Introduction to SAS Predictive Modelling

SAS Predictive Modellingis an end-to-end machine learning project using SAS Enterprise Miner, also known as SAS E minor software. This will use three machine learning algorithms: decision trees, gradient boosting, and regression.

SAS Predictive Modellingis a vital to have a solid understanding of the data set. Let’s take one example to clearly under what is SAS Predictive Modelling?

An Excel spreadsheet will be utilized to analysevarious variables for a sports man, including player ID, age, experience level, participation in international events, dietary habits (categorized as veg or non-veg), gender, coach type, and surface specialist type.

These variables will be grouped into three categories: full-time coach, part-time coach, and no coach; part-time coach and no coach; and surface specialist type.

Here, we can see that the data dictionary tab will offer a detailed explanation of the variables being utilized in the study. By this you can thoroughly understanding the data set before commencing the project.

What is SAS Predictive Modelling?

SAS Predictive Modelling uses statistical and machine learning methods to generate predictive models from historical data.

It predicts customer attrition, fraud detection, risk assessment, and marketing response using regression, decision trees, random forests, neural networks, and time series analysis.

SAS Predictive Modelling runs alongside SAS data management and reporting tools to give an end-to-end data analysis and predictive modelling solution.

The software’s user-friendly interface lets analysts and data scientists construct and deploy predictive models without scripting.

Finance, healthcare, retail, and marketing use it to improve decision-making, operational efficiency, and opportunity identification.

Main Difference between nominal and categorical variables

The ID variable will be a nominal variable because it does not have any significance. The grip type variable will be a grip type because it is a nominal variable.

Data Partitioning Process

The data partitioning process involves adjusting the training and validation assumptions to ensure that the data is representative of the population.

And it also includes determining the best predictor of the target variable, which is determined by the best predictor of the target variable.

Purpose of Dividing Data Set In SAS Predictive Modelling

The purpose of dividing the data set into three categories is to apply three different predictive modeling techniques, namely decision trees, gradient boosting, and regression, to the same data set. This allows for a comparison of the performance and results of each model.

What is the use of control point in SAS Predictive Modelling?

The control point is used to connect the data partition node to the decision tree, gradient boosting, and regression nodes.

It serves as a reference point for the modeling process, allowing for the application of the three different techniques to the same data set.

By comparing the results of each model, analysts and data scientists can make informed decisions about which model is best suited for their specific predictive modeling needs.

Changes of Misclassificationrate to average squared error?

To change the selection criteria from misclassification rate to average squared error, you need to go to the model comparison node, open the properties panel, and change the default setting from the model selection tab to the average squared error. Then, re-run the regression and the selection table will be updated with the new selection statistic.

Stepwise regression to analyse of SAS Predictive Modelling

Stepwiseregression to analyse the performance of three machine learning models. We will use the properties panel to change the selection model, model selection, and selection table settings, and then run the regression to see the results.

Statistical tools of data sets in SAS Predictive Modelling

Various tools of statistics can be used in one data set, such as independent samples, t-tests, two-way Anova regression linear, regression logistic regression, chi-square tests of independence, post-hoc test two keys, Fisher’s LSD tests, residual analysis including Cook’s, distance measure studentized literate, and residuals and The Leverage Value method.

What are the differences between using Base SAS and SAS Enterprise Guide?

SAS offers a variety of tools for statistical analysis; each has their own strengths and capabilities.

Base SAS is a programming-based tool that is well-suited for complex statistical modelling and automation.

While SAS Enterprise Guide is a user-friendly GUI-based tool that is ideal for basic statistical analyses, data manipulation, and visualization.

Both tools can be used in conjunction with SAS/STAT, a SAS component that provides a wide range of statistical analysis techniques, in order to enhance the statistical capabilities of the analysis.

The choice between these tools depends on the specific needs and requirements of the project, including the level of complexity, the need for customization, and the user’s level of comfort with programming.

Advantageof the E minor SAS Enterprise Guide

The advantage of the E minor is its extensive range of statistical techniques and higher performance compared to other SAS and SAS EG models.

However, building a statistical model requires a large data size, which can take time in the Enterprise guide.

Once the data size is high, it may take longer for the Enterprise guide to run a particular model or SAS to overcome it.

Statistical method inSAS Enterprise Guide

the importance of using a statistical method to target customers and make informed decisions. This can be achieved through regression decision trees, regression L, or other statistical methods.

The analytics field is now becoming more value-driven, not only in banking but also in retail, telecom, and other industries.

Uses of statistical methods

the importance of understanding the data aspect of the analytical process. This understanding is achieved through the use of statistical methods such as linear regression or decision trees, which are known as regression L.

This approach allows for more accurate and efficient decision-making in different industries such as banking, telecom, and retail.

Thisunderstanding of data is already present and ready for further analysis. It is crucial for businesses to have this understanding in order to improve their operations and stay competitive in the market.

Emphasizingthe importance of data understanding in the field of analytics and the need for continuous improvement in this area.

By taking a more comprehensive approach to data analysis, businesses can gain a better understanding of their customers, improve their decision-making processes, and ultimately drive growth in the analytics field.

What is the process for preparing data for statistical analysis?

The process for preparing data for statistical analysis includes several steps. First, the data source is defined and a library is used to access the data.

Once the data is ready, the process continues with basic Exploratory Data Analysis (EDA), where the data is analysed to identify any outliers.

If outliers are found, they are checked and determined based on the technique needed to be used for further analysis. Once the technique is chosen, the data is analyzed using the selected method.

This process ensures that the data is prepared and cleaned before moving on to the modelling phase, where the unit of each variable is analyzed.

This comprehensive process allows for more accurate and efficient statistical analysis.

Conclusion

SAS Predictive Modeling technology is a robust tool for data analysis and predictive modeling, capable of handling large data sets and providing accurate decision-making for industries like banking, telecom, and retail.

It uses statistical methods like linear regression and decision trees to prepare data for analysis and model building.

The process includes data preparation, exploratory data analysis, and outlier detection and treatment.

This technology helps businesses understand customers, improve decision-making processes, and drive growth in the analytics field.

It also offers continuous improvement and development to ensure market competitiveness.