Prediction using Neural Networks

Prediction Using Neural Networks 

In the first part of this post, we discussed what neural networks prediction are, what the “artificial” component in them is, and how they are used in data science.

Today we look at how they are used in predictive analytics. We will also answer why neural networks still are not being used by many businesses. Read more about prediction using neural networks.

The two big arguments against using artificial neural networks are

1. They are resource-intensive
2. Their results are often hard to interpret.

On the other hand, neural networks may be used for solving problems the human brain is very good at, such as recognizing sounds, pictures, or text. They can be used to extract features from neural network prediction algorithms for clustering and classification, essentially making them modules of larger Machine Learning apps.

As we said in our earlier post, an artificial neural network (ANN) is a predictive model designed to work the way a human brain does. In fact, ANNs are at the very heart of deep learning. The deep neural networks model (DNN model) can group unlabeled data based on similarities existing in the inputs, or classify data when they have a labeled dataset to train on.

What’s more, DNNs are also scalable, and best suited for machine learning tasks. Using these, we can build very robust and accurate predictive models for predictive analytics.

Structure of A Neural Network Prediction

There are three layers to the structure of a neural-network algorithm:

1. The input layer: This enters past data values into the next layer.
2. The hidden layer: This is a key component of a neural network. It has complex functions that create predictors. A set of nodes in the hidden layer called neurons represents math functions that modify the input data.
3. The output layer: Here, the predictions made in the hidden layer are collected to produce the final layer – which is the model’s prediction.

How does actually Neural Networks Predict?

Each neuron takes into consideration a set of input values. Each of them gets linked to a “weight”, which is a numerical value that can be derived using either supervised or unsupervised training such as data clustering, and a value called “bias”.

The network chooses from the answer produced by a neuron based on its’ weight and bias.

Where “Classification” is concerned, all such tasks are contingent on labeled datasets.  This means that you need supervised learning.

Supervised Learning is where humans check to see if the answers the neural network gives are correct. This helps the neural network understand the relationship between labels and data.

Examples of this are face-detection, image recognition, and labeling, voice detection, and speech transcription. With classification, deep learning can associate pixels in an image and the name of a person.

“Clustering” or grouping is the recognition of similarities. One must understand that the deep learning model does not always require labels to find similarities.

When there are no labels by helpful humans to learn from, it uses machine learning to learn on its own – which means unsupervised learning. This retains the potential of producing highly accurate models. Examples of clustering can be customer churn.

Industry Use Cases of Predictive Neural Networks

Predictive neural networks, or artificial neural networks, are used in both startups and big MNCs.

Let’s illustrate some industry use cases of ANN.

Finance: In this sector, artificial neural networks (ANN) can help banks come up with solutions for all issues related to business and analyze possible profits.

Retail: Predictive neural networks can make precise sales forecasts and help companies purchase the relevant stock.

They can also minimize the possibility of selling out a few items.   

eCommerce: These machine learning algorithms can allow businesses to examine the gap between product purchases by monitoring the buying habits of customers.

A study from Amazon reports that their sales have increased by 29% after following suggestions given by ANNs to enhance their recommendation systems. 

Healthcare: Predictive analytics using neural networks allows healthcare professionals to examine patients’ medical histories and their present health status.

Insurance: In the insurance sector, the segmentation of policyholders can be done smoothly by ANN.

This allows businesses to determine and provide suitable pricing plans.

Use of Neural Networks Prediction in Predictive Analytics

As we all know, predictive analytics combines techniques like predictive modeling with machine learning to analyze past data to predict future trends.

But neural networks differ from regular predictive tools. The most-oft used model – linear regression – is actually a very simple way of going about things as compared to a neural network.

Neural networks work better at predictive analytics because of the hidden layers. Linear regression models use only input and output nodes to make predictions.

The neural network also uses the hidden layer to make predictions more accurate. That’s because it ‘learns’ the way a human does.

So why doesn’t everyone use neural network prediction? For one, they require massive amounts of computing power, so they are cost-prohibitive.

In addition, neural networks work best when trained with extremely large data sets, which your business might not have. But with IT tech getting cheaper, the first hurdle may soon disappear. Soon, technology like ANNs will mean that there will be no more “unpleasant surprises”.

Applications of Predictive Neural Networks

Applications of predictive neural networks include:

Make accurate predictions and sales forecasting: ANNs produce predicted categories or values for upcoming observations—vital information for the business.

The critical predictor variables are indicated, offering more value-added information to help decision-making.

Stock market predictions: Businesses can determine the future value of their stock and various financial assets traded on exchanges by using stock market predictions fuelled by machine learning.

Product return score prediction: The major and most commonly seen challenge in the eCommerce sector is product returns.

Predictive neural networks can be used to forecast product return scores.

Forecasting customer churn score: The major problem for different companies is custom churn.

Advanced neural networks can be used to discover users with low retention or high churn risk.

NLP: Neural network models can be used to classify text, perform language translation, recognize speech, and analyze the sentiment of a text.

A customer data platform that “thinks” like humans? That’s right. A good CDP provides a unified view of customers. But our CDP Oyster goes beyond that.

It uses neural networks to understand customer behavior much better than other CDPs. Oyster is trained to “learn and think” like the human brain. Which means highly accurate predictions about customer behavior.

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Conclusion:

Artificial neural networks act as the platform for various applications and provide all types of possibilities for predictive analytics.

No doubt, they are worth researching as they enable businesses to optimize outcomes and decision-making. Predictive analytics using neural networks have simplified evaluation and changed traditional algorithms.

References:

ISEN 613_Team3_Final Project Report