India

<div>Please go through all the details and guidelines of this project very carefully and then send your proposal accordingly. <br><br>This is a research-oriented assessment.</div><div>The project requires you to apply appropriate big data analytics methodologies and techniques</div><div>on a specific topic/problem related to large dataset processing, analytics and visualisation. You</div><div>need to analyse requirements, formulate solution, and implement your solution in the form of</div><div>software development (preferred programming language is R). Finally you need to present all</div><div><br></div><div>Module Study Guide template – May 2019 7</div><div>your work and results in a written technical report (jointly by group members).</div><div>1. Project topic</div><div>There is an initial set of proposed topics as listed below. However, it is allowed (and</div><div>encouraged) to propose alternative small projects, or variants, which will have to be</div><div>discussed with the lecturers.</div><div> Music Recommendation System</div><div>Music recommendation systems are becoming a hot topic these days due to increase in</div><div>number of online listeners to systems like Spotify. Recommending users with relevant</div><div>songs and predicting which songs will be liked by a particular user is always a very good</div><div>feature for any music application. You are to developing a music recommendation system</div><div>based on the Million Song Dataset.</div><div>Dataset: http://labrosa.ee.columbia.edu/millionsong/pages/getting-dataset</div><div> Predict short term movements in stock prices</div><div>The basic assumption is that the stock price largely depends on both inside and outside</div><div>factors, where inside factor include company performance (earnings and profits),</div><div>company news (introducing new products, securing a new large contract, etc.), and</div><div>outside factor such as industry performance, investor sentiment (bull market or bear</div><div>market, news sentiments), economic environment (interest rates, economic outlook and</div><div>inflation, etc.).</div><div>Dataset: https://www.kaggle.com/c/battlefin-s-big-data-combine-forecasting-challenge</div><div> Twitter to predict the next best restaurant</div><div>Yelp has a data set that include restaurant rankings and reviews. One idea for this project</div><div>is to use tweets to predict restaurant star ratings. This would enable you combine Yelp</div><div>data with twitter data.</div><div>Dataset: https://www.yelp.com/academic_dataset</div><div> Community detection in large-scale social network</div><div>Social network is often modelled as graph. Community detection aims to identify highly</div><div>connected groups of individuals or objects inside the graph. You may explore graph</div><div>mining techniques to address the issue.</div><div>Dataset: http://snap.stanford.edu/data/index.html#communities</div><div>Please note the above topics are only suggestions which provide some general guidelines</div><div>on what could be the problems to be tackled. It is expected that you will take these</div><div>guidelines, and you should feel free to propose a specific topic based on your own research</div><div>and interests.</div><div>2. Project tasks</div><div>The expected group work of this project will include:</div><div> Performing research on the subject topic, identifying project goal and analysing</div><div>requirements;</div><div> Formulating technical solutions step by step, e.g. collecting data (public datasets),</div><div>exploring and preparing data, choosing appropriate big data models / algorithms and</div><div>applying them to your project, practising data visualisation techniques where appropriate;</div><div> Implementing the proposed solution as a software system using preferred programming</div><div>language R;</div><div> Conducting experiments with collected datasets to obtain results from your implemented</div><div><br></div><div>Module Study Guide template – May 2019 8</div><div><br></div><div>models/algorithms;</div><div> Analysis of results and evaluation of performance;</div><div> Making conclusions.</div><div><br></div><div>3. Project report</div><div>The structure of the group report is recommended to have the following sections, but you</div><div>may like to customise it based on your own project:</div><div> Cover page: where you should state the project topic, the name and student ID of each</div><div>member of your group.</div><div> Abstract: where you summarise the contents, outlining why and how you go about the</div><div>investigation, plus a brief summary of results and findings.</div><div> Introduction: where you introduce the topic of the investigation and provide necessary</div><div>background, motivating why it is interesting, and referencing all appropriate literature.</div><div> Model and implementation: where you go into more technical detail of the techniques</div><div>and procedures you have used, justifying appropriateness of</div><div>models/algorithms/techniques used, explaining software implementation. Referencing all</div><div>appropriate literature.</div><div> Experiments: where you describe experiments conducted, presenting experimental</div><div>results properly (e.g. practice of data visualisation where appropriate)</div><div> Analysis: where you analyse the experimental results, delving into deep technical</div><div>explanations of the results, suggesting possible improvement, etc.</div><div> Conclusions: where you summarise the investigation, stating the conclusions that the</div><div>results and analysis showed, along with suggestions of potential future work (if</div><div>appropriate).</div><div> Reference: where all the literature you have cited in your report can be listed.</div><div> Appendix: where the software source code you developed can be included. It is</div><div>important that you put comments in code properly.</div><div><br></div><div><br></div>

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Big Data Analytics Project of 3000 to 4000 Words by 18th May

Big Data Analytics Project of 3000 to 4000 Words by 18th May

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