Top 17 Machine Learning Case Studies to Look Into Right Now (Updated for 2024)

Machine learning is one of the most valuable skills that a data science professional can have in 2024.

According to this report from Gartner, as the adoption of machine learning continues to grow across industries, it is evolving from mere predictive algorithms to a more data-centric discipline.

Machine learning case studies are in-depth analyses of real-world business problems in which machine learning techniques are applied to solve the problem or provide insights.

If you’re looking for an updated list of machine learning case studies to explore, you’re in the right place. Read on for our hand-picked case studies and tips on solving them.

Better Job Prospects

Employers are often concerned that their recruits lack business acumen or data-handling skills. Working on real-world case studies and adding them to your resume will showcase your hands-on expertise, thereby bolstering your CV.

We’ve seen numerous examples where adding relevant personal and academic projects to interviewees’ resumes has helped them get their foot in the door.

Helps You Identify In-Demand Skills

Case studies often highlight tools and techniques currently in demand within a particular industry. By studying them, you can tailor your preparation strategy to acquire these skills, aligning your expertise with what leading tech firms are looking for.

This will enhance your prospects in a very competitive job market.

Insight On Industry-Specific Challenges

Industries leverage data science and machine learning in different ways. By examining case studies across healthcare, finance, or retail, you’ll gain insight into how ML solutions are customized to meet industry-specific challenges.

For example, suppose you are planning to interview at a banking organization.

In that case, you can leverage what you learned to discuss industry-relevant ML applications and propose solutions to common banking and financial challenges. This will help you land specialized roles that are much more lucrative than general data roles.

With these benefits in mind, let’s explore the top 15 machine learning case studies that are particularly relevant in 2024.

We’ve curated examples that highlight the innovative use of AI and ML technologies and reflect common business challenges in today’s job market.

1. Starbucks Customer Loyalty Program

Starbucks aims to enhance customer engagement and loyalty by delivering personalized offers and recommendations.

The goal is to analyze customer data to uncover patterns and preferences for tailoring marketing efforts and increase customer satisfaction by making each customer feel uniquely understood.

• Objective: To increase retention, boost sales, and enhance the customer experience.
• How to build: Cluster customers based on similar behaviors, identify the types of offers most likely to appeal to each group and develop a recommendation engine to generate personalized offers. Key tools include data management systems like SQL databases for structured data storage, Python for data processing and machine learning with libraries such as pandas and scikit-learn, and a platform like TensorFlow to develop and train the recommendation models.

2. Amazon Pricing Case Study

Amazon employs a dynamic pricing model to avoid updating prices manually. It uses sophisticated algorithms to adjust prices in real time based on demand, competitor pricing, inventory levels, and customer behavior to achieve maximum profitability.

• Objective: To maximize revenue and market share by implementing optimal pricing across millions of products.
• How to build: First aggregate real-time and historical data. Train regression models and ensemble methods like random forests or boosted trees to predict optimal price points. These models learn from past pricing outcomes and continuously adjust to changing variables. Use core technologies including a big data platform like Apache Hadoop and machine learning frameworks such as TensorFlow or PyTorch.

Here is an interesting pricing problem for calculating electricity consumption.

3. Amazon’s Real-Time Fraud Detection System

Another case study from Amazon—its fraud detection system uses machine learning to identify and prevent fraudulent transactions as they occur.

• Objective: To accurately identify potentially fraudulent transactions in real time without impacting the user experience with false positives.
• How to build: Create relevant features from raw data that help identify suspicious activity, such as unusual transaction sizes or patterns that deviate from the norm. Employ ensemble methods like random forest or gradient boosting machines (GBMs) due to their robustness and ability to handle unbalanced datasets. Tools you’d typically use include Amazon Redshift, frameworks such as TensorFlow, and Apache Kafka for real-time streaming.

Here is our takehome project on a similar business problem: detecting credit card fraud.

4. Netflix’s Recommendation Engine

Netflix’s recommendation engine analyzes individual viewing habits to suggest shows and movies that users are likely to enjoy.

This personalization is critical for enhancing user satisfaction and engagement and driving continued subscription renewals.

