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Mastering Predictive Analytics With R

There are many great resources available to learn predictive analytics using any number of programming languages. You can start with books, YouTube videos, or by picking up some coding skills yourself.

Many companies have their internal tools that they use for predictive modeling, so it is not necessary to create your own from scratch. By learning how these tools work, you can pick and choose what features matter to you, and then add your own tweaks to make them work better for your organization.

In this article, we will be exploring one such tool — R. What makes R unique is that it is free software which means you do not need to pay monthly fees to access its functionality. It also does not require much in terms of computing knowledge as there are lots of ways to get help when needed.

R was designed to facilitate statistical analysis and computational tasks. This includes things like regression (prediction models), correlation (relationships) studies, factor analyses, and more. Because of this, it is no surprise that it is a very popular choice among data scientists.

Interpret results

mastering predictive analytics with r

When you are doing predictive analytics, you will be generating large amounts of data. These datasets can get very long so having some basic understanding of statistics is helpful.

Interpretation of statistical results depends upon what your goal is. If you want to know how likely it is that someone with brown hair weighs less than average people then determine the averages for each variable (hair color and weight) and compare them to the other variables in the population.

This process is called regression analysis and produces an odds ratio or probability that describes this likelihood. For example, if there were only people with white skin in the world, then determining the average amount of skin per person would be a way to calculate odds!

If you wanted to predict whether someone’s parents went to college, then look at the average number of years their parent’s did not go to school and see if they have a degree. This theory predicts that those who do not have a degree probably never finished high school, so there is a good chance they did not spend time going through college.

Statistics such as these tell us something about the relationships between different factors in our society, but they cannot tell us anything definitive about individual members of the population.

Apply predictive modeling strategies

The second way to use machine learning with R is by applying it directly onto existing data sets or using it for creating new datasets. This approach is referred to as applied predictive analytics, and you will see it done frequently in business settings.

For example, say your company wants to know which advertisements perform best on YouTube. You could create a predictive model that determines this information through trial and error.

Your model would look at all of the past ads that performed well and then determine what features of the ad worked by analyzing the underlying data.

These features could be things like the length of the video, whether there were pictures or not, if the person speaking was clear, etc. Once the feature set is determined, the model can then test different combinations of these features to determine which ones work for advertising videos on YouTube.

By doing this, you don’t need to go into detail about how each element of an advertisement works.

Create a forecast

mastering predictive analytics with r

A predictive model is simply a set of steps that predict an outcome. In other words, it looks at present data to make assumptions about what will happen in the future!

A lot of business applications use regression models to create forecasts. These are statistical methods that determine whether past behavior predicts future results. For example, if you have never purchased chocolate then there is no reason to believe that you will one day buy the box of truffles that takes up half your bedroom.

Predictive analytics goes beyond just binary predictions though. It can also calculate probabilities for different outcomes which help inform decisions.

For instance, a company might find that although people with diabetes are more likely to suffer health complications, they only have a 1% chance of having such a complication within the next year. This means that unless someone with diabetes does something drastic (like give up sugar or start wearing glucose monitoring equipment), their medical condition is not particularly urgent.

That could be a good thing since it gives them time to prepare for the upcoming event! However, it still makes sense to monitor blood sugar levels because even small changes may indicate disease development.

Validate your model

mastering predictive analytics with r

The second fundamental component of predictive analytics is validation. This can be done in two ways: internal or external. Internal validation tests whether your algorithm produces accurate results for examples that you provide to yourself, such as by testing how well your regression algorithms work.

External validation compares predicted values with actual values from sources outside of your data set, suchas comparing what industry averages are with what yours are. Both of these methods check if your predictions match reality!

When doing predictive modeling, it is important to remember that no mathematical formula will always predict correct answers.

Modify your model

mastering predictive analytics with r

A common way to test predictive models is by modifying the variables in the equation. This can be done through adding or removing features, changing the value of a feature, or creating new features.

The easiest way to do this is via logistic regression where you can remove features or add new ones. For example, if you wanted to determine whether someone will lie about their income, you could use age as a predictor. If people are lying about their income, then they are older than average. Therefore, removing age would not make it difficult for someone to tell the truth about their income!

Alternatively, if we were trying to find out who makes more money, there is an easy fix to that- how much does the person drive? If the answer is yes, then the individual probably drives a lot so they earn more money due to all of the expenses associated with owning a car.

Prepare for a crisis

mastering predictive analytics with r

When you are ready to start predictive analytics with R, there is one thing that you should be prepared for. A major part of learning any new skill or area of study is having to deal with difficult material. This can mean trying things out on your own, looking up answers, and potentially asking someone for help!

That is what this topic section will talk about! You will learn how to access and use resources effectively to take your predictive analytics skills to the next level. We will also go over some quick tips and tricks to help you become more familiar with the software and predictive modeling concepts.

Resources

There are many great sources available to you at anytime. Some are free while others cost money, but they all have something valuable inside!

By adding these educational tools into your repertoire, you will never run out of knowledge. These could include YouTube videos, blogs, and lecture notes and decks.

Software

Predictive analytics applies statistical models to predict future events. There are several different types of models that can be used, such as logistic regression which works best when there are only two possible outcomes (binary classification).

Some common softwares used for predictive analysis in business are: SAS, SPSS, Stata, Minitab, and Excel.

Respond to a crisis

mastering predictive analytics with r

In this era of social media, where people constantly update their statuses, followers are often left feeling overwhelmed and stressed out. People feel as if they have to keep up with all the updates and messages that others post, and it can sometimes make them unhappy or even depressed.

For employers, these posts can also be an indication of a potential employee’s mental state. If you work for a company that publishes frequent emotional status updates, it can give your colleagues a clue about how you might respond in a stressful situation.

It is important to note that while it may seem like there’s nothing you can do to prevent someone else’s bad mood, you can take some steps to reduce your own stress level.

By being aware of what types of things cause stress for you and taking precautions to avoid those situations, you will probably find yourself more relaxed and able to handle whatever comes along. This will help you perform your job better and potentially inspire other employees to do the same.

Take advantage of predictive analytics

mastering predictive analytics with r

A predictive analytic tool that has seen significant growth in popularity is Machine Learning (ML). ML algorithms learn as they go, changing themselves depending on what they are exposed to.

A common analogy for explaining machine learning is watching a movie. At first, the characters talk about things that happen every day, but as the story unfolds, the characters’ dialogue changes and new themes emerge.

That metaphor helps bring into focus how ML works. When you start a movie with no clues about what will occur next, the plot twists and turns out of the control of the writer or director. But as time goes by, the film-maker uses all the information available to him/her to help make decisions about what comes next.

In the same way, an ML algorithm learns from past experiences and predicts what it thinks will occur in the future. For example, if there have been several cases of people doing something before, then the algorithm will assume that more instances of that behavior is likely to lead to more instances of someone committing a crime.

Using this theory, the software develops its own set of rules and applies them to new data. Because each individual instance of the algorithm is different, users can also apply their knowledge to effectively use the technology towards their personal goals.

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