Logistic Regression, Decision Tree and Neural Network in R

Logistic Regression, Decision Tree and Neural Network in R, available at $19.99, has an average rating of 3.95, with 12 lectures, based on 32 reviews, and has 3000 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone seeking a career as data scientist, data analyst , finance analyst, statistician , actuary, just to name few It is particularly useful for Anyone seeking a career as data scientist, data analyst , finance analyst, statistician , actuary, just to name few.

Enroll now: Logistic Regression, Decision Tree and Neural Network in R

Summary

Title: Logistic Regression, Decision Tree and Neural Network in R

Price: $19.99

Average Rating: 3.95

Number of Lectures: 12

Number of Published Lectures: 12

Number of Curriculum Items: 12

Number of Published Curriculum Objects: 12

Original Price: $44.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Anyone seeking a career as data scientist, data analyst , finance analyst, statistician , actuary, just to name few

Target Audiences

Anyone seeking a career as data scientist, data analyst , finance analyst, statistician , actuary, just to name few

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: How to download R

Lecture 3: Welcome

Lecture 4: Description of the data

Lecture 5: Data transformation

Lecture 6: Missing values detection and treatment

Lecture 7: Data visualisation

Lecture 8: Training and Testing set

Lecture 9: Decision Tree

Lecture 10: Logistic regression

Lecture 11: Neural network

Lecture 12: Min Max normalization

Instructors

Modeste Atsague
Data Scientist, Statistician

Rating Distribution

1 stars: 3 votes

2 stars: 0 votes

3 stars: 6 votes

4 stars: 13 votes

5 stars: 10 votes

Frequently Asked Questions

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