Advanced Predictive Modeling In R Training

Advanced Predictive Modeling in R Training

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Advanced Predictive Modeling UpComing Batches

Nov-22 - Jan-03

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Timings: 07:00 AM To 10:00 AM (IST)

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Nov-15 - Dec-27

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Dec-04 - Jan-15

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Dec-11 - Jan-22

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Dec-18 - Jan-29

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Dec-25 - Feb-05

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Timings: 20:30 PM To 23:30 PM (IST)

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Course Curriculum

Advanced Predictive Modelling Certification Training

SELF PACED

OL Tech Edu's 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.

  • WEEK 5-6
  • 10 Modules
  • 6 Hours
Advanced Predictive Modelling  Certification Training

Learning Objectives: In this module you will get a brief introduction to statistics and will conduct best test and exploratory analysis. 

Topics:
  • 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. 

Topics:
  • 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. 

Topics:
  • 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. 

Topics:
  • 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. 

Topics:
  • 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.

Topics:
  • 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, Perform Single and Multiple Imputation.


Learning Objectives: The goal of this module is to give an introduction on forecasting and time series data. 

Topics:
  • 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. 

Topics:
  • 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. 

Topics:
  • 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. 

Topics:
  • 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.

Program Syllabus

Curriculum

You can also view the program syllabus by downloading this program Curriculum.

Projects

Learn through real-life industry projects sponsored by top companies across industries.

Top two dissertations will get an all paid trip to LJMU-Engage in collaborative projects with student-mentor interaction-Benefit by learning in-person with Expert Mentors-Personalised subjective feedback on your submissions to facilitate improvement.

Learn through real-life industry projects sponsored by top companies across industries.

Top two dissertations will get an all paid trip to LJMU-Engage in collaborative projects with student-mentor interaction-Benefit by learning in-person with Expert Mentors-Personalised subjective feedback on your submissions to facilitate improvement.

Learn through real-life industry projects sponsored by top companies across industries

-Top two dissertations will get an all paid trip to LJMU-Engage in collaborative projects with student-mentor interaction-Benefit by learning in-person with Expert Mentors-Personalised subjective feedback on your submissions to facilitate improvement.

Course Description


About The Course
About the Course.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.

Objectives Of Our Online PySpark Training Course
Why Learn Advance Predictive Modeling using R?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.

Go For Online Spark Training
What are the objectives of this course ? 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 and Learn Neural Networks.
  • Explain Dimensionality Reduction.
  • Understands the algorithms associated with Dimensionality Reduction.
  • Understand urvival Analysis.

Learning With Our PySpark Certification Training
Who should go for this course?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.

Who Should Go For Our PySpark Training Course
What are the prerequisites for this course Training ? Basic Understanding of R will be necessary in order to take up this course.

Course Certification

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Features

Explore step by step paths to get started on your journey to Jobs of Today and Tomorrow.

Instructor-led Sessions

30 Hours of Online Live Instructor-Led Classes.
Weekend Class : 10 sessions of 3 hours each.

Real Life Case Studies

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various real life solutions / services.

Assignments

Assignments

Each class will be followed by practical assignments.

24 x 7 Expert Support

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.

Certification

Certification

Towards the end of the course, OL Tech Edu certifies you for the course you had enrolled for based on the project you submit.

Course FAQ's

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