This document discusses celebrities and celebrity endorsements. It defines celebrities as people who enjoy public recognition, and lists different types of celebrities including actors, models, athletes, entertainers, pop stars, businessmen, and politicians. It then explains how celebrities appear in public through their professions and events. Celebrities are often used to endorse and promote products through testimonials, endorsements, and as spokespeople. The effectiveness of celebrity endorsements depends on the fit between the celebrity and the brand. While endorsements can increase awareness and brand image, there are also risks if the celebrity damages their reputation.
This document discusses celebrities and celebrity endorsements. It defines celebrities as people who enjoy public recognition, and lists different types of celebrities including actors, models, athletes, entertainers, pop stars, businessmen, and politicians. It then explains how celebrities appear in public through their professions and events. Celebrities are often used to endorse and promote products through testimonials, endorsements, and as spokespeople. The effectiveness of celebrity endorsements depends on the fit between the celebrity and the brand. While endorsements can increase awareness and brand image, there are also risks if the celebrity damages their reputation.
This document discusses scheduling recipes and cooking multiple dishes simultaneously. It provides an example recipe for curry rice in JSON format including ingredients and steps. It also mentions scheduling jobs efficiently with the shortest time constraints and resources, and searching for but not finding scheduling libraries.
The document discusses an IoT refrigerator project that collects sensor data on refrigerator door openings using an Arduino and gyro sensor. The data includes open count, open seconds, and is sent every minute to an IoT platform and visualized on a graph. A web application was then developed to aggregate the raw data by day, month, or year in response to API requests. The client side uses D3.js to visualize the aggregated data.
An IoT refrigerator collects sensor data from an Arduino-attached gyroscope to track opening counts, durations, and send the data every minute to a visualization platform. A web application was then developed to aggregate the raw sensor data by day, month or year through a server-side API, and display interactive summaries on the client-side using D3.js.
This document contains code snippets in Python for analyzing stock market data, normalizing features, performing grid search for an SVM classifier, and using the classifier to make predictions and tweet the results. The snippets load CSV data, sort values by date, normalize features, tune an SVM using grid search, make predictions, and tweet the prediction using the Twitter API.
This document discusses scheduling recipes and cooking multiple dishes simultaneously. It provides an example recipe for curry rice in JSON format including ingredients and steps. It also mentions scheduling jobs efficiently with the shortest time constraints and resources, and searching for but not finding scheduling libraries.
The document discusses an IoT refrigerator project that collects sensor data on refrigerator door openings using an Arduino and gyro sensor. The data includes open count, open seconds, and is sent every minute to an IoT platform and visualized on a graph. A web application was then developed to aggregate the raw data by day, month, or year in response to API requests. The client side uses D3.js to visualize the aggregated data.
An IoT refrigerator collects sensor data from an Arduino-attached gyroscope to track opening counts, durations, and send the data every minute to a visualization platform. A web application was then developed to aggregate the raw sensor data by day, month or year through a server-side API, and display interactive summaries on the client-side using D3.js.
This document contains code snippets in Python for analyzing stock market data, normalizing features, performing grid search for an SVM classifier, and using the classifier to make predictions and tweet the results. The snippets load CSV data, sort values by date, normalize features, tune an SVM using grid search, make predictions, and tweet the prediction using the Twitter API.