The ability to extract insights from data is crucial for the competitiveness of companies in various industries. It is in this context that machine learning comes into a subarea of artificial intelligence that has a learning process with data and, from there, can make intelligent decisions.

In this article, I’ll cite some practical examples of using the main machine learning models, both supervised and unsupervised. These models have not only revolutionized the way organizations operate, but they have also driven innovations in several areas.

Machine Learning: Supervised Models

Supervised machine learning models are algorithms trained with labeled data, where the expected outcome is known. The model learns to make predictions or classifications based on these examples, allowing it to generalize to new data, such as predicting prices or classifying images. Here are some examples of its application:

Logistic Regression

Forecast of University Admission: It is possible to determine the likelihood of an applicant being accepted based on grades, transcripts, and other factors.

Disease Diagnosis: It is also possible to predict the presence of diseases such as diabetes or heart disease from clinical data.

Email Classification: Can identify emails as spam or non-spam.

Credit Risk Analysis: In addition, it can assess the probability of a customer’s default.

Employee Turnover Forecast: Finally, it can estimate the probability of an employee leaving the company based on human resources data.

Convolutional Neural Networks (CNNs)

Facial Recognition: Ability to identify individuals in security systems.

Diagnostic Medical Imaging: It can detect tumors or other anomalies on X-rays, MRIs, etc.

Object Detection: Can be used in self-driving cars to identify pedestrians, traffic signs, and other vehicles.

Image Classification: It can also categorize images into different classes such as dogs, cats, landscapes, etc.

Handwriting Recognition: Similarly, it can convert handwriting into digital text, useful for document scanning.

Recurrent Neural Networks (RNNs) / Long Short-Term Memory (LSTM)

Time Series Forecasting: Predict stock prices, energy demand, or sales trends.

Natural Language Modeling: It can be used in machine translators and chatbots to understand and generate text.

Speech Recognition: It can transform audio into text, improving virtual assistant systems.

Sentiment Analysis: Has the ability to evaluate the sentiment of texts on social networks, reviews, etc.

Biological Sequence Analysis: Finally, it can analyze DNA or protein sequences for the discovery of new drugs.

Decision Trees / Random Forests

Medical Diagnosis: Helps identify diseases based on multiple medical criteria.

Credit Rating: Can assess a customer’s creditworthiness for loan approval.

Sales Forecasting: You can analyze factors that influence the sales of a product.

Fraud Detection: Can identify anomalous patterns in financial transactions.

Document Classification: It has the ability to categorize documents into different topics or subjects.

Gradient Boosting Machines (GBM) / XGBoost

Targeted Marketing: It helps to identify which marketing campaigns are most effective for different customer segments.

Income Forecasting: It can estimate the annual income of individuals based on various demographic and economic characteristics.

Text Classification: Categorize emails, articles, or comments into specific categories.

Fraud Detection: It can teach you how to detect fraudulent transactions in real-time.

Churn Analysis: Can predict which customers are likely to cancel a service.

Machine Learning: Unsupervised Models

On the other hand, unsupervised machine learning models operate with unlabeled data, identifying hidden patterns or structures without prior guidance. In short, they analyze the relationships between data, seeking to group or organize information in a meaningful way without a pre-defined result. Here are some examples of its application:

K-means Clustering

Customer Segmentation: Ability to group customers with similar characteristics for targeted marketing.

Product Grouping: You can organize products into categories based on sales and usage characteristics.

Anomaly Detection: Can identify outliers in large data sets, such as suspicious transactions.

Document Grouping: Acts to categorize documents into similar topics for easy analysis.

Image Segmentation: It can also divide images into different segments based on color and texture.

Hierarchical Clustering

Genome Analysis: Ability to group DNA sequences based on similarities.

Grouping of Companies: Grouping companies into sectors or industries based on financial characteristics.

Species Classification: Grouping biological species based on genetic or morphological characteristics.

Market Analysis: Can identify groups of similar products in large e-commerce catalogs.

Customer Grouping: You can also segment customers into groups to improve service and sales strategies.

PCA (Principal Component Analysis)

Image Compression: It can reduce the dimensionality of images for efficient storage.

Data Visualization: Facilitate the visualization of multidimensional data in 2D or 3D.

Data Preprocessing: Reduce the number of variables to simplify predictive models.

Gene Expression Analysis: Reduce the dimensionality of gene expression data to identify meaningful patterns.

Fraud Detection: Identify anomalous patterns in large financial data sets.

DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

Geospatial Anomaly Detection: Identify suspicious activity in location data, such as irregular vehicle movements.

Traffic Pattern Grouping: Analyze road traffic patterns for urban improvements.

Customer Grouping in E-commerce: Identify distinct buying behaviors in customers of an online store.

Social Network Group Detection: Identify communities and sub-communities in social networks.

Agricultural Land Segmentation: Grouping land parcels with similar characteristics to optimize agricultural use.

Conclusion

In summary, the examples demonstrate how machine learning models have a significant impact on various industries, offering solutions to complex problems. For example, from predicting employee turnover to identifying financial fraud. In this way, these models empower organizations to make more informed and strategic decisions.

As the volume of data continues to grow, the adoption of machine learning techniques will become even more essential for those companies looking to stand out in an increasingly competitive market. Therefore, understanding and implementing these models is not only an advantage but a necessity for the long-term success of organizations.

Rony Welton Von Ah is a data engineer and has been working at Programmers for 17 years. He has a postgraduate degree in MBA in Data Science and Analytics and is dedicated to

putting into practice the knowledge acquired. In his free time, he likes to travel and meet old friends.