Data Science Projects with Python : A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-learn

Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key Features Tackle data science problems by identifying the problem to be solved Illustrate patterns in data using appropriate visualizations Implement suitable machine learning algorithms to gain insights from data Book Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You'll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you'll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions. By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data. What you will learn Install the required packages to set up a data science coding environment Load data into a Jupyter notebook running Python Use Matplotlib to create data visualizations Fit machine learning models using scikit-learn Use lasso and ridge regression to regularize your models Compare performance between models to find the best outcomes Use k-fold cross-validation to select model hyperparameters Who this book is for If you are a data analyst, data scientist, or business analyst who wants to get started using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful Cover FM Copyright Table of Contents Preface Chapter 1: Data Exploration and Cleaning Introduction Python and the Anaconda Package Management System Indexing and the Slice Operator Exercise 1: Examining Anaconda and Getting Familiar with Python Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Exercise 2: Loading the Case Study Data in a Jupyter Notebook Getting Familiar with Data and Performing Data Cleaning The Business Problem Data Exploration Steps Exercise 3: Verifying Basic Data Integrity Boolean Masks Exercise 4: Continuing Verification of Data Integrity Exercise 5: Exploring and Cleaning the Data Data Quality Assurance and Exploration Exercise 6: Exploring the Credit Limit and Demographic Features Deep Dive: Categorical Features Exercise 7: Implementing OHE for a Categorical Feature Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Summary Chapter 2: Introduction to Scikit-Learn and Model Evaluation Introduction Exploring the Response Variable and Concluding the Initial Exploration Introduction to Scikit-Learn Generating Synthetic Data Data for a Linear Regression Exercise 8: Linear Regression in Scikit-Learn Model Performance Metrics for Binary Classification Splitting the Data: Training and Testing sets Classification Accuracy True Positive Rate, False Positive Rate, and Confusion Matrix Exercise 9: Calculating the True and False Positive and Negative Rates and Confusion Matrix in Python Discovering Predicted Probabilities: How Does Logistic Regression Make Predictions? Exercise 10: Obtaining Predicted Probabilities from a Trained Logistic Regression Model The Receiver Operating Characteristic (ROC) Curve Precision Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Summary Chapter 3: Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Pearson Correlation F-test Exercise 11: F-test and Univariate Feature Selection Finer Points of the F-test: Equivalence to t-test for Two Classes and Cautions Hypotheses and Next Steps Exercise 12: Visualizing the Relationship between Features and Response Univariate Feature Selection: What It Does and Doesn't Do Understanding Logistic Regression with function Syntax in Python and the Sigmoid Function Exercise 13: Plotting the Sigmoid Function Scope of Functions Why is Logistic Regression Considered a Linear Model? Exercise 14: Examining the Appropriateness of Features for Logistic Regression From Logistic Regression Coefficients to Predictions Using the Sigmoid Exercise 15: Linear Decision Boundary of Logistic Regression Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients Summary Chapter 4: The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Gradient Descent to Find Optimal Parameter Values Exercise 16: Using Gradient Descent to Minimize a Cost Function Assumptions of Logistic Regression The Motivation for Regularization: The Bias-Variance Trade-off Exercise 17: Generating and Modeling Synthetic Classification Data Lasso (L1) and Ridge (L2) Regularization Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Exercise 18: Reducing Overfitting on the Synthetic Data Classification Problem Options for Logistic Regression in Scikit-Learn Scaling Data, Pipelines, and Interaction Features in Scikit-Learn Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Summary Chapter 5: Decision Trees and Random Forests Introduction Decision trees The Terminology of Decision Trees and Connections to Machine Learning Exercise 19: A Decision Tree in scikit-learn Training Decision Trees: Node Impurity Features Used for the First splits: Connections to Univariate Feature Selection and Interactions Training Decision Trees: A Greedy Algorithm Training Decision Trees: Different Stopping Criteria Using Decision Trees: Advantages and Predicted Probabilities A More Convenient Approach to Cross-Validation Exercise 20: Finding Optimal Hyperparameters for a Decision Tree Random Forests: Ensembles of Decision Trees Random Forest: Predictions and Interpretability Exercise 21: Fitting a Random Forest Checkerboard Graph Activity 5: Cross-Validation Grid Search with Random Forest Summary Chapter 6: Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Preparing Samples with Missing Data Exercise 22: Cleaning the Dataset Exercise 23: Mode and Random Imputation of PAY_1 A Predictive Model for PAY_1 Exercise 24: Building a Multiclass Classification Model for Imputation Using the Imputation Model and Comparing it to Other Methods Confirming Model Performance on the Unseen Test Set Financial Analysis Financial Conversation with the Client Exercise 25: Characterizing Costs and Savings Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client Summary Appendix Index Data Science Projects with Python will help you get comfortable with using the Python environment for data science. This book will start you on your journey to mastering topics within machine learning. These skills will help you deliver the kind of state-of-the-art predictive models that are being used to deliver value to businesses across ...