A new look at an old research study In 1986, a group of urologists in London published a research paper in The British Medical Journal that compared the effectiveness of two different methods to remove kidney stones. Treatment A was open surgery (invasive), and treatment B was percutaneous nephrolithotomy (less invasive). When they looked at the results from 700 patients, treatment B had a higher success rate. However, when they only looked at the subgroup of patients different kidney stone sizes, treatment A had a better success rate.
The objective of this project is to minimize wastage of meal kits in retail stores. Currently, this is being done by tracking each individual item from the source until the point of sale. This is a cumbersome process and is labor intensive. In order to realize the objective using machine learning the first step in the process is to have an accurate forecast of the demand. This project focuses on generating accurate forecast for each individual item (46 unique items) for each store (47 unique stores).
Computers are ubiquitously used in a number of jobs and even at home as an aid to facilitate various tasks. Operation devices are used to transfer information to machine and adjust or change the state of machine [1]. Most interaction with a computer involve using either mouse or a keyboard. People most often maintain static, unnatural posture for long hours. Using the mouse means repetitive movements which may cause physiological problems in the arm, wrist and shoulder [2].
Sentiment analysis is about detecting emotions, opinions of people about certain topics by analyzing their texts from tweets, fb comments or status , youtube comments so on and so forth. Retail industries and companies in general use sentiment analysis to get an overview of their clients’ opinions on their products which enables them to make improvements and certain modifications to their products so that it meets their clients’ standards. There are a lot of social media sites like Google Plus, Facebook, and Twitter that allow expressing opinions, views, and emotions about certain topics and events.
Introduction AirBnB is a marketplace for short term rentals that allows you to list part or all of your living space for others to rent. You can rent everything from a room in an apartment to your entire house on AirBnB. Because most of the listings are on a short-term basis, AirBnB has grown to become a popular alternative to hotels. The company itself has grown from it’s founding in 2008 to a 30 billion dollar valuation in 2016 and is currently worth more than any hotel chain in the world.
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.