• Objective: To maximize the relevance of recommended content, promote a diverse array of content that users might not find on their own, and increase overall viewing time and subscriber retention.
• How to build: Extract useful features from the data, such as time stamps, duration of views, and metadata of the content like genres, actors, and release dates. Netflix uses various methods, including collaborative filtering, matrix factorization, and deep learning techniques, to predict user preferences. Also, test and refine the algorithms using A/B testing and other evaluation metrics to ensure the recommendations are accurate and engaging.

For more practice, the MovieLens dataset is a classic choice for building recommendation systems.

5. Google’s Search Algorithm

Google’s search engine uses complex machine learning algorithms to analyze, interpret, and rank web pages based on their relevance to user queries.

The core of it involves crawling, indexing, and ranking web pages using various signals to deliver the most relevant results.

• Objective: To provide the most accurate and relevant search results based on the user’s query and search intent with speed and efficiency.
• How to build: Developing a basic version of a search engine like Google’s would involve several key components:
  1. Web crawling: Use crawlers to visit web pages, read the information, and follow links to other pages on the internet.
  2. Indexing: Organize the content found during crawling into an index. This index needs to be structured so that the system can find data quickly in response to user queries.
  3. Ranking algorithm: Google uses the PageRank algorithm, which evaluates the quality and quantity of links to a page to determine a rough estimate of the website’s importance.
  4. Query processing: Develop a system to interpret and process user queries, applying natural language processing techniques to understand context.
• Tools: Open-source web crawlers like Apache Nutch, scalable databases such as Apache Cassandra, and NLTK or spaCy libraries in Python for understanding user queries.

6. Telecom Customer Churn Prediction

In the telecom industry, customer churn prediction models identify customers likely to cancel their services.

This allows companies to address at-risk customers with targeted interventions.

• Objective: To identify customers likely to churn by understanding the factors that lead to customer dissatisfaction.
• How to build: Use ML algorithms such as logistic regression, decision trees, or ensemble methods like random forests or gradient boosting machines to build the model.

The Telco Customer Churn dataset on Kaggle is very popular for customer churn prediction projects.

7. Loan Application Case Study

Machine learning models are increasingly used by financial companies to streamline and improve the decision-making process for loan applications.

These models analyze applicants’ financial data, credit history, and other relevant variables to predict the likelihood of default.

• Objective: To improve the accuracy of loan approval decisions by predicting the risk associated with potential borrowers. Automating this process also reduces the time to approve a loan application.
• How to build: Develop features from raw data that are predictive of loan repayments, such as debt-to-income ratio, credit utilization rate, and past financial behavior. Use supervised learning algorithms like logistic regression, decision trees, or more sophisticated methods such as gradient boosting or neural networks to train the model on historical data.

Here is a list of more fintech projects to try.

8. LinkedIn’s AI-Powered Job Matching System

LinkedIn leverages advanced algorithms to connect job seekers with the most relevant opportunities.

This system analyzes job postings and user profiles to make accurate recommendations that align with the user’s career goals and the employer’s needs.

• Objective: To refine the accuracy of job matches, increase user engagement, and streamline the hiring process for employers.
• How to build: Clean and transform data from profiles, job listings, and user interactions using NLP methods to extract relevant features for job matching. Use collaborative filtering and neural networks on this data to predict user preferences and match jobs.

9. Twitter Contextual Ad Placement Study

Twitter’s contextual ad placement system dynamically serves ads based on real-time analysis of user interactions.

• Objective: To enhance user engagement with ads by making them more relevant and less intrusive. This relevance increases the likelihood of users interacting with the ads, which improves the efficiency of ad campaigns.
• How to build: Extract useful features from the data, such as keywords from tweets, used hashtags, sentiment of the tweets, and user engagement rates with similar content. Models like logistic regression for click prediction or deep learning models for more complex patterns are common choices for the algorithm. Finally, implement the model using a real-time processing framework to allow for dynamic ad placement as user behavior changes.

10. Uber’s Demand Forecasting

Uber’s demand forecasting model leverages machine learning to predict future ride demand in various geographic areas.

This system helps optimize the allocation of drivers while maximizing earnings.

• Objective: To balance supply and demand across Uber’s network. This includes lowering wait times, maximizing earnings for drivers, and optimizing surge pricing by predicting spikes in demand.
• How to build: Employ time series forecasting models like ARIMA or more complex models such as LSTM (long short-term memory) networks, which are capable of handling sequential data and can learn patterns over time.

