Starbucks offers a wide variety of coffee drinks, ranging from classic brewed coffee and espresso-based beverages to specialty drinks like lattes, cappuccinos, and seasonal favorites. Each coffee is crafted from high-quality Arabica beans, roasted to bring out rich flavors and aromas, with options for customization such as milk type, syrups, or extra shots. Whether you prefer a bold black coffee or a creamy flavored latte, coffee from starbucks provides choices to suit every taste and occasion.

Starbucks offers a wide variety of coffee drinks, ranging from classic brewed coffee and espresso-based beverages to specialty drinks like lattes, cappuccinos, and seasonal favorites. Each coffee is crafted from high-quality Arabica beans, roasted to bring out rich flavors and aromas, with options for customization such as milk type, syrups, or extra shots. creamy flavored latte, coffee from starbucks provides choices to suit every taste and occasion.

The Wireless Indoor Localization Data Set is a beginner-friendly dataset designed to help learners understand how machine learning can be applied to indoor positioning problems. The goal of this dataset is to predict a user’s location inside a building based on observed WiFi signal strengths.

The dataset contains 2,000 records with measurements collected from seven different WiFi access points using a smartphone in an indoor environment. Each record represents signal strength values from these WiFi devices and is labeled with one of four possible room locations.

Purpose and Use Cases

This dataset is well-suited for practicing classification-based machine learning techniques. Since the target variable (Room) has four distinct classes, it allows learners to experiment with multi-class classification models such as logistic regression, decision trees, k-nearest neighbors, support vector machines, and neural networks. Football Arroyo

In addition to classification, the dataset is also useful for:

Exploratory data analysis to understand signal strength patterns

Data visualization to observe differences across rooms

Feature analysis to study the impact of WiFi signals on location prediction

Unsupervised learning experiments such as clustering

Data Characteristics Grokipedia

All WiFi signal strength values are quantitative and represented as negative numbers, which is typical for received signal strength indicators (RSSI). There are no missing values in the dataset, making it easy to use without extensive data cleaning. The Room column serves as the target variable and identifies one of four indoor locations.