16 Real World Case Studies of Machine Learning

A decade ago, no one must have thought that the term “Machine Learning” would be hyped so much in the years to come. Right from our entertainment to our basic needs to complex data handling statistics, Machine Learning takes care of all of this. The clutches of Machine Learning aren’t just limited to the basic necessities and entertainment.

The technology plays a pivotal role in domain areas such as data retrieval, database consistency, and spam detection along with many other vast ranges of applications. We do come across various articles that are ready to teach us about the basic concepts of Machine Learning, however, learning becomes more fun when we actually see it working in practicality.

Keeping this in mind, PythonGeeks brings to you, an article that will talk about the real-life case studies of Machine Learning stating its advancement in various fields. We will talk about the merits of Machine Learning in the field of technology as well as in Life Science and Biology. So, without further delay, let us look at these case studies and get to know a bit more about Machine Learning.

Machine Learning Case Studies in Technology

1. Machine Learning Case Study on Dell

We all are aware of the multinational leader in technology, Dell. This tech giant empowers people and communities from across the globe by providing superior software and hardware services at very affordable prices. As a matter of fact, data plays a pivotal role in the programming of the hard drive of Dell, the marketing team of Dell requires a data-driven solution that supercharges response rates and exhibits why certain words and phrases outpace others in terms of efficiency and reliability.

Dell made a partnership with Persado, one of the names amongst the world’s leading technology in AI and ML fabricating marketing creative, in order to harness the power of words in their respective email channel and garner data-driven analytics for each of their key audiences for a better user experience.

As an evident outcome of this partnership, Dell experienced a 50% average increase in CTR and a 46% average increase in responses from its customer engagement. Apart from this, it also witnessed a huge 22% average increase in page visits and a 77% average increase in add-to-carts orders.

Overwhelmed by this success rate and learnings with email, Dell adamantly wanted to elevate their entire marketing platform with Persado for more profit and audience engagement. Dell now makes use of machine learning algorithms to enhance the marketing copy of their promotional and lifecycle emails. Apart from these, their management even deploys Machine Learning models for Facebook ads, display banners, direct mail, and even radio content for a farther reach for the target audience.

2. Machine Learning Case Study on Sky

Sky UK is a British telecommunication service that transforms customer experiences with the help of machine learning and artificial intelligence algorithms with the help of Adobe Sensei.

Due to the immense profit that the company gained due to the deployment of the Machine Learning model, the Head of Digital Decisioning and Analytics, Sky UK once stated that they have 22.5 million very diverse customers. Even attempting to divide people by their favorite television genre can result in pretty broad segments for their services.

This will result in the following outcomes:

• Creating hyper-focused segments to engage customers.
• Usage of machine learning to deliver actionable intelligence.
• Improvement in the relationships with customers.
• Applying AI learnings across channels to understand what matters to customers.

The company was competent in efficiently analyzing large volumes of customer information with the help of machine learning frameworks. With the deployment of Machine Learning models, the services were able to recommend their target audience with products and services that resonated the most with each of them.

McLaughlin once stated that people think of machine learning as a tool for delivering experiences that are strictly defined and very robotic in their approach, but it’s actually the other way round. With Adobe Sensei, the management of the Sky was drawing a line that connects customer intelligence and personalized experiences that are valuable and appropriate for their customers.

3. Machine Learning Case Study on Trendyol

Trendyol is amongst the leading e-commerce companies based in Turkey. It once faced threats from its global competitors like Adidas and ASOS, particularly for its sportswear sales and audience engagement.

In order to assist the company in gaining customer loyalty and to enhance its emailing system, Trendyol partnered with the vendor Liveclicker, which specializes in real-time personalization for a better user experience for its customers.

Trendyol made use of machine learning and artificial intelligence algorithms to create several highly personalized marketing campaigns based on the interests of a particular target audience. It was not only aimed at providing a personalized touch to the campaign, but it also helped to distinguish which messages would be most relevant or draw the attention of which set of customers. It also came up with an offer for a football jersey imposing the recipient’s name on the back of the jersey to ramp up the personalization level and grab the consumer’s attention.

