Logistic Regression, Decision Tree and Neural Network in R

Logistic Regression, Decision Tree and Neural Network in R

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What you’ll learn

  • At the end of this Course, A student will be able to use Predictive analytics ( Decision tree , neural network or Logistic regression) to predict future outcomes. Some areas of application are the following: Actuarial Science, marketing, financial services, insurance, mobility, pharmaceuticals, healthcare, just to name a few

Requirements

  • The only prerequisite for this class is the willingness to learn and some basic knowledge of R but not necessary

Description

In this course, we cover two analytics techniques: Descriptive statistics and Predictive analytics. For the predictive analytic, our main focus is the implementation of a logistic regression model a Decision tree and neural network. We well also see how to interpret our result, compute the prediction accuracy rate, then construct a confusion matrix .

By the end of this course , you will be able to effectively summarize your data , visualize your data , detect and eliminate missing values, predict futures outcomes using analytical techniques described above , construct a confusion matrix, import and export a data.

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