Fokus: Datenabruf und Verarbeitung über öffentliche API-Schnittstellen 1. Zusammenfassung Diese Webanwendung demonstriert ein klassisches Muster der modernen Webentwicklung: Eine clientseitige Applikation (Single Page Application) ruft dynamisch Daten von externen Diensten ab, verarbeitet diese im Browser und stellt sie dem Nutzer interaktiv dar. Der Kernprozess umfasst drei Schritte: Geocoding (Ort zu Koordinaten), Datenabfrage (Tankstellen-API) und clientseitiges Rendering (Sortierung/Filterung).

Ambientação da disciplina e instalação do R. Instalação do RStudio, configuração inicial e validação do ambiente.

As CTO and head of Blackwell's eCommerce Team, I'd like to welcome you aboard. I'm excited to get started on this project, but I'd first like to give you a bit of background to get you up to speed. Blackwell has been a successful electronics retailer for over three decades, with over numerous stores in various locations. A little over a year ago we launched our eCommerce website. We are starting to build up customer transaction data from the site and we want to leverage this data to inform our decisions about site-related activities, like online marketing, enhancements to the site and so on, in order to continue to maximize the amount of revenue we generate from eCommerce sales. To that end, I would like you to explore the customer transaction data we have collected from recent online and in-store sales and see if you can infer any insights about customer purchasing behavior. Specifically, I am interested in the following: Do customers in different regions spend more per transaction? Which regions spend the most/least? Is there a relationship between number of items purchased and amount spent? To investigate this, I’d like you to use data mining methods to explore the data, look for patterns in the data and draw conclusions. I have attached a data file of customer transactions; it includes some information about the customer who made the transaction, as well as the amount of the transaction, and how many items were purchased. Now that you have investigated the different aspects of customer purchases, I need you to dive deeper in to specific customer demographics so we can better understand to whom to market and why. Our VP of Sales, Martin Goodrich, thinks that customers who shop in the store are older than customers who shop online and that older people spend more money on electronics than younger people. He is considering some marketing activities and potentially some design changes to the website to attract older buyers. Before we even consider any additional activities related to the website, we want to gain insight into any factors that can better understand the age of our customers and if it correlates with how much they spend. To that end, I would like you to explore the customer transaction data we have collected from recent online and in-store sales and see if you can infer any insights about customer purchasing behavior. Specifically, I am interested in the following: Are there differences in the age of customers between regions? If so, can we predict the age of a customer in a region based on other demographic data? We need to investigate Martin’s hypothesis: Is there any correlation between age of a customer and if the transaction was made online or in the store? Do any other factors predict if a customer will buy online or in our stores? To investigate this, I’d like you to use machine learning to build a predictive model that can help us in our search. I have attached the same data file of customer transactions. As you know, it includes some information about the customer who made the transaction, as well as the amount of the transaction, and how many items were purchased.