Based on the features of Titanic passengers, the goal is to determine which types of people were more likely to survive.

Based on information about residential properties in Ames, Iowa, the goal is to predict the sales price of a given property.

Based on grayscale images of handwritten digits, the goal is to build a model to accurately classify each digit from 0 to 9.

Given high-resolution images of tumor tissues, the goal is to classify these images into categories such as benign, malignant, or uncertain.

Given high-dimensional features extracted from medical images, the goal is to classify these images into categories such as 'normal', 'abnormal', and 'critical'.

Given historical house prices and features like the number of bedrooms, square footage, and location, the goal is to predict house prices and categorize houses into different price ranges for market analysis.

Given various features of customer reviews, the goal is to predict the satisfaction score of each review, which is a numerical value ranging from 1 to 10.

Given features related to customer transactions, the goal is to segment customers into distinct groups based on their purchasing behavior and classify new customers into these predefined segments.

Given features related to residential properties, the goal is to first cluster the properties into different segments based on their characteristics and then use regression models to predict the property prices within each segment.

Given customer reviews with features extracted from text, the goal is to classify these reviews into categories like 'positive', 'negative', and 'neutral'.

Given factors such as temperature, humidity, wind speed, and historical pollution levels, the goal is to estimate the air quality index (AQI) for a specific location.