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Data Science Projects with Python

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Introduction

Overview Course Structure

Data Exploration and Cleaning

Introduction: Python for Data Science Exercise: Getting Familiar with Python Different Types of Data Science Problems Introduction to Jupyter and pandas Exercise: Loading the Case Study Data in a Jupyter Notebook Getting Familiar with Data and Performing Data Cleaning Exercise: Verifying Basic Data Integrity Boolean Masks Exercise: Continuing Verification of Data Integrity Exercise: Exploring and Cleaning the Data Exercise: Exploring the Credit Limit and Demographic Feature Deep Dive: Categorical Features Exercise: Implementing OHE for a Categorical Feature Exploring the Financial History Features in the Dataset Summary: Data Exploration and Cleaning Quiz: Data Exploration and Cleaning

Mini Project

(Challenge) Exploring Remaining Financial Features in Dataset

Introduction to scikit-learn and Model Evaluation

Introduction: scikit-learn and Model Evaluation The Response Variable and Concluding the Initial Exploration scikit-learn Generating Synthetic Data Exercise: Linear Regression in scikit-learn Model Performance Metrics for Binary Classification Splitting the Data: Training and Test Sets Classification Accuracy Exercise: Calculating True and False Rates and Confusion Matrix Exercise: Obtaining Probabilities from Logistic Regression Model The Receiver Operating Characteristic (ROC) Curve Precision Summary: Introduction to scikit-learn and Model Evaluation Quiz: Introduction to scikit-learn and Model Evaluation

Mini Project

(Challenge) Logistic Regression and Precision-Recall Curve

Details of Logistic Regression and Feature Extraction

Introduction: Logistic Regression and Feature Extraction Examining Relationships Between Features and Response Variable Pearson Correlation Mathematics of Linear Correlation Exercise: F-test and Univariate Feature Selection Finer Points of the F-test Exercise: Visualizing the Feature and Response Variable Relationship Univariate Feature Selection Exercise: Plotting the Sigmoid Function Scope of Functions Why Is Logistic Regression Considered a Linear Model?Exercise: Finding Appropriate Features for Logistic Regression Logistic Regression Predictions Using Sigmoid Exercise: Linear Decision Boundary of Logistic Regression Summary: Details of Logistic Regression and Feature Extractor Quiz: Details of Logistic Regression and Feature Extraction

Mini Project

(Challenge) Logistic Regression Model and Coefficients

The Bias-Variance Trade-Off

Introduction: The Bias-Variance Trade-Off Estimating the Coefficients and Intercepts of Logistic Regression Gradient Descent to Find Optimal Parameter Values Exercise: Using Gradient Descent to Minimize a Cost Function Assumptions of Logistic Regression The Motivation for Regularization Exercise: Generating and Modeling Synthetic Classification Data Lasso (L1) and Ridge (L2) Regularization Cross-Validation Exercise: The Synthetic Data Classification Problem Options for Logistic Regression in scikit-learn Scaling Data, Pipelines, and Interaction Features in Scikit-Learn Summary: The Bias-Variance Trade-Off Quiz: The Bias-Variance Trade-Off

Mini Project

(Challenge) Cross-Validation and Feature Engineering

Decision Trees and Random Forests

Introduction: Decision Trees and Random Forests Decision Trees Exercise: A Decision Tree in scikit-learn Training Decision Trees: Node Impurity Training Decision Trees: Greedy Algorithm and Stopping Criteria Using Decision Trees: Advantages and Predicted Probabilities A More Convenient Approach to Cross-Validation Exercise: Find Optimal Hyperparameters for a Decision Tree Random Forests: Predictions and Ensembles of Decision Trees Exercise: Fitting a Random Forest Checkerboard Graph Summary: Decision Trees and Random Forests Quiz: Decision Trees and Random Forests

Mini Project

(Challenge) Cross-Validation Grid Search with Random Forest

Gradient Boosting, XGBoost, and SHAP Values

Introduction: XGBoost

Gradient Boosting and XGBoost

XGBoost Hyperparameters: Early Stopping

XGBoost Hyperparameters: Tuning the Learning Rate

Other Important Hyperparameters in XGBoost

Exercise: Randomized Grid Search to Tune XGBoost Hyperparameters

Another Way of Growing Trees: XGBoost's grow_policy

Explaining Model Predictions with SHAP Values

Exercise: SHAP Visualization and Analysis

Summary: Gradient Boosting, XGBoosting, and SHAP Values

Quiz: Gradient Boosting, XGBoosting, and SHAP Values

Mini Project

(Challenge) XGBoost and SHAP Explanation for Case Study Data

Test Set Analysis, Financial Insights, and Delivery to the Client

Introduction: Test Set Analysis Review of Modeling Results Model Performance on the Test Set Distribution of Predicted Probability and Decile Chart Exercise: Equal-Interval Chart Calibration of Predicted Probabilities Financial Analysis Exercise: Characterizing Costs and Savings Summary: Analysis, Financial Insights, and Delivery to the Client Quiz: Test Set Analysis and Financial Insights

Mini Project

(Challenge) Deriving Financial Insights

Appendix

How to Create Jupyter Notebook Locally

Quiz: Gradient Boosting, XGBoosting, and SHAP Values

Quiz yourself on the gradient boosting, XGBoost, and SHAP values concepts you learned this chapter.

Here’s a rapid-fire round of questions about this chapter.

1

What is the primary purpose of SHapely Additive exPlanations (SHAP)?

A)

To interpret machine learning models and explain individual predictions.

B)

To optimize feature selection in the training of machine learning models.

C)

To quantify the uncertainty of machine learning predictions.

D)

To evaluate the fairness and bias of machine learning models.

Question 1 of 50 attempted

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