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3 Best Practices For Predictive Data Modeling

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3 Best Practices For Predictive Data Modeling


Predictive modeling is used to develop models that use past occurrences as reference points for organizations to forecast future business-related events and make clever decisions.

It is heavily involved in the strategy-making processes of companies in industries such as healthcare, law enforcement, pharmaceuticals and many more. The practices that can be used to make predictive data modeling error-free can be of great importance to everybody.

Predictive data modeling involves the creation, testing and validation of data models that will be used for predictive analysis in businesses. The lifecycle management of such models is a part of predictive data modeling. Such models, which use data captured by AI systems, machine learning tools, and other sources, can be used in advanced predictive analysis software systems used by organizations. The predictive data modeling process can be broken down into four steps:

  • Developing a model

  • Testing the model

  • Validating the model

  • Evaluating the model

There are a significant number of application areas for predictive analysis, such as financial risk management, international trade, clinical trials, cancer detection and many others. As we can see, each application area specified above is sensitive to mistakes or prediction inaccuracies. An inaccurate prediction could lead to incorrect diagnoses, potential patient deaths or financial turmoil in such industries. Therefore, organizations must implement certain practices to optimize the process of predictive data modeling. They must also continuously monitor the performance of the models.

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1)  Keeping the First Model Simple

As a process, predictive data modeling uses plenty of resources before organizations can expect it to bear fruit for them. Therefore, the competence of IT infrastructure present in the organization to carry out predictive data modeling is vital for streamlining the process without lag or inefficiencies. Accordingly, businesses must invest time and money to firstly make sure that their IT infrastructure is able to handle the process. This can be made sure with actions such as checking network connectivity, checking internet speeds, cybersecurity-related elements, and other factors before your business can use predictive data modeling. Additionally, your business needs to make sure that all your IT tools are aligned perfectly to make the model development process smoother.

More importantly, the first model created by an organization need not be overly complex or fancy. The first model will not be used for hardcore endpoint applications. A simplistic model provides the metrics and behaviors that can be used as a yardstick to test bigger and more complicated data models in the future. During the initial phases, businesses need to answer a few queries related to carrying out the predictive data modeling process. Some of such questions are related to the number of features needed to test a specific hypotheses, whether features that are useful are practical to make for the future, where they can store a model for maximum data security and threat protection, and finally, whether every significant decision-maker believes that the architecture and tools present currently in the organization are good enough to carry out the process.

Having an advanced hardware and software infrastructure conducive to predictive data modeling is vital for the process to be a success. Maintaining the simplicity of the first data model is valuable to train other, more complex models easily in the future.

2)  Validating Models Consistently

Result validation involves organizations running their model and evaluating its results with visualization tools. To carry out the validation process, organizations need to understand how business data is generated, and how it flows through organizational data networks. As we know, today, data analytics is highly integrated into nearly every business aspect. Individuals at every level in an organization use company resources and the web to make calculated business decisions. Information is gathered for predictive model training purposes too. Accordingly, getting the datasets that can be used to train predictive models also requires a lot of effort. The level of effort involved in data collection means that predictive models are quite highly valued, and each model may have the power to influence organizational data compliance (in a good or a bad way), financial bottom lines, as well as the creation of legal risks for the organization. As a result, such high-value assets need to be validated consistently.

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Additionally, businesses may be under the impression that model validation is a one-off process and does not need to be carried out in the future. However, as an expert in the field of model training will tell you, that is a misconception. Predictive models need to constantly evolve with time to become more adept at making accurate forecasts, and so, the validation process needs to take place on a consistent basis. Here are some of the tasks that must certainly be carried out in the validation process:

The Thorough Validation of ‘Predictor’ Variables

A model is made up of several variables. Some of those variables may have strong predictive abilities. Such variables are labeled ‘predictors’ due to those capabilities. While predictors are useful for regular business work, they may, in some cases, also cause unwanted risk exposure for their organization when they are used for predictive analysis. For example, the absence of ultra-personal details of users in models may be a conscious effort taken by network administrators to not fall into legal troubles regarding privacy violations of users.

