This project examines the extensive dataset of New York City Airbnb listings. Exploratory data analysis is conducted to
identify patterns and trends in host demographics, property availability, and key metrics. Additionally, prediction
algorithms are utilized to estimate rental prices based on factors such as location, property type, and amenities.
The objective is to offer valuable insights for hosts, guests, and stakeholders in the tourism industry, aiding in
pricing strategies and investment decisions within the dynamic NYC Airbnb market.
This project revolves around the analysis of digital marketing data from a consumer goods company, with a
specific focus on the grooming segment of the company's operations, encompassing two separate sub-projects.
The first involves quantifying unidentified potential grooming consumers across all of the company's databases
in five countries to refine targeting strategies. The second sub-project analyzes Amazon Marketing Cloud data to
extract insights on consumer behavior and preferences on Amazon's website, addressing predefined business questions. Through these
endeavors, valuable insights are derived to optimize marketing efforts and stimulate growth in the grooming segment.
This project aims to assess the effectiveness of Machine Learning classification algorithms—such as Decision Tree, K-Nearest Neighbors,
Support Vector Classifier—in determining device locations based on WiFi signal strengths indoors. It utilizes
datasets from Shopping Eldorado and Jaume I University. Indoors, GPS signals frequently falter due to obstructions like buildings,
which hinder reception, as well as materials such as concrete and metal that diminish signals, and multipath interference that distorts
signals. Hence, alternative localization methods are imperative for precise indoor positioning.