Predictive Modelling in R Online Training | R Certification Course | Edureka

Advanced Predictive Modelling in R Certification Training

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R offers a free and open source environment that is perfect for both learning and deploying predictive modelling solutions. This Certification Training is intended for a broad audience as both, an introduction to predictive models as well as a guide to applying them, covering topics such as Ordinary Least Square Regression, Advanced Regression, Imputation, Dimensionality Reduction etc. Readers will also be able to learn basics of Statistics, such as Correlation and Linear Regression Analysis.
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Learning Objectives: In this module you will get a brief introduction to statistics and will conduct best test and exploratory analysis. 

  • Covariance & Correlation
  • Central Limit Theorem
  • Z Score
  • Normal Distributions
  • Hypothesis

Hands On: Calculating statistical parameters such as mean, median, mode and making custom visualizations for developing intuition of data with respect to statistical parameters.
Learning Objectives: In this module you will get a brief introduction of basic regression and multiple regression and will learn how to present the same graphically. 

  • Bivariate Data
  • Quantifying Association
  • The Best Line: Least Squares Method
  • The Regressions
  • Simple Linear Regression
  • Deletion Diagnostics and Influential Observations
  • Regularization

Hands On: Ridge and Lasso regression implementation.
Learning Objectives: The goal of this module is to dive you into linear regression and make the model a better fit, make necessary transformation check for over fitting and under fitting and outliers’ identification and treatment. 

  • Model fitting using Linear Regression
  • Performing Over Fitting & Under Fitting
  • Collinearity
  • What is Heteroscedasticity?

Hands On: Perform exploratory data analysis and check for heteroscedasticity, perform remedial steps and transform the data and implement linear regression model.
Learning Objectives: In this module, you will understand the problems related with Linear Probability Model, will be introduced to logistic regression and various uses of the same and its industry usage. 

  • Binary Response Regression Model
  • Linear regression as Linear Probability Model
  • Problems with Linear Probability Model
  • Logistic Function
  • Logistic Curve
  • Goodness of fit matrix
  • All Interactions Logistic Regression
  • Multinomial Logit
  • Interpretation
  • Ordered Categorical Variable

Hands On: Build a logistic regression model to classify the data.
Learning Objectives: In this module, you will dig deeper into logistic regression and learn about more varied usage of logistic regression on various dataset. 

  • Poisson Regression
  • Model Fit Test
  • Offset Regression
  • Poisson Model with Offset
  • Negative Binomial
  • Dual Models
  • Hurdle Models
  • Zero-Inflated Poisson Models
  • Variables used in the Analysis
  • Poisson Regression Parameter Estimates
  • Zero-Inflated Negative Binomial

Hands On: Create ZINB and Hurdel regression model.
Learning Objectives: In this module, you will learn about addressing missing values and how to impute it using various processes.

  • Missing Values are Common
  • Types of Missing Values
  • Why is Missing Data a Problem?
  • No Treatment Option: Complete Case Method
  • No Treatment Option: Available Case Method
  • Problems with Pairwise Deletion
  • Mean Substitution Method
  • Imputation
  • Regression Substitution Method
  • K-Nearest Neighbour Approach
  • Maximum Likelihood Estimation
  • EM Algorithm
  • Single and Multiple Imputation
  • Little’s Test for MCAR

Hands On: Implement KNN model and perform single and multiple imputation.
Learning Objectives: The goal of this module is to give an introduction on forecasting and time series data. 

  • Need for Forecasting
  • Types of Forecast
  • Forecasting Steps
  • Autocorrelation
  • Correlogram
  • Time Series Components
  • Variations in Time Series
  • Seasonality
  • Forecast Error
  • Mean Error (ME)
  • MPE and MAPE---Unit free measure
  • Additive v/s Multiplicative Seasonality
  • Curve Fitting
  • Simple Exponential Smoothing (SES)
  • Decomposition with R
  • Generating Forecasts
  • Explicit Modeling
  • Modeling of Trend
  • Seasonal Components
  • Smoothing Methods
  • ARIMA Model-building

Hands On: Implement Exponential Smoothing and ARIMA model for time series forecasting.
Learning Objectives: In this module, you will learn about Seasonality, Trend Analysis and decaying the factors over the time. 

  • Analysis of Log-transformed Data
  • How to Formulate the Model
  • Partial Regression Plot
  • Normal Probability Plot
  • Tests for Normality
  • Box-Cox Transformation
  • Box-Tidwell Transformation
  • Growth Curves
  • Logistic Regression: Binary
  • Neural Network
  • Network Architectures
  • Neural Network Mathematics
Learning Objectives: In this module, you will get a complete knowledge on Dimensionality Reduction and will discuss and apply few of the important algorithms associated with Dimensionality Reduction. 

  • Factor Analysis
  • Principal Component Analysis
  • Mechanism of finding PCA
  • Linear Discriminant Analysis (LDA)
  • Determining the maximum separable line using LDA
  • Implement Dimensionality Reduction algorithm in R

Hands On: Implement Principal component analysis and Boosting(ADAboost).
Learning Objectives: In this module, you will learn about Churn analysis and Regression on time series data with time component. 

