Top 15 Machine Learning Case Studies: Transforming Industries with Innovative Solutions

Last updated by Rishabh Dev Choudhary on Aug 30, 2024 at 05:16 PM | Reading time: 17 minutes

Machine learning is playing a key role in transforming industries and their operations. From healthcare to finance, ML has emerged at the centre of all technological developments and how these sectors evolve. If you are curious to understand how ML is applied in the real world, look no further than machine learning case studies. These case studies highlight the potential of this emerging technology.

In recent years, ML-based applications have quickly transformed companies to carry out a majority of their operations. It is changing how work is carried out and how people interact with the world around them.

In this article, we present the top 15 machine learning case studies impacting business operations and the way companies carry out their functions. In addition, we also explain the importance of these ML case studies and how they can benefit you. Learning these ML use case examples will help you build your machine learning skills.

Also read: What is Machine Learning? A Comprehensive Guide

Top 15 Machine Learning Case Studies for Enhanced Learning

Machine learning is not just a technology of the future, it is reshaping the way industries operate. It is helping turn data into actionable insights and solutions. It allows businesses to analyze large amounts of data, discover statistical patterns, and make predictions.

Now let’s look at top 15 machine learning case studies:

1. Loan Application Classification

Financial institutions are the trove of data as they receive vast amounts of information about people looking to borrow money. These organizations also depend significantly on data for decision-making.

Since data is so important for these organizations, machine learning is important. Manually processing the data can be time-consuming and prone to errors, ML can help quickly analyze this data and provide actionable insights.

Today, an increasing number of financial institutions are using machine learning models to streamline and improve the decision-making process for the numerous loan applications they receive every day.

These models help analyze the applicant’s financial data, credit history, and other relevant information to predict the likelihood of default.

The objective of this machine learning use case is to improve the accuracy of loan approval decisions by predicting the risks associated with the potential borrowers. Using machine learning and automating the decision-making process will help reduce the time required to process and approve loan applications.

In this ML use case, you can develop features from the raw data and use information such as debt-to-income ratio, credit utilization rate, past financial behavior, and more to create a machine learning model. Further, supervised learning algorithms such as logistic regression, decision trees, and others can be used to train the model on historical data.

2. Machine Learning Case Study on Dell

Dell is world’s one of the leading technology companies that designs, develops, and sells computers, servers, data storage devices, and more. Data is an integral part of Dell’s operations and helps the company understand its target customers and determine ways to make data-driven decisions to improve the operations and results.

The company relies on data to help the marketing team make data-focused solutions to improve the response rate and highlight why some words & phrases may be more effective than others.

Dell uses machine learning and artificial intelligence (AI) to create marketing content. ML models help the company revamp its email marketing strategy and leverage data analytics to attract the audience’s attention.

Today, the company uses machine learning to improve its marketing copies of its promotional and lifecycle emails, social media ads, display banners, and other forms of content.

This machine learning case study shows how ML helps use data-driven instruments to revolutionize the marketing function. Dell now uses large language models to develop effective language for its messaging and marketing copies.

This ML use case is a perfect example of how machine learning can help develop & maintain customer relations and boost the company’s performance.

Also read: Machine Learning Engineer Salary in the USA

3. Harley Davidson Machine Learning Case Study

Harley Davidson is one of the leading motorcycle manufacturers in the world and uses AI-powered robots for many of its marketing functions. The company uses AI such as Albert to direct traffic, create new stories, work in hotels, and more.

In this machine learning case study, it can be seen that it uses ML and AI in various marketing channels like email and social media. Here, the company uses AI to automatically prepare customized creative copies and forecasts that can help in converting customers.

Harley Davidson examined the customer data to analyze activities of the past customers who made successful purchases and invested more time than usual on its website and pages. The company then used this knowledge to divide its customer base into different groups and adjust the scale of its test campaigns accordingly.

4. Tesla Machine Learning Case Study

Tesla designs, manufactures, and sells electric cars, battery storage systems, and solar products. The company strongly emphasizes the use of the latest technologies and innovations to promote sustainability and reduce dependence on fossil fuels.