The Validation of Data Distribution

This type of validation is carried out by organizations to get an understanding of the distribution of predictor and target variables. Over time, there may be distribution shifts in such variables and models. If such shifts are detected in variables within data models, such models will have to be retrained with new data as they wouldn’t be able to provide predictive analysis with accuracy.

The Validation of Algorithms

As we know, analytical algorithms are used to train models. Validation must be done for algorithms that train models, which go on to carry out predictive analysis in businesses. Also, only certain types of models can provide clear, interpretable predictions. For example, there are multiple types of models, such as decision trees and neural networks. Decision trees provide more open and interpretable—albeit less accurate—results whereas neural networks do not—with more accurate results. So, decision trees must be validated more frequently as they participate more in predictive analysis. Data administrators need to choose between interpretability and prediction accuracy when carrying out the validation of such algorithms.

Compare Model-Prediction Accuracy Tests

To know the actual competence of a model, it must be compared with other models for accuracy. The most accurate models must be used in predictive analysis systems. This is also a validation task and must be carried out regularly if newer, more accurate models enter the fray with time. After all, the improvements in predictive analysis performance carry on perpetually.

Additionally, tasks such as auditing of models, and keeping track of every validation log entry are included under the umbrella of validation. Finally, the performance of models is monitored before and after deployment. Before deployment, businesses must test them for operational glitches that may impact their decision-making and predictive capabilities. Pre-deployment checking is essential because most models chosen for predictive analytics are used in real-world environments.

After a model is deployed, it needs to be monitored for wear, as, generally, models tend to degrade over time. So, validation helps with phasing such models out from a predictive analytics system and replacing them with new, useful ones. With constant validation, models could become less error-prone and more time-efficient. Constant validation is a potent practice as it improves the predictive data modeling process in several ways.

3) Recognizing Data Imbalances

Imbalanced data is a classification issue where the number of observations per class is not equally distributed. As a result, there may be a higher number of observations for a given class—known as a majority class—and much fewer observations for one or other classes—known as minority classes. Data imbalances cause inaccuracies in predictive analysis.

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A data imbalance in a model can cause it to be erratic, and not very useful. For example, let’s talk about a fraud forecasting system proposed for a bank. Now, the bank may have a record of 95%— meaning that 95% of its transactions turn out to be non-fraudulent. In an imbalanced system, a system may state that the bank is 100% safe. Now, while the system may be right, and the bank will face fraud only 5% of the time, the forecast system will be in trouble whenever any fraud takes place because the system had clearly stated that the safety quotient was at a 100%.

Predictive data modeling is a tough task in the current digital world due to certain potential weaknesses that may creep into its functioning. By following the best practices, businesses can be sure of avoiding poor forecasting.



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TECHNOLOGY

The Best Programming Languages for Web Services and Their Advantages In 2022

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The world is becoming more digital, and the need to learn digital skills is higher now than ever before. One such digital skill that has gained more prominence since the last decade is programming languages. We now live in a world revolving around technology, and basic knowledge of programming languages is becoming a necessity for many career options and job profiles. 

There are many reasons to learn about the different programming languages and their advantages, and web services or web development is a big part of it. Web development is an ever-growing field and has plenty of opportunities. To grab these opportunities in web development, you need to equip yourself with the correct programming language.  However, the plethora of language options available to you can be confusing, especially if you are a beginner. 

Javascript

This high-level language, often used by most websites as a client-side language, is a core technology in the World Wide Web. Initially, it was used only for developing web browsers; however, they now have new usages in non-web browser applications and server-side website deployments. 

Since its creation in 1995, it has evolved into one of the world’s most popular programming languages and is highly sought after by various organizations. 

Pros

  • Learning and implementing javascript is easy.
  • It is very popular and is everywhere on the net.
  • It can run immediately in the client-side browser.
  • It doesn’t demand much from the website server. 

Cons

  • Different browsers may interpret it differently sometimes, so writing cross-browser code is difficult.

Python

This is a powerful programming language and is one of the best used in web development for multiple purposes. Furthermore, this open-source language is very easy to learn and deploy and is among the best languages for learning web development since it develops scalable applications and web pages. 

Python has a unique selling point: the simple, productive, and elegant design of this language. It is the best for backend development with first-class integration with other programming languages such as C++ and C. 

