Predictive modeling is a technique that uses mathematical and computational methods to predict an event or outcome. A mathematical approach uses an equation-based model that describes the phenomenon under consideration. The model is used to forecast an outcome at some future state or time based upon changes to the model inputs. he model parameters help explain how model inputs influence the outcome. Examples include time series regression models for predicting airline traffic volume or predicting fuel efficiency based on a linear regression model of engine speed versus load.
About R Programming Language
R is an open-source programming language that is widely used as a statistical software and data analysis tool. R generally comes with the Command-line interface. R is available across widely used platforms like Windows, Linux, and Mac OS. Also, the R programming language is the latest cutting-edge tool.
R programming language is an implementation of the S programming language. It also combines with lexical scoping semantics inspired by Scheme.
Why R Programming Language?
R programming is used as a leading tool for machine learning, statistics, and data analysis. Objects, functions, and packages can easily be created by R programming.
It’s a platform-independent language. This means it can be applied to all operating systems.
It’s an open-source free language. That means anyone can install it in any organization without purchasing a license.
R programming language is not only a statistic package but also allows us to integrate with other languages (C, C++). Thus, you can easily interact with many data sources and statistical packages.
The R programming language has a vast community of users and it’s growing day by day.
R is currently one of the most requested programming languages in the Data Science a job market that makes it the hottest trend nowadays.
About this course:
This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. Predictive modeling 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 will let you gain expertise to understand the relationships among these variables and how their relationships can be exploited to make decisions.
Why Learn Advance Predictive Modeling using R programming?
This course will introduce you to some of the most widely used predictive modeling techniques and their core principles which are 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.
What you will learn from this course:
Understand the Basics of Statistics using R programming and Explain Regression.
Understand Simple, Multiple, Advanced, and Logistic Regression and Perform model fitting using Linear Regression.
Explain What is Heteroscedasticity?
Understand Binary Response Variable and Linear Probability Model.
Explain Imputation and Understand Forecasting or Learn Neural Networks.
Explain Dimensionality Reduction.
Understands the algorithms associated with Dimensionality Reduction and Understand Survival Analysis.
Who this Course is for:
Developers aspiring to be a ‘Data Scientist’.
Analytics Managers who are leading a team of analysts.
R programming professionals who want to capture and analyze Big Data.
Business Analysts who want to understand Machine Learning (ML) Techniques.
Syllabus:
1. Basic Statistics in R
Covariance & Correlation or Central Limit Theorem.
Z Score, Normal Distributions, and Hypothesis.
2. Ordinary Least Square Regression 1
Bivariate Data, Quantifying Association, and The Best Line: Least Squares Method.
The Regressions and Simple Linear Regression.
Deletion Diagnostics, Influential Observations, and Regularization.
3. Ordinary Least Square Regression 2
Model fitting using Linear Regression.
Performing Over Fitting & Under Fitting.
4. Logistic Regression
Binary Response Regression Model and Linear regression as Linear.
Probability Model Problems with Linear Probability Model.
Logistic Function and Curve.
Goodness of fit matrix and All Interactions Logistic Regression.
Multinomial Logit, Interpretation, and Ordered Categorical Variable.
5. Advanced Regression
Poisson Regression, Model Fit Test, Offset Regression, Poisson Model with Offset, and Negative Binomial.
Zero-Inflated Poisson Models and Zero-Inflated Negative Binomial.
Variables used in the Analysis, Poisson Regression Parameter Estimates, and Dual and Hurdle Models.
6. Imputation
Missing Values are Common, Types of Missing Values and Why is Missing Data a Problem?
No Treatment Option: Complete and Available Case Method.
Problems with Pairwise Deletion, Mean Substitution Method, Imputation, and Regression Substitution Method.
K-Nearest Neighbour Approach, Maximum Likelihood Estimation, and EM Algorithm.
Single and Multiple Imputation and Little’s Test for MCAR.
7. Forecasting 1
Need for Forecasting, Types of Forecast, and Forecasting Steps.
Autocorrelation, Seasonality, Forecast Error, and Correlogram.
Time Series Components and Variations in Time Series.
Mean Error (ME) MPE and MAPE—Unit free measure and Additive v/s Multiplicative Seasonality.
Curve Fitting, Simple Exponential Smoothing (SES), and Decomposition with R.
Generating Forecasts, Explicit Modeling, Modeling of Trend, Seasonal Components, Smoothing Methods, and ARIMA Model-building.
8. Forecasting 2
Analysis of Log-transformed Data.
Partial Regression Plot, Normal Probability Plot, Tests for Normality.
Box-Cox Transformation, Box-Tidwell Transformation, Growth Curves, and Logistic Regression: Binary.
Neural Network and Network Architectures and Neural Network Mathematics.
9. Dimensionality Reduction
Factor Analysis and Principal Component Analysis.
Mechanism of finding PCA and Linear Discriminant Analysis (LDA).
Determining the maximum separable line using LDA and Implement Dimensionality Reduction algorithm in R.
Prerequisites:
A basic understanding of R programming will be necessary in order to take this course.
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