By innovating such one-to-one personalization, not only were the retailer’s open rates, click-through rates, conversions were high, it also significantly made their sales reach all-time highs. It resulted in the generation of a 30% increase in click-through rates for Trendyol, a 62% growth in response rates, and a striking 130% increase in conversion rates for the tech giant.

4. Machine Learning Case Study On Harley Davidson

The world that we live in today is where it becomes difficult to break through traditional marketing. For an emerging business like – Harley Davidson NYC, Albert (an artificial intelligence-powered robot) has a lot of appeal for the growth and popularity of the company. Powered by effective and reliable machine learning and artificial intelligence algorithms, robots are writing news stories, opening new dimensions, working in hotels, managing traffic, and even running McDonald’s customers’ outlets.

We can use Albert in various marketing channels including social media and email campaigns. The software accurately predicts and differentiates among the consumers who are most likely to convert and adjust personal creative copies on their own for the benefits of the campaign.

Harley Davidson is the only brand to date that uses Albert to its advantage. The company analyzed customer data to determine a strong pattern in the behavior of previous customers whose actions were positive in terms of purchasing and spending more than the average amount of time on browsing through the website giving way to the use of Albert. With this analyzed data, Albert bifurcates segments of customers and scales up the test campaigns according to the interests and engagement of customers.

Once the company efficiently deployed Albert, Harley Davidson witnessed an increase in its sales by 40% with the use of Albert. The brand also witnessed a 2,930% increase in leads, with 50% of those from high converting ‘lookalikes’ identified by artificial intelligence and machine learning using Albert.

5. Machine Learning Case Study on Yelp

As far as our technical knowledge is concerned, we are not able to recognize Yelp as a tech company. However, it is effectively taking advantage of machine learning to improve its users’ experience to a great extent.

Yelp’s machine learning algorithms assist the company’s non-robotic staff in tasks like collecting, categorizing, and labeling images more efficiently and precisely. Since images play a pivotal role to Yelp as user reviews themselves, the tech giant is always trying to improve how it handles image processing to analyze customer feedback in a constructive way. Through this assistance, the company is serving millions of its users now with accurate and satisfactory services.

For an entire generation nowadays, capturing photos of their food has become second nature. Owing to this, Yelp has such a huge database of photos for image processing. Its software makes use of techniques for analysis of the image to identify and classify the extracted features on the basis of color, texture, and shape. It implies that it can recognize the presence of, say, pizzas, or whether a restaurant has outdoor seating by merely analyzing the images that we provide as input data.

As a constructive outcome, the company is now capable of predicting attributes like ‘good for kids’ and ‘classy ambiance’ with a striking more than 80% accuracy.

6. Machine Learning Case Study on Tesla

Tesla is now a big name in the electric automobile industry and the chances that it will continue to be the trending topic for years to come are really high. It is popular and extensively known for its advanced and futuristic cars and their advanced models. The company states that their cars have their own AI hardware for their advancement. Tesla is even making use of AI for fabricating self-driving cars.

With the current progress rate of technology, cars are not yet completely autonomous and need human intervention to some extent. The company is working extensively on the thinking algorithm for cars to help them become fully autonomous. It is currently working in an advert partnership with NVIDIA on an unsupervised ML algorithm for its development.

This step by Tesla would be a game-changer in the field of automobiles and Machine Learning models for many reasons. The cars feed the data directly to Tesla’s cloud storage to avoid data leakage. The car sends the driver’s seating position, traffic of the area, and other valuable information on the cloud to precisely predict the next move of the car. The car is equipped with various internal and external sensors that detect the above-mentioned data for processing.

Machine Learning Case Studies in Life Science and Biology

7. Development of Microbiome Therapeutics

We have studied and identified a vast number of microorganisms, so-called microbiota like bacteria, fungi, viruses, and other single-celled organisms in our body till today with the advancement in technology. All the genes of the microbiota are collectively known as the microbiome. These genes are present in an enormous number of trillions, for example, the bacteria present in the human body have more than 100 times more unique genes than humans could ever have.

