Book Image

The Data Science Workshop

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

The Data Science Workshop

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

Overview of this book

You already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.

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Table of Contents (18 chapters)
Preface
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
Merging Datasets
Binning Variables
Manipulating Dates
Performing Data Aggregation
Summary
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
Summary
16
16. Machine Learning Pipelines
16. Machine Learning Pipelines
Introduction
Pipelines
Automating ML Workflows Using Pipeline
ML Pipeline with Processing and Dimensionality Reduction
ML Pipeline for Modeling and Prediction
ML Pipeline for Spot-Checking Multiple Models
ML Pipelines for Identifying the Best Parameters for a Model
Applying Pipelines to a Dataset
Summary
17
17. Automated Feature Engineering
17. Automated Feature Engineering
Introduction
Feature Engineering
Featuretools on a New Dataset
Summary

Summary

This chapter introduced the topic of linear regression analysis using Python. We learned that regression analysis, in general, is a supervised machine learning or data science problem. We learned about the fundamentals of linear regression analysis, including the ideas behind the method of least squares. We also learned about how to use the pandas Python module to load and prepare data for exploration and analysis.

We explored how to create scatter graphs of bivariate data and how to fit a line of best fit through them. Along the way, we discovered the power of the statsmodels module in Python. We explored how to use it to define simple linear regression models and to solve the model for the relevant parameters. We also learned how to extend that to situations where the number of independent variables is more than one – multiple linear regressions. We investigated approaches by which we can transform a non-linear relation between a dependent and independent variable so that a non-linear problem can be handled using linear regression, introduced because of the transformation. We took a closer look at the statsmodels formula language. We learned how to use it to define a variety of linear models and to solve for their respective model parameters.

We continued to learn about the ideas underpinning model goodness of fit. We discussed the R-squared statistic as a measure of the goodness of fit for regression models. We followed our discussions with the basic concepts of statistical significance. We learned about how to validate a regression model globally using the F-statistic, which Python calculates for us. We also examined how to check for the statistical significance of individual model coefficients using t-tests and their associated p-values. We reviewed the assumptions of linear regression analysis and how they impact on the validity of any regression analysis work.

We will now move on from regression analysis, and Chapter 3, Binary Classification, and Chapter 4, Multiclass Classification with RandomForest, will discuss binary and multi-label classification, respectively. These chapters will introduce the techniques needed to handle supervised data science problems where the dependent variable is of the categorical data type.

Regression analysis will be revisited when the important topics of model performance improvement and interpretation are given a closer look later in the book. In Chapter 8, Hyperparameter Tuning, we will see how to use k-nearest neighbors and as another method for carrying out regression analysis. We will also be introduced to ridge regression, a linear regression method that is useful for situations where there are a large number of parameters.

Previous Section
End of Chapter 2
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