Logistic Regression, Decision Tree and Neural Network in R
4.6 (6 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
225 students enrolled
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Logistic Regression, Decision Tree and Neural Network in R

Logistic Regression, Decision Tree and Neural Network in R
New
4.6 (6 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
225 students enrolled
Created by Modeste Atsague
Last updated 8/2017
English
Current price: $10 Original price: $25 Discount: 60% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 1 hour on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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
View Curriculum
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.

Who is the target audience?
  • Anyone seeking a career as data scientist, data analyst , finance analyst, statistician , actuary, just to name few
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Curriculum For This Course
12 Lectures
01:00:28
+
Introduction
12 Lectures 01:00:28





Missing values detection and treatment
02:53

Data visualisation
03:36

Training and Testing set
04:26

Decision Tree
05:23

Logistic regression
04:08

Neural network
26:52

Min Max normalization
01:51
About the Instructor
Modeste Atsague
4.6 Average rating
10 Reviews
296 Students
2 Courses
Statistician

Hi, My Name is Modeste. Currently a Lectures at Central Connecticut State University, I invest a lot of time on learning and teaching. Covering a wide range of topics in Mathematics, Statistics and Computer Science , Some of my main interests include machine learning, data reduction techniques, Statistical Computing, regression analysis and a wide range of mathematical Statistics topics including parameter estimate.

With my background, which combines Mathematics ,Statistics and computer science, I have a very strong interest in computational Statistics and Statistical computing.

You might not or have less background in Computer Science     and Statistics but i will do my best so that you benefit from my experience