Introduction to Predictive Modeling
Learning Outcomes
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What’s Included
Introduction to Statistical Models
Introduction to Statistical Models
In this lesson we'll outline the topics that will be covered in this course.
Simple Linear Regression
Simple Linear Regression
This lesson explains regression analysis and the basic principles associated with it. It also explains simple linear regression, the most basic form of regression, using an intuitive example.
Multiple Linear Regression
Multiple Linear Regression
Expanding on the last lesson, this lesson will explain the concept of multiple linear regression, which is more common in the real world than simple linear regression.
Logistic Regression
Logistic Regression
Logistic regression is used when you want to analyze the factors that influence whether an event of interest will happen. We'll explain logistic regression in this lesson.
Cluster Analysis
Cluster Analysis
Cluster analysis is used to divide a data set into groups of similar points. This lesson introduces the principles of cluster analysis and demonstrates k-means clustering, the most commonly used method of cluster analysis.
Introduction to Classification
Introduction to Classification
This lesson introduces the concepts associated with classification models. This includes the idea of splitting data into training and test data and evaluating a classification model with a confusion matrix.
The Naive Bayes Classifier
The Naive Bayes Classifier
This lesson introduces the Naïve Bayes classifier and explains the basic principles of the model, without going into complex mathematical details. We analyze the classifier using a simple example.
Decision Trees
Decision Trees
Decision trees are an intuitive classification model. This lesson explains how they can be used for prediction and explains the principles behind how they can be created.
Boosting
Boosting
The next two lessons look at ensemble methods. This lesson introduces the concept of ensemble methods and discusses the concepts behind boosting.
Random Forests
Random Forests
The random forest is an ensemble method that works by building a large number of decision trees. This lesson demonstrates how the random forest model improves the predictive power of decision trees.
Principles of Time Series Data
Principles of Time Series Data
The final lessons in this course consider time series analysis. This lesson introduces the basic principles of time series data, like trends, seasonality, and forecasting.
ARIMA Models
ARIMA Models
The Auto-Regressive Integrated Moving Average (ARIMA) model is commonly used for time series forecasting. This lesson explains how the model works, using various examples of different orders of ARIMA models.
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