Machine Learning Applications

Machine learning applications have the power to transform companies and elevate them to new heights. InetSoft has brought its flagship BI software to the realm of machine learning by providing an integrated visual analytics interface to help end users understand the results of a machine learning model. This makes the running of ML algorithms as easy as clicking a button. Now, non data scientists can harness the power of automated data modeling to find solutions to the business challenges they face.

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Machine Learning Churn Analytics Example

Automate Insights From Complex Data

2 Types of Machine Learning Algorithms

Supervised Learning

Supervised learning is just that, learning that requires supervision. Algorithms in this category utilize your established variable prediction model and train a ML predictor variable against it to make future calculations.

Unsupervised Learning

Unsupervised learning, unlike supervised learning, asks machine learning to find patterns on its own. In this method, no human provides examples to be used as a base to learn from. For business applications, two types of unsupervised learning are often seen: clustering and churn. Cluster analysis is especially useful for revealing potential segmentation methods which might not have come to mind. This is done by grouping website visitors or customers into clusters. The dashboard image above is an example of churn machine learning created by InetSoft.

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Customer Churn Rate Prediction

Customer churn rate prediction is an unsupervised machine learning method that quantifies the amount of individuals or items moving away from a collective group over a particular period, and therefore is an aid for determining a company's customer base. Business users have the ability to first label a set of users into the churn classes, and then let the machine learning algorithm study the data set to figure out how to do the same classification automatically.

The bottom line is if you have a set of data with output produced by a human, machine learning can automate the job for you. This can help companies identify at-risk users or clients while there is still time to take action on them.

At the top is a sample dashboard image created by InetSoft, utilizing a churn prediction model, where we demonstrate how you can use ML models to analyze the results of a churn prediction model. We applied this model to existing customers and re-trained it on different customer segments to further improve predictability.

Two More Examples of Using Machine Learning in the Real World

One fascinating application of machine learning in the real world is in the field of healthcare, particularly in medical imaging analysis. Machine learning algorithms have shown remarkable capabilities in assisting radiologists and clinicians in diagnosing diseases from medical images such as X-rays, MRIs, and CT scans. For example, in the detection of breast cancer, deep learning models trained on large datasets of mammograms have demonstrated impressive accuracy in identifying suspicious lesions and tumors. These algorithms can analyze subtle patterns and anomalies in medical images that may be difficult for the human eye to detect, potentially leading to earlier and more accurate diagnoses. By automating parts of the image analysis process, machine learning systems can help reduce the workload on healthcare professionals, improve diagnostic accuracy, and ultimately enhance patient outcomes.

Another intriguing application of machine learning is in the field of natural language processing (NLP), particularly in the development of chatbots and virtual assistants. NLP algorithms enable computers to understand, interpret, and generate human language, facilitating interactions between humans and machines in a more natural and conversational manner. Chatbots powered by machine learning can be found in various industries, including customer service, finance, and e-commerce, where they assist users with inquiries, provide recommendations, and perform tasks such as booking appointments or making reservations. These intelligent virtual assistants leverage machine learning techniques such as natural language understanding (NLU) and natural language generation (NLG) to analyze user input, extract relevant information, and generate appropriate responses, creating seamless and efficient user experiences.

More Resources About Machine Learning

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