest, which is the price of a property, is a real number. Examples of the key attributes of a property that can be used to predict its value are as follows:

  • The age of the property
  • The number of bedrooms in a property
  • Whether the property has a pool or not
  • The area of land the property covers
  • The distance of the property from facilities such as railway stations and schools

Regression analysis can be employed to study this scenario, in which you have to create a function that maps the key attributes of a property to the target variable, which, in this case, is the price of a property.

Regression analysis is part of a family of machine learning techniques called supervised machine learning. It is called supervised because the machine learning algorithm that learns the model is provided a kind of question and answer dataset to learn from. The question here is the key attribute and the answer is the property price for each property that is used in the study, as shown in the following figure:

Figure 2.2: Example of a supervised learning technique

Figure 2.2: Example of a supervised learning technique

Once a model has been learned by the algorithm, we can provide the model with a question (that is, a set of attributes for a property whose price we want to find) for it to tell us what the answer (that is, the price) of that property will be.

This chapter is an introduction to linear regression and how it can be applied to solve practical problems like the one described previously in data science. Python provides a rich set of modules (libraries) that can be used to conduct rigorous regression analysis of various kinds. In this chapter, we will make use of the following Python modules, among others: pandas, statsmodels, seaborn, matplotlib, and scikit-learn.

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