The Advanced Predictive Modelling in R Certification Training course is designed to help learners master the techniques of predictive analytics using the R programming language. It covers data preparation, statistical modeling, machine learning algorithms, and model evaluation. This course is ideal for data scientists, analysts, statisticians, and professionals in analytics-driven roles who want to deepen their skills in forecasting, classification, and regression modeling.

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Instructor led live online Classes

Advanced Predictive Modelling in R Certification Training

Instructor led live online Training (Weekday/Weekend)

Why Enroll in the Advanced Predictive Modelling in R Course?

Deepen Analytics Skills: Learn to build, validate, and deploy predictive models using R.
Career-Boosting Certification: Earn a recognized credential in advanced data analytics.
Hands-On Learning: Apply concepts to real-world datasets and business problems.
Comprehensive Curriculum: Covers everything from data preprocessing to advanced model evaluation techniques.
High Demand Skills: Predictive modeling is a top skill across industries like finance, marketing, and healthcare.

Course Description

This course focuses on the use of R programming for developing, tuning, and evaluating predictive models.

1. Data Scientists and Analysts seeking advanced modeling skills. 2. Statisticians and Academics working with large datasets. 3. Business Intelligence Professionals aiming to implement predictive techniques. 4. R Programmers looking to apply their skills in analytics.

1. Teaches best practices for feature engineering and model validation. 2. Prepares you for real-world implementation of predictive solutions. 3. Includes expert guidance, live sessions, and project work.

What you'll learn

Data Preparation: Handling missing data, outliers, and feature engineering.

Regression Techniques: Linear, logistic, ridge, and lasso regression.

Classification Algorithms: Decision trees, random forest, and support vector machines.

Model Validation: Cross-validation, AUC, ROC, and confusion matrix.

Time Series Forecasting: ARIMA, exponential smoothing, and trend analysis.

Unsupervised Learning: Clustering techniques like K-means and hierarchical clustering.

Requirements

Working knowledge of R programming and basic statistics.
Prior experience with data manipulation and analysis is helpful.

Curriculum Designed by Experts

Covariance & Correlation
Central Limit Theorem
Z Score
Normal Distributions
Hypothesis

Calculating statistical parameters such as mean, median, mode and making custom visualizations for developing intuition of data with respect to statistical parameters.

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

Ridge and Lasso regression implementation.

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

Perform exploratory data analysis and check for heteroscedasticity, perform remedial steps and transform the data and implement linear regression model.

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

Build a logistic regression model to classify the data.

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

Create ZINB and Hurdel regression model.

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

Implement KNN model and perform single and multiple imputation.

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

Implement Exponential Smoothing and ARIMA model for time series forecasting.

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

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

Implement Principal component analysis and Boosting(ADAboost).

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

Do PCA preprocessing and implement Elastic Net model.

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Advanced Predictive Modelling in R Certification Training

Read Review

Instructor led live online Classes

Advanced Predictive Modelling in R Certification Training