Reference no: EM131499182
Big Data Small Project using R Language
(Screen shots / Explanations-Script and Result, then explain
The work will combine research on a specific problem/technique related to Big Data Processing, implementation of a computing solution and presentation of the results.
There is an initial set of proposed topics. However, it is allowed (and encouraged) to propose alternative small projects, or variants, which will have to be discussed with the lecturers.
The expected work will include performing research on the subject topic, selecting and implementing a computing solution based on technologies covered in the lectures such as Map/Reduce, Mongo DB and data analytic technique, and obtain the desired results from the processed data. While the topic provides some general guidelines on what the coursework will consist of. It is expected thatyou will take these guidelines, and suggest a specific proposal of what are they aiming to achieve in the project.
Music Recommendation System
Music recommendation systems are becoming a hot topic these days due to increase in number of online listeners to systems like Spotify. Recommending users with relevant songs and predicting which songs will be liked by a particular user is always a very good feature for any music application. You are to developing a music recommendation system based on the Million Song Dataset.
Dataset: https://labrosa.ee.columbia.edu/millionsong/pages/getting-dataset
Predict short term movements in stock prices
The basic assumption is that the stock price largely depends on both inside and outside factors, where inside factor include company performance (earnings and profits), company news (introducing new products, securing a new large contract, etc), and outside factor such as industry performance, investor sentiment (bull market or bear market, news sentiments), economic environment (interest rates, economic outlook and inflation, etc).
Twitter to predict the next best restaurant
Yelp has a data set that include restaurant rankings and reviews. One idea for this project is to use tweets to predict restaurant star ratings. This would enable you combine Yelp data with twitter data.
Have you provided a context for the project? Have you provided a description of the data?
have you loaded the data? Have you explored/processed the data? have you provided script(s) for pre-analysis?
Have you identified the objective of the analysis and the technique to be used?
How are presenting the result of the analysis?
Compulsory Requirement
Topic must be approved first but if from the above suggestion then that is not required. you need to make sure you that you MUST have the analytics process (exploration, cleaning, modelling). You also need to show that you can apply noSql and Hadoop (including a related technology).
Reflective Critique
You should keep a reflective diary of your progress during the assignment. It should cover your activities and how you collected other material on the methods used. This should be submitted as an appendix to the report developed for part two and is subject to the same submission criteria.
You also need to evaluate the solution that you are proposing and how would you improve it
Article -
https://www.dropbox.com/s/tbc54ohdexyrtrj/retutorversal_com.zip?dl=0