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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

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

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 Missing Data 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: Introduction to scikit-learn and Model Evaluation

Quiz yourself on the scikit-learn and model evaluation concepts you learned this chapter.

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

1

A binary classification model is trained on a dataset of 100 instances. The following confusion matrix shows the model’s predictions:

What is the precision and recall of the model?

A)

Precision = 0.80, Recall = 0.67

B)

Precision = 0.80, Recall = 0.70

C)

Precision = 0.70, Recall = 0.80

D)

Precision = 0.67, Recall = 0.80

Question 1 of 50 attempted

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