Tesla uses machine learning to identify ways to improve the performance and features of electric cars. For instance, the autopilot system is one of the most advanced features today in vehicles that drive the cars automatically without the intervention of any human.
The company developed this system that collects and analyzes data from different sensors, cameras, and machine learning algorithms to steer the car and keep the passengers safe. This system also controls features like cruise control, automatic emergency braking, and more.

Further, the company uses deep neural networks to process vast amounts of real-world driving-related data such as road conditions, traffic, passengers in the car, etc. Based on this information, the system predicts driving behavior and potential hazards.

Tesla’s development of the electric cars and the features it provide is a perfect machine learning use case example.

5. Starbucks Customer Loyalty Program

One of the key aims of Starbucks is to increase customer engagement and loyalty to sustain in this hyper-competitive market. The company wants to do this by delivering personalized offers and recommendations to the customers.

By undertaking this practice, Starbucks wants to identify new patterns in customer behavior and determine ways to make them loyal to the company. With such information, the company can improve its marketing efforts and increase the customer satisfaction rate by making each customer unique and valued.

In this regard, the company wants to build a cluster of customers based on similar behaviors. Here, Starbucks uses machine learning to develop offers that most likely will appeal to the customers. Building an ML-based recommendation engine is the best way to generate personalized offers.

Here, the company can use database management systems like SQL databases for structured data storage, Python for data processing, and ML libraries like Pandas to develop and train the recommendation model.

6. Amazon Real-Time Fraud Detection System

Another machine learning case study is from Amazon, the world’s leading e-commerce company. It relies on the ML-based fraud detection system to identify and prevent fraudulent transactions.

The objective of using machine learning is to identify potential frauds in real-time and prevent them from happening in the first place without impacting other users’ experience.

Amazon has built this fraud detection system by using raw data to understand user activities and other such information like unusual transaction size or patterns to determine if there are chances of any fraud.

In this regard, it uses methods like random forest or gradient boosting machines (GBMs). These can handle any unbalanced datasets, make out patterns, and provide actionable insights.

7. How Netflix Gives Personalized Recommendations?

Netflix is one of the leading names when it comes to giving personalized recommendations to users. Both Netflix and YouTube pioneered this system.

Netflix used machine learning to analyze a user’s viewing habits and based on this information it suggests shows and movies that users most likely will enjoy. It realized that this feature could help improve overall customer satisfaction and engagement while increasing subscription renewals.

To build this system, the company used viewing data such as time stamps, duration of views, and metadata of content like the genre, actors, release dates, and more. It then used methods such as collaborative filtering, matrix factorization, and even deep learning techniques to predict user preferences.

8. Google’s Search Algorithm

Google uses complex machine learning algorithms to analyze, interpret, and rank webpages and websites based on their relevance to user’s queries. At the core, this process involves crawling, indexing, and ranking web pages to provide users with more relevant results.

In this machine learning use case example, the search engine used the most accurate and relevant search results based on the user’s query and search intent with speed and efficiency.

It used different machine learning models for web crawling and indexing. It further used ML to develop complex ranking algorithms for query processing.

Also read: Demystifying the Role of a Machine Learning Engineer: Skills, Responsibilities, and Career Path

9. Crop and Soil Health Monitoring at John Deere

Machine learning case studies: Application in agriculture

Machine learning is not just limited to the tech domain. It is slowly trickling into every aspect of our lives. This machine learning use case is a perfect example of this.

In agriculture, challenges such as optimizing crop yields, cost reduction, and efficient management of resources are common but integral. John Deere, a leading agriculture equipment manufacturer used ML to provide the farmers with advanced tools that helped them monitor the crop health and soil conditions to make informed decisions.

The company integrated machine learning with sensors & IoT devices and developed a system that checks crop health, predicts soil conditions, and even suggests optimal planting and fertilizing strategies. This system heavily relies on data to analyze patterns and detect anomalies that could affect the crop.

