Machine Learning vs Predictive Analytics – 7 Useful Differences

Difference Between Machine Learning and Predictive Analytics

Machine learning

Machine learning is an area in computer science, which is growing leaps and bound these days. Recent progress in hardware technologies which resulted in massive increase in computational power such as GPU (graphical processing units) and advancement in neural networks, machine learning has become a buzz word.

Today, the world is generating more data than ever before, and now that we have the capacity to store a massive amount of data, the popularity and usability of machine learning is bound to increase. Due to this rapid increase in the need to analyze and extract information from data, a requirement for machine learning engineers and data scientists are only increasing. Hence, it has become very important to understand what machine learning exactly is and how one can take advantage of this recent surge.

Essentially, using machine learning techniques, we can build algorithms to extract data and see important hidden information from it. Predictive analytics is also a part of machine learning domain which is limited to predict future outcome from data based on previous patterns. While predictive analytics has been in use since more than two decades mainly in banking and finance sector, application of machine learning has taken prominence in recent time with algorithms like object detection from images, text classification, and recommendation systems.

Machine learning internally uses statistics, mathematics, and computer science fundamentals to build logic for algorithms that can do classification, prediction, and optimization in both real times as well as batch mode.

Classification and Regression are two main classes of a problem under machine learning. Let’s understand both Machine Learning and Predictive Analytics in detail.

Classification

Under these bucket of a problem, we tend to classify an object based on its various properties into one or more classes. For example, classifying a bank customer to be eligible for a home loan or not based on his/her credit history. Usually we would have transactional data available for the customer like his age, income, educational background, his work experience, industry in which he is working, number of dependents, monthly expenses, previous loans if any, his spending pattern, credit history etc. and based on this information we would tend to calculate if he should be given loan or not.

There are many standard machine learning algorithms which are used to solve classification problem. Logistic regression is one such method, probably most widely used and most well know, also the oldest. Apart from that we also have some of the most advanced and complicated models ranging from decision tree to random forest, AdaBoost, XP boost, support vector machines, naïve baize and neural network. Since the last couple of years, deep learning is running at the forefront. Typically neural network and deep learning are used to classify images. If there are hundred thousand images of cats and dog and you want to write a code that can automatically separate images of cats and dog, you may want to go for deep learning methods like a convolutional neural network. Torch, cafe, sensor flow etc. are some of the popular libraries in python to do deep learning.

To measure the accuracy of regression models, metric like false positive rate, false negative rate, sensitivity etc. are used.

Regression

Regression is another class of problem in machine learning where we try to predict a continuous value of a variable instead of a class unlike in classification problem.  Regression techniques are generally used to predict the share price of a stock, sale price of a house or car, a demand for a certain item etc. When time series properties also come into play, regression problems become very interesting to solve. Linear regression with ordinary least square is one of the classic machine learning algorithms in this domain. For time series based pattern, ARIMA, exponential moving average, weighted moving average and simple moving average are used.

To measure the accuracy of regression models, metric like mean square error, absolute mean square error, root measure square error etc. are used.

Predictive Analytics

There are some areas of overlap between machine learning and predictive analytics. While common techniques like logistic and linear regression come under both machine learning and predictive analytics, advanced algorithms like a decision tree, random forest etc. are essentially machine learning. Under predictive analytics, the goal of the problems remains very narrow where the intent is to compute a value of a particular variable at a future point of time. Predictive analytics is heavily statistics loaded while machine learning is more of a blend of statistics, programming, and mathematics. A typical predictive analyst spends his time computing t square, f statistics, Innova, chi-square or ordinary least square. Questions like whether the data is normally distributed or skewed, should student’s t distribution be used or bells curve be used, should alpha be taken at 5% or 10% bug them all the time. They look for the devil in details. A machine learning engineer does not bother with many of these problems. Their headache is completely different, they find themselves stuck on accuracy improvement, false positive rate minimization, outlier handling, range normalization or k fold validation.

A predictive analyst mostly uses tools like excel. Scenario or goal seek are their favorite. They occasionally use VBA or micros and hardly write any lengthy code. A machine learning engineer spends all his time writing complicated code beyond common understanding, he uses tools like R, Python, Saas. Programming is their major work, fixing bugs and testing on different landscape a daily routine.

These differences also bring a major difference in their demand and salary. While predictive analysts are so yesterday, machine learning is the future. A typical machine learning engineer or data scientist (as mostly called these days) are paid 60-80% more than a typical software engineer or predictive analyst for that matter and they are the key driver in today’s technology-enabled world. Uber, Amazon and now self-driving cars are also possible because of them only.

Head To Head Comparison between Machine Learning vs Predictive Analytics (Infographics)

Below is the top 7 Comparision between Machine Learning vs Predictive Analytics

Machine Learning vs Predictive Analytics Comparision Table

Below is the detailed explanation of Machine Learning vs Predictive Analytics

Conclusion – Machine Learning vs Predictive Analytics

From above discussion on both Machine Learning and Predictive Analytics, it is clear that predictive analytics is basically a sub-field of machine learning. Machine learning is more versatile and is capable to solve a wide range of problems.

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