Machine learning use cases: making the world a better place 🦾
Tags: machinelearning, ai, webdev, beginners
Published: 2023-10-13 08:00:00

Which sector do you want to transform with your machine learning skills?

Let's explore the transformative power of machine learning (ML) and its applications across various industries. We've compiled insights from recent research and case studies to better understand how ML revolutionizes finance, healthcare, natural hazards, and agriculture sector.


## **1. Algorithmic Trading**


The global algorithmic trading market size is expected to reach USD 36.75 billion by 2032.

**Case Study:**

Renaissance Technologies and Two Sigma are two hedge funds that have successfully leveraged machine learning for algorithmic trading. Renaissance Technologies, in particular, is known for its Medallion Fund, which has reportedly used various forms of AI, including machine learning, to make trading decisions. The fund has yielded annual returns of over 35% after fees since 1988, demonstrating the potential profitability of machine learning in algorithmic trading.

**Technical insights:**

Algorithmic trading is a process that leverages intricate algorithms to automate trading strategies. It takes into account numerous factors such as price, timing, and volume. To successfully execute these strategies, developers need a solid grasp of both financial markets and programming languages.

Access to historical and real-time market data is crucial, as it forms the basis for the machine learning models that drive trading decisions. Backtesting, the practice of testing trading strategies against historical data, is an essential step in validating the effectiveness of these models.
In addition, deploying effective risk management strategies is crucial to prevent potential losses, which might involve setting stop losses or diversifying investments across diverse assets.

Learn more: [How Renaissance beat the markets with Machine Learning](https://towardsdatascience.com/how-renaissance-beat-the-markets-with-machine-learning-606b17577797)


## **2. Fraud Detection Predictive Models**


Fraud detection and prevention market is projected to reach $252.7 billion by 2032.

**Case Study:**
 
SPD Group worked on a credit card fraud detection project for an E-commerce and financial service company. The company offered products and services that could be paid using mobile money or a bank card (e.g., Visa and MasterCard). The project aimed to make their platform a safer place for online transactions for their customers. The SPD Group used Machine Learning to implement a modern fraud prevention method for their platform.

**Technical insights:**

The SPD Group utilized Classification trees, a type of Machine Learning model, for credit card fraud detection due to the limited size of the available dataset. While Neural Networks are effective with larger datasets for identifying patterns and anomalies, Classification trees proved more suitable for this project. The choice of model based on dataset characteristics is crucial, as it ensures optimal performance and accuracy in fraud detection.

Learn more: [Credit card fraud detection case study](https://spd.tech/machine-learning/credit-card-fraud-detection-case-study/)


## **3. Disease Prediction**

 
The global market size of machine learning in healthcare is projected to reach $45.2 billion by 2026.

**Case Study:**  

The study focuses on predicting Chronic Kidney Disease (CKD) stages using machine learning techniques. It employs Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) models to predict CKD stages, aiming to facilitate early detection and timely intervention, thereby reducing patient health complications.

**Technical insights:** 

The models use Analysis of Variance and Recursive Feature Elimination for feature selection, evaluated through tenfold cross-validation. According to the study, Recursive Feature Elimination with Cross Validation (RF) was found to outperform Support Vector Machines (SVM) and Decision Trees (DT). Prediction models were created for both binary and multi-classification scenarios, offering a comprehensive approach to predicting stages of Chronic Kidney Disease (CKD).

Learn more: [Chronic kidney disease prediction using machine learning techniques](https://journalofbigdata.springeropen.com/articles/10.1186/s40537-022-00657-5)


## **4. Natural hazard mitigation**
 


**Case Study:** 

Google's Crisis Response team is using machine learning models for Flood Forecasting to alert people in areas impacted by floods before disaster strikes. Google developed this project in collaboration with researchers from various universities, and is part of Google's efforts to address the climate crisis.

**Technical insights:**

The ML-based flood forecasting model uses large amounts of streamflow data, which includes water levels or flow rates. The Caravan project, an open-source repository for global streamflow data, was developed to address the challenge of data collection and accessibility. This repository provides open-source Python scripts that leverage essential weather and geographical data.

Learn more: [Directing ML toward natural hazard mitigation](https://blog.research.google/2023/04/directing-ml-toward-natural-hazard.html)


## **5. Agriculture and Smart Irrigation**


Global Smart Agriculture market size is expected to reach USD 20.02 billion by 2025.

**Case Study:** 

Arable Mark 2 is a sophisticated weather station and crop monitor that leverages machine learning to enhance its predictive analytics capabilities. It collects over 40 different types of plant and climate data, providing actionable insights for various growing conditions. The ML models are trained on a vast dataset from Arable's network and then applied to real-time data to provide accurate and reliable measurements.

**Technical insights:**

The ML models use classification and regression algorithms to analyze data. For instance, the device uses a patented [acoustic disdrometer](https://journals.ametsoc.org/view/journals/atot/18/10/1520-0426_2001_018_1640_ltrfua_2_0_co_2.xml) to capture rainfall sounds. The audio data is analyzed using ML to identify rainfall and calculate precipitation amounts. The models are updated regularly as more data is collected, improving their accuracy over time. This iterative process of training and applying ML models and using specific algorithms provides valuable insights for developers.

Learn more: [Arable measurement accuracy whitepaper](https://learn.arable.com/hubfs/Whitepapers/Arable%20Measurement%20Accuracy%20Whitepaper%2021_01.pdf?hsCtaTracking=f3d29d22-e5e9-4272-bee9-15a112966108%7C45dda6db-c53e-4afd-b8f6-2d41e2519456)

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