Python has numerous usages, especially in developing web and desktop applications and GUI-based applications such as machine learning, data science, and network servers. It also offers multiple toolsets for mathematics, statistics, and computational science with numerous libraries and frameworks like NumPy, Pandas, SciPy, and Scikit-Learn. 

Pros 

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  • It increases the productivity of programmers and developers. 
  • It supports several systems and platforms.
  • It follows object-oriented programming. 
  • It ensures easy scalability, even with complex web applications.
  • Offers numerous support libraries. 
  • It focuses on code readability. 

Cons

  • The database is primitive and slightly underdeveloped.
  • Not ideal for mobile computing.

HTML

HTML stands for HyperText Markup Language. This language is the foundation upon which web development is built, and every developer must familiarize themselves with it. It is a very popular language and is currently one of the most used programming languages. While it cannot be described as a full-fledged programming language, HTML is the standard language for creating web pages. 

HTML gives web pages structure and ensures that images and texts are correctly formatted. In addition, it is a fast language and helps developers design web pages much quickly because it uses templates. 

Pros 

  • Learning and implementing is easy. 
  • It is free and accessible.
  • All browsers support it. 

Cons

  • This language is static, so creating dynamic pages is impossible. 
  • It requires lots of code to develop a simple webpage. 

PHP

This is an ideal language for web development. Even though many other programming languages like python and javascript are also suitable for web development, the need for professional developers who work with PHP remains very high in the market. It is a general-purpose language that can be used for developing server-side applications. In addition, PHP is dynamic and maintained by open-source platforms working across Windows. UNIX, and Mac systems. 

PHP is highly productive in web development and is used vastly within this industry to build and deploy applications quickly. 

Pros

  • It has plenty of frameworks to offer. 
  • It can be used to develop web pages with ease. 
  • It supports object-oriented and functional programming paradigms.
  • It also supports multiple automation tools used for testing and deploying applications.
  • It uses the Xdebug extension, which allows for great debugging. 
  • It has a large ecosystem with incredible community support. 

Cons 

  • It doesn’t have features for handling errors and security. 
  • It is slower to build a web page using this language alone than other programming languages. 

CSS

CSS is similar to HTML because it is also a basic language that all developers should know. CSS is the acronym for Cascading Style Sheets. This language is mainly used along with HTML to govern a web page’s style. Combining CSS and HTML languages is often the mother of programming languages. For example, CSS determines the color, positions, and size of elements within a webpage. 

CSS uses a cascading style, which means an applied style cascades down from the parent to the child throughout the web page. This cascading nature allows it to uphold consistency. 

Pro

  • It is a beginner-friendly language that’s easy to learn.
  • CSS supports responsive website design. 
  • It simplified web page customization
  • It increases the webpage loading speed. 

Cons 

  • It is possible to suffer cross-browser issues.
  • It has multiple levels, such as CSS2 and CSS3, which may lead to confusion.

C++

This middle-level programming language was created in 1980 as an Objective C extension. C++ language is simple and easy to learn for beginners, and it has lots of usefulness. It offers everything that Objective C offers and more. It supports features such as object-oriented programming, except type checking and handling. 

C++ supports OOP concepts such as encapsulation, inheritance, and abstraction. The compiling mechanism is quick and efficient, and it has a massive standard library. 

Pros 

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  • It is highly scalable. 
  • It is portable and easy to handle.
  • It has extensive community support. 

Cons

  • Garbage collection is not automatic.
  • The pointers have associated complexities. 

Conclusion 

There are numerous options for programming languages to learn for web development in 2022. However, you could get spoiled for choice and miss your way if you’re a beginner and new to the web development scene. Therefore, this article discusses some of the best and most popular programming languages you can kick start your web service journey in 2022. 

Author Bio

Eun Rockwell is a professional journalist with extensive experience, four years. She also works as a freelancer and writes a lot of articles. In addition, she has had writing stints at My-assignment.help and Superiorpapers. Eliza is always focused on doing quality work to achieve her goals and objectives. In addition, Eliza is fascinated by creating original works that meet high standards. Feel free to connect with her by email. 

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