10. Darktrace: Anomaly Detection

As the digital world is evolving, cyber threats are increasingly becoming sophisticated. Today, cybercriminals can easily avoid any of the traditional detection methods. In this regard, Darktrace developed a solution with the help of machine learning that can adaptively learn from the ongoing activities in a network and proactively identify and respond to threats.

The company used ML with its Enterprise Immune System technology. Just like a human immune system, this technology can detect any abnormalities and detect potential threats in the network behavior. It is a self-learning system that can adapt itself to the unique patterns of a network and use this knowledge to identify deviations leading to a potential threat.

This system is deployed across a network where it passively learns the normal operating behaviors and continually updates its understanding of the network’s behavior. If there is any change in the network’s behavior the system will be able to identify it and take necessary actions.

11. Spotify’s Recommendation Engine

Spotify is another leading name when it comes to personalized recommendations. The company aims to continually enhance user engagement and satisfaction. To do this, Spotify aims to provide personalized music recommendations.

The company used machine learning algorithms to develop its recommendation engine. In addition, Spotify also uses machine learning models for natural language processing that analyzes text from user-created playlists, search queries, metadata of the songs, and various deep learning models to process the audio files.

This system gives recommendations to the users in real time, while incorporating feedback instantaneously to refine the suggestions.
As a result of this recommendation engine, Spotify has been able to increase user engagement on the platform.

12. Hotel Recommendation System

Hospitality is another industry that uses a huge amount of data. The hotel recommendation system is generally used by travel aggregators to recommend hotels to users based on data such as their booking history, search queries, availability, travel dates, etc.

This system helps boost booking conversions for travel platforms and also enhances user satisfaction.

The system uses machine learning algorithms like collaborative filtering to recommend hotels based on similar user preferences or content-based filtering to suggest hotels similar to users’ previous bookings.

Some advanced models use deep learning to handle complex data sets and provide more accurate and personalized recommendations to the users.

13. How IBM Uses to Make Weather Forecasts and Predictions

IBM is one of the leading technology companies and offers a wide variety of services to its clients. Its The Weather Company uses advanced machine learning and AI to improve the accuracy of the weather forecasts.

Through this system, the company aims to help provide accurate weather predictions to aid informed decision-making from the authorities. This system has applications in a wide range of industries, ranging from agriculture to disaster response.

The company uses ML models like neural networks and ensemble methods to analyze complex weather data. Further, these models are regularly refined and tested against actual weather outcomes to check its accuracy and reliability.

Further, the use of high-capacity databases such as IBM Db2 or various cloud storage solutions helps handle large datasets.

14. Mental Illness Diagnosis Machine Learning Case Study

Machine learning case studies: Use of AI and ML for diagnosing mental illness

In recent years, the use of ML in developing predictive models for diagnosing and managing mental illnesses is increasing. One of the main reasons for using machine learning in this case is its ability to handle large and complex data sets and identify patterns and correlations, which otherwise would be difficult to be analyzed manually.

The training of ML algorithms can be pushed to a whole new level by using diverse data sources such as clinical assessments, self-reported symptoms, physiological measures like brain imaging, etc. These algorithms can then be used to develop predictive models to identify high-risk individuals.

One machine learning use case example, in this regard, is that ML can be used to develop suicide risk assessment tools. These tools can analyze risk factors like demographic information, medical history, social media activity, and more to identify the individuals at risk of committing suicide.

Moreover, these tools can also be used to guide early intervention and support efforts for such individuals.
Another such machine learning use case is developing chatbots that can analyze a user’s response and recommend necessary actions that can be taken immediately.

15. Duolingo’s Adaptive Learning for Language Education

Duolingo used an adaptive learning model which is powered by machine learning algorithms to facilitate its learners. The company faced the challenge of tailoring the language learning process to fit diverse global users with different learning styles. The goal of developing this ML system was to increase the engagement and efficacy of their language learning app.

