IIRF Online > Development > Data Science > R (programming language) > Advanced Predictive Modelling in R Certification Training
Advanced Predictive Modelling in R Certification Training by Edureka
Skills you will learn!
Curriculum
5 Topics
Covariance & Correlation
Central Limit Theorem
Z Score
Normal Distributions
Hypothesis
7 Topics
Bivariate Data
Quantifying Association
The Best Line: Least Squares Method
The Regressions
Simple Linear Regression
Deletion Diagnostics and Influential Observations
Regularization
4 Topics
Model fitting using Linear Regression
Performing Over Fitting & Under Fitting
Collinearity
What is Heteroscedasticity?
10 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
11 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
14 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
21 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
12 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
6 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
7 Topics
Time-to-Event Data
Censoring
Survival Analysis
Types of Censoring
Survival Analysis Techniques
PreProcessing
Elastic Net
Advanced Predictive Modelling in R Certification Training
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