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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 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: The Bias-Variance Trade-Off

Quiz yourself on the bias-variance trade-off concepts you learned this chapter.

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

1

Which statement about scaling data is true?

A)

Scaling data is not necessary when using L1 regularization with the saga solver.

B)

Scaling data ensures that all features have the same minimum and maximum values.

C)

Scaling data can only be done using the MinMaxScaler function.

D)

Scaling data is not necessary when doing regularization.

Question 1 of 50 attempted

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