Data Science Projects with Python A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-Learn
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 u...
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| Main Author | |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Birmingham
Packt Publishing, Limited
2019
Packt Publishing Limited Packt Publishing |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9781838551025 1838551026 |
| DOI | 10.0000/9781838552602 |
Cover
Table of Contents:
- 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
- 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
- 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: A case study approach to successful data science projects using Python, pandas, and scikit-learn