11. Hotel Recommendation System

These systems analyze vast amounts of data, including previous bookings, user ratings, search queries, and user demographics, to predict hotels that a customer might prefer.

This approach enhances user satisfaction and boosts booking conversion rates for platforms.

• Objective: The system aims to increase the likelihood of bookings by providing relevant recommendations that match user preferences. In the long term, personalized experiences help build customer loyalty, as users are more likely to return to a service that understands their needs.
• How to build: Create features that can help in understanding user preferences, such as preferred locations, amenities, price range, and types of accommodations (e.g., hotels, B&Bs, resorts). Features related to temporal patterns, like booking during a particular season or for specific types of trips (business, leisure), can also influence decisions. Implement machine learning algorithms such as collaborative filtering, which can recommend hotels based on similar user preferences, or content-based filtering, which suggests hotels similar to those the user has liked before. Advanced models also integrate deep learning to handle the complexity of the data.

Here is an interesting takehome problem on recommending Airbnb homes to users.

12. IBM’s Weather Prediction

IBM’s The Weather Company harnesses advanced machine learning and artificial intelligence to enhance the accuracy of weather forecasts.

Through these tools, IBM aims to provide precise weather predictions that can inform decisions ranging from agriculture to disaster response.

• Objective: To enhance the precision of weather forecasts to better predict events such as storms, rainfall, and temperature fluctuations. Also, it aims to support decision-making in various sectors for operational decisions and advance climate research.
• How to build: Advanced machine learning models like neural networks and ensemble methods are utilized in order to analyze complex weather data. The models are regularly refined and tested against actual weather outcomes to improve their accuracy over time. High-capacity databases like IBM Db2 or cloud storage solutions are used to handle large datasets.

13. Zillow’s House Price Prediction System

Zillow’s house price prediction, well-known through its “Zestimate” feature, utilizes machine learning to estimate the market value of homes across the US.

This system analyzes data from various sources, including property characteristics, location, market conditions, and historical transaction data to generate a market value in near real time.

• Objective: To provide homeowners and buyers with a reliable estimate of property values, helping them make informed buying, selling, and refinancing decisions.
• How to build: Develop predictive features from the collected data. This involves extracting insights from the raw data, like normalizing prices by square footage or adjusting values based on local real estate market health. Employ advanced regression models and techniques like gradient boosting or neural networks to learn from complex datasets. Libraries such as XGBoost, TensorFlow, or PyTorch are commonly employed.

14. Tesla’s Autopilot System

Tesla’s Autopilot system is a highly advanced driver-assistance system that uses machine learning to enable its vehicles to steer, accelerate, and brake automatically under the driver’s supervision.

The system relies on a combination of sensors, cameras, and algorithms to interpret the vehicle’s surroundings, make real-time driving decisions, and learn from diverse driving conditions.

• Objective: To reduce the likelihood of accidents by assisting drivers with advanced safety features and optimizing driving decisions and to improve the system toward achieving full self-driving capabilities eventually.
• How to build: Key features such as object detection, lane marking recognition, and vehicle trajectory predictions are derived from the raw data to train models. Convolutional neural networks (CNNs) are employed to process and interpret the sensory input. Tesla also uses over-the-air software updates to deploy new features based on aggregated fleet learning.

15. GE Healthcare Image Analysis

GE Healthcare leverages machine learning to enhance the analysis of medical images to improve the accuracy and efficiency of diagnostics across various medical fields.

This technology allows for more precise identification and evaluation of anomalies in medical imaging, such as MRI, CT scans, and X-rays.

• Objective: To detect and classify anomalies in medical images that might be too subtle for human eyes. It also accelerates image analysis and shortens diagnosis time, which is important for providing quick patient care.
• How to build: Collect large sets of annotated medical images, which include a variety of imaging types and conditions. Label these images accurately to serve as a training set. Extract relevant features from the images, such as texture, shape, intensity, and spatial patterns of the imaged tissues or organs. Convolutional neural networks (CNNs) are particularly effective due to their ability to pick up on spatial hierarchies and patterns and should be deployed. Be sure to rigorously test the models against new, unseen images to ensure they generalize well and maintain high accuracy and reliability.

As an extension, we also have an article on healthcare data science and machine learning projects, which we highly recommend you check out.

