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.
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‚Äô identification and treatment.¬†
Model fitting using Linear Regression
Performing Over Fitting & Under Fitting
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
Goodness of fit matrix
All Interactions Logistic Regression
Ordered Categorical Variable
Hands On: Build a logistic regression model to classify the data.
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.¬†
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).
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
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.
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.
You no longer need a credit history or a credit card to purchase this course. Using ZestMoney, we allow you to complete your payment with a EMI plan that best suits you. It's a simple 3 step procedure:
Fill your profile: Complete your profile with Aadhaar, PAN and employment details.
Verify your account: Get your account verified using netbanking, ekyc or uploading documents
Activate your loan: Setup automatic repayment using NACH to activate your loan
Our Pricing (USD)
Advanced predictive modelling in r certification training