It developed an ML-based system that adjusts the difficulty of the different exercises and the sequence of lessons. This is based on the user’s performance, preferences, and engagement levels.

From all the above machine learning case studies, it is clear that the role and importance of ML in the modern world will only continue to grow.

Also read: 12 Machine Learning and Artificial Intelligence Jobs

Why Should You Look Into Machine Learning Case Study?

Now that you learned about machine learning case studies, it is time that you understand the reasons why these case studies are important and how they can benefit you.

Better Job Opportunities

Working on such real-world machine learning use cases and adding them to your resume will showcase case your hands-on expertise, thereby boosting your machine learning resume. Today, employers do not look for people who can highlight their skills and expertise only on paper. Instead, they want individuals with real-world experience and expertise to take ownership and lead the company to success.

Therefore, looking into the machine learning case studies will help you find better job opportunities and boost your career.

Help Idenitfy In-Demand Skills

By studying the machine learning use case examples you can identify the tools and techniques currently in demand within an industry. You can then add those skills to your resume and clear the application tracking systems. These skills can further help you in machine learning interview preparation.

The machine learning case studies can help you tailor your preparation strategies, learn the in-demand skills, and align your experience with what leading tech and FAANG companies are looking for.

Learn About Industry-Specific Challenges

The machine learning case study examples will help you identify and learn about industry-specific challenges. By learning and analyzing the ML case studies you can understand how different companies customize the ML solutions to meet their requirements and the challenges they face.

This knowledge can help you land specialized roles that are much more lucrative than the general roles and also increase your earning potential.

Also read: Machine Learning Algorithms Demystified: Exploring the Essentials

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FAQs: Machine Learning Case Studies

Q1. What is the Significance of Machine Learning Case Studies in Understanding ML Applications?

Machine learning case studies provide real-world examples of how ML technologies are applied to solve specific business problems. They help bridge the gap between theoretical knowledge and practical implementation, showcasing the impact and potential of ML in various industries.

Q2. How can Machine Learning Case Studies Help in Career Advancement?

Analyzing machine learning case studies can enhance your understanding of practical ML applications, improve your problem-solving skills, and make your resume stand out. They demonstrate hands-on experience and expertise, which are highly valued by employers, especially in tech and data-driven industries.

Q3. What are the Key Components of a Good Machine Learning Case Study?

A comprehensive machine learning case study should include a clear problem statement, the objectives of the ML solution, a description of the data used, the ML models and algorithms implemented, the results achieved, and the lessons learned from the project.

Q4. How do Companies Select the Right Machine Learning Models for Their Projects?

Companies choose machine learning models based on factors such as the type of data available, the complexity of the problem, the desired outcomes, and the resources available. They may use supervised, unsupervised, or reinforcement learning algorithms depending on the specific use case.

Q5. What are Some Common Challenges Faced When Implementing Machine Learning Solutions?

Common challenges include data quality and availability, selecting the appropriate algorithms, ensuring model interpretability and transparency, integrating ML solutions into existing systems, and maintaining the models over time to ensure continued accuracy and relevance.

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Rishabh Dev Choudhary

Machine Learning

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Top 15 Machine Learning Case Studies: Transforming Industries with Innovative Solutions

Contents

Rishabh Dev Choudhary

Attend our Free Webinar on How to Nail Your Next Technical Interview

How to Nail your next Technical Interview

Worried About Failing Tech Interviews?

Top 20 Beginner Programming Interview Questions

Top 5 Factors Affecting Product Marketing Manager Salary

8 Software Engineering Resume Action Verbs

Key Generative AI Interview Questions and Preparation Tips

8 Biggest Challenges of Machine Learning

Impact of Generative AI on Jobs in 2024 and Beyond

Top Python Scripting Interview Questions and Answers You Should Practice

Complex SQL Interview Questions for Interview Preparation

Zoox Software Engineer Interview Questions to Crack Your Tech Interview

Rubrik Interview Questions for Software Engineers

Top Advanced SQL Interview Questions and Answers

Twilio Interview Questions

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