16. Spotify’s Music Recommendation System

Spotify’s recommendation system uses machine learning to curate personalized playlists and suggest songs to users based on their listening habits.

The system enhances user engagement by discovering new music that aligns with individual tastes, leading to more time spent on the platform and increased subscription retention.

• Objective: To boost user engagement by delivering highly personalized music recommendations, encouraging users to explore new content, and promoting lesser-known artists that match user preferences.
• How to build: Start by collecting and processing user data such as listening history, song likes, skips, and playlist additions. Apply collaborative filtering techniques to identify patterns in user behavior and recommend tracks based on similar users’ preferences. Additionally, use content-based filtering by analyzing the audio features of songs, such as tempo, key, and genre, to suggest tracks with similar characteristics. Implement deep learning models like neural networks for more sophisticated pattern recognition and to handle the vast and diverse music catalog. Finally, continuously refine the model using A/B testing to ensure the recommendations remain relevant and engaging.

Here is a resource we have on other music machine-learning data science projects.

17. Predictive Maintenance for Manufacturing Equipment

In the manufacturing industry, machine downtime can lead to significant financial losses. Predictive maintenance uses machine learning to anticipate equipment failures before they occur, allowing for timely maintenance and reducing unplanned downtime.

• Objective: To increase equipment uptime by predicting failures before they happen, thereby reducing maintenance costs and improving overall efficiency in the production process.
• How to build: Begin by collecting sensor data from manufacturing equipment, such as temperature, vibration, and pressure readings. Preprocess this data to remove noise and extract meaningful features that are indicative of machine health. Use supervised learning techniques like random forests or support vector machines (SVM) to classify potential failure modes based on historical data. For more complex scenarios, consider using recurrent neural networks (RNNs) to capture temporal dependencies and trends in the sensor data over time. The model should be continuously updated with new data to improve its predictive accuracy. Finally, implement a real-time monitoring system that triggers alerts when the model detects signs of impending failure, allowing maintenance teams to act proactively.

What skills can I learn from machine learning case studies that are applicable to data science jobs?

Employers look for candidates with a mix of technical and soft skills.

Some competencies you can develop through exploring and analyzing case studies are problem-solving, critical thinking, better data interpretation, an understanding of commonly used ML algorithms, and coding skills in relevant languages.

We recommend that you work on these problems in addition to reading up on them. You can use public datasets provided by Kaggle or UCI Machine Learning Repository or Interview Query’s storehouse of takehome assignments.

To help you get started, we’ve created a comprehensive guide on how to start a data analytics project.

Are there beginner-friendly case studies in machine learning?

The examples we’ve provided in the list above are a mix of beginner-friendly and advanced ML case studies.

There are more beginner-friendly cases you can explore on Kaggle, such as the iris flower classification, Titanic survival predictions, and basic revenue forecasting for e-commerce.

We’ve also compiled a list of data science case studies categorized by difficulty level.

How do I use machine learning case studies to craft a better resume or portfolio?

You can tailor your resume to highlight ML case studies or projects you’ve worked on that match the skills and industry you’re applying to.

For each project, provide a concise title and description of what the project entailed, the tools and techniques used, and its outcomes.

Wherever possible, quantify the impact of the project, for example, the model’s accuracy. Use action verbs like “developed,” “built,” “implemented,” or “analyzed” to increase persuasion.

Lastly, rehearse how you would present your project in an interview, an often overlooked step in getting selected. On a related note, you can try a mock interview with us to test your current preparedness for a project presentation.

To wrap up, staying updated on, exploring, and implementing machine learning case studies is a clever strategy to showcase your hands-on experience and set you apart in a competitive job market.

Plan your interview strategy, considering the perspective of your desired future employer and tailoring your project selection to the skills they want to see.

Here at Interview Query, we offer multiple learning paths, interview questions, and both paid and free resources you can use to upskill for your dream role. You can access specific interview questions, participate in mock interviews, and receive expert coaching.

If you have a specific company in mind to apply to, check out our company interview guide section, where we have detailed company and role-specific preparation guides. We have guides for all the companies that are mentioned in our case study list, including Uber, Tesla, Amazon, Google, and Netflix.

We hope this discussion has been helpful. If you have any other questions, don’t hesitate to reach out to us or explore our blog.