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

Multiple Linear Regression

In the simple linear regression discussed previously, we only have one independent variable. If we include multiple independent variables in our analysis, we get a multiple linear regression model. Multiple linear regression is represented in a way that's similar to simple linear regression.

Let's consider a case where we want to fit a linear regression model that has three independent variables, X1, X2, and X3. The formula for the multiple linear regression equation will look like Figure 2.5:

Figure 2.5: Multiple linear regression equation

Figure 2.5: Multiple linear regression equation

Each independent variable will have its own coefficient or parameter (that is, β1 β2 or β3). The βs coefficient tells us how a change in their respective independent variable influences the dependent variable if all other independent variables are unchanged.

Estimating the Regression Coefficients (β0, β1, β2 and β3)

The regression coefficients in Figure 2.5 are estimated using the same least squares approach that was discussed when simple linear regression was introduced. To satisfy the least squares method, the chosen coefficients must minimize the sum of squared residuals.

Later in the chapter, we will make use of the Python programming language to compute these coefficient estimates practically.

Logarithmic Transformations of Variables

As has been mentioned already, sometimes the relationship between the dependent and independent variables is not linear. This limits the use of linear regression. To get around this, depending on the nature of the relationship, the logarithm function can be used to transform the variable of interest. What happens then is that the transformed variable tends to have a linear relationship with the other untransformed variables, enabling the use of linear regression to fit the data. This will be illustrated in practice on the dataset being analyzed later in the exercises of the book.

Correlation Matrices

In Figure 2.3, we saw how a linear relationship between two variables can be analyzed using a straight-line graph. Another way of visualizing the linear relationship between variables is with a correlation matrix. A correlation matrix is a kind of cross-table of numbers showing the correlation between pairs of variables, that is, how strongly the two variables are connected (this can be thought of as how a change in one variable will cause a change in the other variable). It is not easy analyzing raw figures in a table. A correlation matrix can, therefore, be converted to a form of "heatmap" so that the correlation between variables can easily be observed using different colors. An example of this is shown in Exercise 2.01, Loading and Preparing the Data for Analysis.

Previous Section
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