Overview
Sapphire Global Advanced Predictive Modelling in R Certification Training makes you an expert in using Advanced Predictive Modelling in R Certification concepts. Enroll now for Advanced Predictive Modelling in R Certification online training and get through the concepts of data, by utilizing the internal memory for storing a working set. Sapphire Global introduces all the key concepts in Advanced Predictive Modelling in R Certification to help the learner gain more knowledge. Enroll & Become an Advanced Predictive Modelling in R Certification Consultant.
Course Curriculum
Key features
This course will prepare you to:
- Explain the architecture of the Advanced Predictive Modelling in R Certification component
- Configure and use new functionalities in Advanced Predictive Modelling in R Certification
- Use the standard Advanced Predictive Modelling in R Certification Sub Modules.
- Explain the Advanced Predictive Modelling in R Certification Controlling Configuration and Customization option.
Basic Statistics in R
- Covariance & Correlation
- Central Limit Theorem
- Z Score
- Normal Distributions
- Hypothesis
Ordinary Least Square Regression 1
- Bivariate Data
- Quantifying Association
- The Best Line: Least Squares Method
- The Regressions
- Simple Linear Regression
- Deletion Diagnostics and Influential Observations
- Regularization
Ordinary Least Square Regression 2
- Model fitting using Linear Regression
- Performing Over Fitting & Under Fitting
- Collinearity
- What is Heteroscedasticity?
Logistic Regression
- 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
Advanced Regression
- 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
Imputation
- 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
Forecasting 1
- 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
Forecasting 2
- 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
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
Survival Analysis
- Time-to-Event Data
- Censoring
- Survival Analysis
- Types of Censoring
- Survival Analysis Techniques
- PreProcessing
- Elastic Ne
Practice Test and Interview Questions
Practice Test and Interview Questions
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