  • Time-to-Event Data
  • Censoring
  • Survival Analysis
  • Types of Censoring
  • Survival Analysis Techniques
  • PreProcessing
  • Elastic Net

Hands On: Do PCA preprocessing and implement Elastic Net model.
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This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. Predictive modelling is emerging as a competitive strategy across many business sectors and can set apart high performing companies. Models such as multiple linear regression, logistic regression, auto-regressive integrated moving average (ARIMA), decision trees, and neural networks are frequently used in solving predictive analytics problems. Regression models help us understand the relationships among these variables and how their relationships can be exploited to make decisions
After the completion of this training, you will be able to:
  • Understand Basics of Statistics using R
  • Explain Regression
  • Understand Simple, Multiple, Advanced and Logistic Regression
  • Perform model fitting using Linear Regression
  • Explain What is Heteroscedasticity?
  • Understand Binary Response Variable and Linear Probability Model
  • Explain Imputation
  • Understand Forecasting
  • Learn Neural Networks
  • Explain Dimensionality Reduction
  • Understands the algorithms associated with Dimensionality Reduction
  • Understand Survival Analysis
This course will introduce you to some of the most widely used predictive modelling techniques and their core principles which is designed for anyone who is interested in using data to gain insights and make better business decisions. The techniques discussed in this course are applied throughout all functional areas within business organizations such as accounting, finance, human resource management, marketing, operations, strategic planning etc.
The following professionals can take up this course:
  • Developers aspiring to be a 'Data Scientist'
  • Analytics Managers who are leading a team of analysts
  • 'R' professionals who want to capture and analyze Big Data
  • Business Analysts who want to understand Machine Learning (ML) Techniques
Basic Understanding of R will be necessary in order to take up this course

Edureka’s Programmer using Advanced Predictive Modelling in R Certificate Holders work at 1000s of companies like

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I have been part of Edureka family more than an year and I appreciate all the effort that edureka make for bringing the required courses to make learning easier with affordable prices. I have been part of Java/J2EE and SOA course. The courses were brilliantly designed to make the leraners understand and gain confidence on what they are learning for. I got more than what I am looking for and gathered so much of information and confidence which makes me proceed in the direction which I wished to. Thanks for Edureka and their team!!!

Venkateswarlu ponna link Agile Test Lead (UAT)

Edureka is Best Online training in throughout my career (11 years). I subscribed for DevOps and course is well organized and will get hands by just following PPT, Videos and Lab exercises (Before they launch any course they do lot of home work). Best thing about Edureka is when you stuck while doing Lab exercises just mail to support team, they will call and guide to solve it for sure. It has excellent trainers and support team with 24x7 support.

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Edureka is a revolution in Big Data training. I have taken 5 courses through Edureka from Hadoop, R, Data Science and Python. They have perfect material prepared professionally by highly qualified professionals and their Learning management system is very resourceful. All the links and virtual machines work well and well designed. Instructors for the courses are very well selected. They are very responsive to their customer needs and responds within minutes to questions. They have redefined big data learning for me and I am a loyal customer of Edureka. They keep innovating and add newer courses every month.

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Edureka is providing the best software training I have seen in my 10 years of IT career. I have been an Edureka student for over one year now. Having completed courses such as AWS Architect Certification Training, DevOps Certification Training and Hadoop Administration. I must say Edureka has excellent course content for some of the latest software technologies and is suplimented by well experienced trainers. I am impressed by all the trainers I came across with edureka. Also Edureka provides unique training platform where each live session of the course is recorded and this can be played back unlimited times by the student and has lifetime access to these recordings. This is great way to learn and stay ahead.

Sudhiranjan Sarma link Analytics and Information management, Cognizant

My experience with edureka about Python is much impressed, with such quality of the training. We get access to each day class recorded sessions after the live class. Edureka is very quick reactive towards the queries of each single user, they answer and follow-up to ensure you get the correct response to resolve your query. Online recording of the course is very useful as you can go back and refer at any time. You have access to course and materials for ever, which is really helpful. More over 24/7 quality support, which is very much important to any of us. I must thank to Edureka for giving such support. Thanks guys, cheer…

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I found the big data course from Edureka to be comprehensive, and practical. Course instructor was very knowledgeable, and handled the class very well in terms of making it interactive, keeping it interesting, and responding to all questions from students. The support staff was excellent as well, engineers assisted me remotely when I encountered an issue with HDFS even after the course was over. Overall, highly recommend Edureka for big data training and more.

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I took PMP online classes with edureka. Just wanted to let you know that I was successfully able to pass the PMP exam couple of weeks ago. I enjoyed learning the concepts of PMP through edureka by the excellent laid out structure.Instructor's method of teaching was very helpful. During the classes he went over the concepts in detail and also clarified all the questions very patiently. He also shared his real world experiences which helped the student to relate to the topic. Even after the completion of course work, while reviewing the chapters in LMS, I struggled in few areas and when I reached out to the instructor without any hesitation, he explained it to me by providing some good examples.Wanted to take a moment to thank the people who contributed the most in my success.

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Self Paced Learning

You will undergo self-paced learning where you will get an in-depth knowledge of various concepts that will be covered in the course.

Real-life Project

Towards the end of the course, you will be working on a project where you are expected to implement the techniques learnt during the course.

Case Studies

Each module will contain case study, which can be completed before going to next module.

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You will get lifetime access to all the videos,discussion forum and other learning contents inside the Learning Management System.


edureka certifies you as an expert in Advanced Predictive Modeling based on the project reviewed by our expert panel.


We have a community forum for all our customers that further facilitates learning through peer interaction and knowledge sharing.
You will never miss a lecture at Edureka! You can choose either of the two options:
  • View the recorded session of the class available in your LMS.
  • You can attend the missed session, in any other live batch.
We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight into how are the classes conducted, quality of instructors and the level of interaction in a class.
All the instructors at edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.
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