Business Machine Learning

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1.Course Introduction

About This Course

2.Linear Regression

Introduction to Linear Regression Welcome and Data Overview Exploratory Data Analysis Variance and Covariance Machine Learning Model Evaluation Model Explainability Finalizing and Serializing the Model Model Deployment in the Notebook Cross-validation Summary Exercise: Model the Boston Housing Dataset Solution: Model the Boston Housing Dataset Exercise: Model the King County's House Sales Dataset Solution: Model the King County's House Sales Dataset Quiz: Linear Regression

3.Regularization

Introduction to Regularization Penalties Effect of Regularization Model Performance Summary Quiz: Regularization

4.Bias-Variance Trade-off

Introduction and Data Generation Morale Function and Model Error Generating Student Samples and Plot Model Building to Predict the Morale Bias-Variance Trade-off Summary Quiz: Bias Variance Trade-off

5.Categorical Features

Quantitative vs. Qualitative Data and Creating Dummies Redundant Variables and Machine Learning Summary Exercise: Model the Auto MPG Dataset Solution: Model the Auto MPG Dataset Quiz: Categorical Features

6.Logistic Regression

Introduction to Logistic Regression Probability and Odds Understanding Logistic Regression Logistic Regression Implementation Model Evaluation Summary Quiz: Logistic Regression

7.Logistic Regression: Titanic Data

The Dataset and Exploratory Data Analysis Data Preprocessing Getting Data Ready and Building Machine Learning Model Predictions and Evaluation Model Evaluation Feature Importance and Model Explainability Summary Exercise: Retrain the Model Without Pclass Dummies Solution: Retrain the Model Without Pclass Dummies Quiz: Logistic Regression—Titanic Data

Project

Sentiment Analysis Using Multinomial Logistic Regression

8.Multiclass Classification and Handling Imbalanced Classes

Introduction to Multiclass Logistic Regression Classification Imbalanced Datasets and Techniques to Handle Them Machine Learning and Imbalanced Data Performance of the Trained Models on Unseen Data Summary Quiz: Multiclass Classification and Handling Imbalanced Classes

9.Project: Predicting Chronic Kidney Disease

Problem Definition and the Data Exploratory Data Analysis Data Preprocessing Model Training and Evaluation

10.K-Nearest Neighbors

Introduction to K-Nearest Neighbors Working Principle of K-Nearest Neighbors Visualize the Working of K-Nearest Neighbors Final Thoughts Quiz: K-Nearest Neighbors

11.Implementation of K-Nearest Neighbors

The Dataset and Exploratory Data Analysis Model Training Using Unscaled Data Effect of Feature Scaling Model Training Using Scaled Features Elbow Method to Chose the K Value Model Comparisons Quiz: Implementation of K-Nearest Neighbors

12.Logistic Regression vs. KNN

The Dataset and Exploratory Data Analysis Baseline Accuracy and Machine Learning Model Selection Exercise: Find the Best Combination of Parameters Solution: Find the Best Combination of Parameters Exercise: Find the Best Value for K Solution: Find the Best Value for K

13.Decision Tree Learning

Introduction to Decision Trees Bootstrap, Bagging, and Random Forests The Dataset and Exploratory Data Analysis Machine Learning Feature Importance Hyperparameters and Their Tuning ROC Curve Playing With Probability Cut-off Tree Visualization Exercise: Train a Decision Tree Model Using the Gini Criteria Solution: Train a Decision Tree Model Using the Gini Criteria Exercise: Train a Random Forest Model Using the Entropy Criteria Solution: Train a Random Forest Model Using the Entropy Criteria Quiz: Decision Tree Learning

Project

Implement the Decision Tree Classifier from Scratch

14.Bootstrapping and Confidence Interval

Introduction to Bootstrapping and Confidence Interval The Data Confidence Interval of Mean Confidence Interval for the Median Quiz: Bootstrapping and Confidence Interval

15.Support Vector Machine

Introduction to Support Vector Machine Visualize the Working of the Support Vector Machine The Dataset and Exploratory Data Analysis Feature Selection Machine Learning Grid Search Feature Scaling ROC Curve and Saving the Model Quiz: Support Vector Machine

16.Practice and Comparisons

MNIST: Handwritten Digits Dataset The Iris Dataset The Circles Dataset

17.What's Next?

Final Thoughts and Future Directions

18.Appendix

R-square and Goodness of the Fit

Business Machine Learning/

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Exploratory Data Analysis

Exploratory Data Analysis

Get into a detailed exploration of the data.

We'll cover the following...

Exploratory data analysis (EDA) is a key for any data-related project. A better understanding of data helps create practical and effective models.

Price distribution

Let's see how the price is distributed.

Python 3.8

# housing price (target) histogram 
plt.figure(figsize=(12, 6)) # setting fig size, we can leave this line for default
plt.hist(x=df['price'], bins=50) # we can change the bin number
plt.xlabel('price ($1000s)') # setting x label
plt.ylabel('count') # setting y label

If we look at the above histogram, most of the prices are in the 20s (x1,000s), and the average price is around ...