How can Machine Learning boost your predictive analytics?
75% of Business leaders state ‘growth’ as the key source of value from analytics but only 60% of those leaders have predictive analytics capabilities. So what’s preventing the businesses from achieving predictive analytics capabilities? The major roadblock is applying the right set of tools, which can pull powerful insights from this stockpile of data. But first, a big data system requires identifying and storing of digital information (lots of!!). Using Machine learning and Artificial Intelligence algorithms, businesses can optimize and uncover new statistical patterns which form the backbone of predictive analytics.
Forms of Data Analysis
Organization with huge data can begin analytics. Before beginning data scientists should make sure that predictive analytics fulfills their business goals and is appropriate for the big data environment.
Let’s take a quick look at the three types of analytics –
Descriptive analytics – It is the basic form of analytics which aggregates big data and provides useful insights into the past.
Predictive analytics – Next step in data reduction; It uses various statistical modelling and machine learning techniques to analyze past data and predict the future outcomes
Prescriptive analytics – New form of analytics which uses a combination of business rules, machine learning and computational modelling to recommend the best course of action for any pre-specified outcome.
Neural networks – Building blocks of Data Analysis
Neural network is a system of hardware and software mimicked after the central nervous system of humans, to estimate functions that depend on huge amount of unknown inputs. Neural networks are specified by three things – architecture, activity rule and learning rule.
According to Kaz Sato, Staff Developer Advocate at Google Cloud Platform “A neural network is a function that learns the expected output for a given input from training datasets”. A neural network is an interconnected group of nodes. Each processing node has its own small sphere of knowledge, including what it has seen and any rules it was originally programmed with or developed for itself.
Neural networks – Building blocks of Data Analysis
In short neural networks are adaptive and modify themselves as they learn from subsequent inputs. For example, below is a representation of a neural network that performs image recognition for ‘humans’. The network has been trained with a lot of sample human and non-human images. The resulting network works as a function that takes an image as input and outputs label human or non-human.
Model for Neural network Image recognition for human and non-human
Building predictive capabilities using Machine Learning and Artificial Intelligence
Let’s implement what we have learned about neural networks in an everyday predictive example. For example, we want to model a neural network for banking system that predicts debtor risk. For such a problem we have to build a recurrent neural network which can model patterns over time. RNN will require huge memory and a large quantity of input data. The neural system will take data sets of previous debtors. Input variables can be age, income, current debt etc and provide the risk factor for the debtor. Each time we ask our neural network for an answer, we also save a set of our intermediate calculations and use them the next time as part of our input. That way, our model will adjust its predictions based on the input that it has seen recently.
Model a neural network for banking system that predicts debtor risk
Uses cases for Machine Learning based predictive analytics
As Machine Learning and Artificial Intelligence landscape evolves predictive analytics is finding its way into more business use cases. Coupled with Business intelligence (BI) tools such as Domo and Tableau, business executives can make sense of big data.
Some prospective use cases for ML-based predictive analytics are:
E-commerce – Using ML businesses can predict customer churn and fraudulent transaction. Also predicting which product customer will click on.
Marketing – There are many examples of ML in B2B marketing. Common use case is identifying and acquiring prospects with attributes similar to existing customers. They can also prioritize known prospects, leads, and accounts based on their likelihood to take action.
Customer service – Satisfaction Prediction made by Zendesk uses a machine learning algorithm to process results of historical satisfaction surveys, learning from signals such as the total time to resolve a ticket, response delay, and the specific wording of tickets cross-referenced with customer satisfaction ratings.
Medical Diagnosis – Medical professionals can use a program modelled using ML to predict the likeliness of a particular illness. The model will use a database of patient records and will make predictions based on symptoms exhibited by the patient.
Organizations and technology companies are employing machine learning based predictive analytics to gain an edge over the rest of the market. Machine learning advancements such as neural networks and deep learning algorithms can discover hidden patterns in unstructured data sets and uncover new information. But building a comprehensive data analysis and predictive analytics strategy requires big data and progressive IT systems.