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, also known as artificial neural networks, are a type of machine learning algorithm that can be used to make predictions based on complex data sets.

These algorithms are being increasingly used across a wide range of industries to improve decision-making, enhance operational efficiency, and drive business outcomes.

Here are some industry use cases of predictive neural networks:

1. Finance: Predictive neural networks can be used in the finance industry to analyze complex financial data and make accurate predictions about future market trends, investment opportunities, and risk assessments.
2. Healthcare: In the healthcare industry, predictive neural networks can be used to analyze medical data and predict patient outcomes, identify potential health risks, and develop personalized treatment plans.
3. Retail: Retail businesses can use predictive neural networks to analyze customer data and predict future buying behavior, enabling them to optimize marketing campaigns, manage inventory, and improve customer satisfaction.
4. Manufacturing: Predictive neural networks can be used in the manufacturing industry to analyze data from sensors and other devices, predicting potential equipment failures, identifying quality issues, and optimizing production schedules.
5. Energy: In the energy industry, predictive neural networks can be used to analyze data from sensors, weather forecasts, and other sources, predicting energy demand, optimizing production, and reducing costs.
6. Transportation: Predictive neural networks can be used in the transportation industry to analyze data from vehicles, traffic patterns, and weather forecasts, predicting optimal routes, optimizing fuel consumption, and reducing emissions.

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

Here are some common applications of predictive neural networks:

1. Sales forecasting: Predictive neural networks can be used to analyze sales data and make accurate predictions about future sales trends, enabling businesses to optimize inventory, production, and pricing strategies.
2. Risk assessment: Predictive neural networks can be used to analyze data and predict potential risks, such as credit default, fraud, or security threats.
3. Marketing optimization: Predictive neural networks can be used to analyze customer data and predict future buying behavior, enabling businesses to optimize marketing campaigns, improve customer engagement, and increase sales.
4. Medical diagnosis: Predictive neural networks can be used to analyze medical data and assist with the diagnosis of medical conditions, identify potential health risks and develop personalized treatment plans.
5. Predictive maintenance: Predictive neural networks can be used to analyze data from sensors and other devices, predicting potential equipment failures, identifying quality issues, and optimizing maintenance schedules.
6. Natural language processing: Predictive neural networks can be used to analyze text data and enable applications such as speech recognition, sentiment analysis, and language translation.

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:

Predictive neural networks are a powerful tool for making accurate predictions based on complex data sets. Across industries, these algorithms are being used to improve decision-making, optimize operations, and drive business outcomes.

As the use of predictive neural networks continues to expand, businesses that leverage this technology stand to gain a competitive advantage.

References:

Business Analytics Notes PDF

ISEN 613_Team3_Final Project Report

Making Data Science Accessible – Neural Networks