Introduction How do musicians choose the chords they use in their songs? Do guitarists, pianists, and singers gravitate towards different kinds of harmony?
We can uncover trends in the kinds of chord progressions used by popular artists by analyzing the harmonic data provided in the McGill Billboard Dataset. This dataset includes professionally tagged chords for several hundred pop/rock songs representative of singles that made the Billboard Hot 100 list between 1958 and 1991.
In this project, we will work with exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. The objective of this project is to be able to answer the following questions:
Are employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been there longer?
In this project, we’re going to build a spam filter for SMS messages using the multinomial Naive Bayes algorithm. Our goal is to write a program that classifies new messages with an accuracy greater than 80% — so we expect that more than 80% of the new messages will be classified correctly as spam or ham (non-spam).
To train the algorithm, we’ll use a dataset of 5,572 SMS messages that are already classified by humans.
In this project, we’ll aim to find the two best markets to advertise our product in — we’re working for an e-learning company that offers courses on programming. Most of our courses are on web and mobile development, but we also cover many other domains, like data science, game development, etc.
Understanding the Data To avoid spending money on organizing a survey, we’ll first try to make use of existing data to determine whether we can reach any reliable result.