These microbiotas that are present in the human body have a massive influence on human health and cause imbalances leading to many disorders like Parkinson’s disease or inflammatory bowel disease. There is also the presumption that such imbalances may even cause several autoimmune diseases if precariously left in the human body. So, microbiome research is a very trendy research area and Machine Learning models can help in handling them effectively.

In order to influence the microbiota and develop microbiome therapeutics to reverse the diseases caused by them, we need to understand the microbiota’s genes and their influence on our body. With all the gene sequencing possibilities that are present today, terabytes of data are available however we cannot use it as it is not yet probed.

8. Predicting Heart Failure in Mobile Health

Heart failure typically leads to emergency or hospital admission and may even be fatal in some situations. And with the increase in the aging population, the percentage of heart failure in the population is expected to increase.

People that suffer from heart failure usually have some pre-existing illnesses that go undiagnosed and lead to fatal ailments. So, it is not uncommon that we make use of telemedicine systems to monitor and consult a patient, and collect valuable data like mobile health data like blood pressure, body weight, or heart rate and transmit it effectively.

Most prediction and prevention systems are now fabricated based on fixed rules, like when specific measurements of the vital readings of the human body are beyond a predefined threshold, the patient is alerted even before the diagnosis of any kind of ailment. It is self-explanatory that such a predictive system may lead to a high number of false alerts, due to fluctuating reading of the vitals due to reasons that are not serious.

Because of the programming that we do on the algorithms, alerts lead mostly to hospital admission. Due to this reason, too many false alerts lead to increased health costs and deteriorate the patient’s confidence in the prediction defying the cause of the algorithms. Eventually, the concerned patient will stop following the recommendation for medical help even if the algorithm alters it for fatal ailments.

So, on the basis of baseline data of the patient like age, gender, smoker or not, a pacemaker or not along with measurements of vital elements of the body like sodium, potassium, or hemoglobin concentrations in the blood, apart from the monitored characteristics like heart rate, body weight, (systolic and diastolic) blood pressure, or questionnaire proves to be helpful in answering about the well-being, or physical activities, a classifier on the basis of Naïve Bayes has been finally developed to reduce the chances of false positives.

9. Mental Health Prediction, Diagnosis, and Treatment

According to an estimated number that at least 10% of the global population has a mental disorder, it is now high time that we need to take preventive measures in this field. Economic losses that are evident due to mental illness sum up to nearly $10 trillion.

Mental disorders include a large variety of ailments ranging from anxiety, depression, substance use disorder, and others. Some other prime examples include opioids, bipolar disorder, schizophrenia, or eating disorders that cause high risk to the human resources.

As a result of which, the detection of mental disorders and intervention as early as possible is critical in order to reduce the loss of precious resources. There are two main approaches to deploy Machine Learning models in detecting mental disorders: apps for consumers that detect mental diseases and tools for psychiatrists to support diagnostics of their patients.

The apps for consumers are typically conversational chatbots enhanced with machine learning algorithms to help the consumers in reducing their anxiety or panic attacks. The app analyzes the behavioral traits of the person like the spoken language of the consumer and recommends help to the customers accordingly. As the recommendations must be strictly on the basis of scientific evidence, the interaction and response of proposals and the individual language pattern of the chatbot, as well as, the consumer must be predicted as precisely as possible.

10. Research Publication and Database Scanning for Bio-Markers for Stroke

As a matter of fact, Stroke is one of the major reasons for disability and death amongst the elder generations. The lifetime risk analysis of an adult person is about 25% of having once a stroke history. However, stroke is a very heterogeneous disorder in nature. Therefore, having individualized pre-stroke and post-stroke care is critical for the success of a cure.

In order to determine this individualized care, the person’s phenotype indicates that the observable characteristics of a person should be chosen wisely. Furthermore, we usually achieve this by biomarkers. A so-called biomarker represents a measurable data point such that we can stratify the patients. Examples of such biomarkers are disease severity scores, lifestyle characteristics, or genomic properties.

There are many recognized biomarkers already published or in databases. Apart from this, there are hundreds of scientific publications that talk daily about the detection of biomarkers for all the different diseases.

11. 3D Bioprinting

Bioprinting is yet another trending topic in the domain of biotechnology. It works on the basis of a digital blueprint where the printer uses cells and natural or synthetic biomaterials — also called bio-inks — to print layer-by-layer living tissues like skin, organs, blood vessels, or bones that have exact replication of the real tissues.

As an alternative for depending on organ donations, we can produce these tissues in printers more ethically and cost-effectively. Apart from this, we can even perform drug tests on the synthetic build tissue than with animal or human testing. The whole technology is still emerging and is in early maturity due to its high complexity. One of the most crucial parts to cope with this complexity of printing is data science.

12. Supply Chain Optimization

As we might have observed, the production of drugs needs time, especially for today’s high-tech cures based on specific substances and production methods only. Apart from this, we have to break down the whole process into many different steps, and several of them are outsourced to specialist delivery agents.

We observe this currently with the COVID-19 vaccine production as well. The vaccine inventors deliver the blueprint for the vaccine. Then the production happens in plants of companies specialized in sterile production. The production unit then delivers the vaccine in tanks to companies. They do the filling in small doses under clinical conditions, and at last, another company makes the supply for the given blueprint.

The complete planning, right from having the right input substances available at the right time, then having the adequate production capacity, and at last, the exact amount of drugs stored for serving the demand is a highly complicated system. As a result of which, this must be managed for hundreds and thousands of therapies, each with its specific conditions.

13. AES On Google Cloud AutoML Vision

As we have known, the AES Corporation is a power generation and distribution company. They generate and sell power that the consumers use for utilities and industrial work. They depend on Google Cloud on their road to make renewable energy more efficient. AES makes use of Google AutoML Vision to review images of wind turbine blades and analyze their maintenance needs beforehand.

Outcomes of this case study:

• It reduces image review time by approximately 50%
• It helps in reducing the prices of renewable energy
• This results in more time to invest in identifying wind turbine damage and mending it

14. Bayes AG on AWS SageMaker

Bayer AG is an emerging name in multinational pharmaceutical and life sciences companies and it is based in Germany. One of their key highlights is in the production of insecticides, fungicides, and herbicides for agricultural purposes.

In order to assist farmers monitor their crops, they fabricate their Digital Yellow Trap: an Internet of Things (IoT) device that alerts farmers of pests using image recognition on the farming land.

Outcomes of this case study:

• It helps in reducing Bayer lab’s architecture costs by 94%
• We can scale it to accommodate for fluctuating demand
• It is able to handle tens of thousands of requests per second
• It helps in Community-based, early warning

15. American Cancer Society on Google Cloud ML Engine

The American Cancer Society is a nonprofit organization for eradicating cancer. They operate in more than 250 regional offices all over America.

They make use of the Google Cloud ML Engine to identify novel patterns in digital pathology images. Their aim is to improve breast cancer detection accuracy and reduce the overall diagnosis timeline as well as ensure effective costing.

Outcomes of this use case:

• It helps in enhancing the speed and accuracy of image analysis by removing human limitations
• It even aids in improving patients’ quality of life and life expectancy
• This helps to protect tissue samples by backing up image data to the cloud

16. Road Safety Commission of Western Australia

The Road Safety Commission of Western Australia operates under the Western Australia Police Force. It takes the responsibility for tracking road accidents and making the roads safer by taking adequate precautions.

In an attempt to achieve its safety strategy “Towards Zero 2008-2020” which aims at reducing road fatalities by 40%, the road safety commission is depending on machine learning, artificial intelligence, and advanced analytics for precise and reliable results.

Outcomes of this case study:

• It helps in achieving the goal of data engineering and visualization time reduced by 80%
• It has achieved an estimated 25% reduction in vehicle crashes
• This is based on straightforward and efficient data sharing
• It works on flexibility of data with various coding languages

Conclusion

With this, we have seen the various case studies that are done till now in the field of Machine Learning. PythonGeeks specially curated this list of case studies to help readers to understand the deployment of Machine Learning models in the real world. The article can benefit you in various ways since it delivers accurate studies of the various uses of Machine Learning. You can study these cases to get to know Machine Learning a bit better and even try to find improvements in the existing solution.