Summarising and visualising the data set

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Reference no: EM132237925

Assignment - The aims of this assignment are to put into practice the concepts covered in lectures, apply these to a real dataset, and to demonstrate your ability to use Python to carry out machine learning tasks.

The main steps you need to carry out and report on are:-

  • Identifying and providing a clear statement of the problem you wish to explore.
  • Summarising and visualising the data set.
  • Preparing your dataset for analysis (data cleansing, choosing appropriate features to model) etc.
  • Choosing appropriate models for your chosen problem and building, running and evaluating these.
  • Presenting and interpreting the results of your models and relating these to your initial problem.

The data you are going to be working on comes from the Airbnb data for Bristol. The data contains information about all the Airbnb properties in Bristol as of November 2018 (2,375 and 28 attributes), and problem you choose to work on is entirely up to you.

You can work on either a classification or regression tasks but should aim to apply a range (around 2-4) of techniques including the basic ones (which might be useful as baseline comparisons) and also the more sophisticated ones covered in this class, but the emphasis should be on using the techniques appropriately and interpreting the results.

Your assignment needs to be submitted in Jupyter notebook format (and submitted as a .ipynb file), and should include the Python code used and developed interleaved with explanations of the steps you are taking and interpretations and analysis of the outcomes. I would recommend that you still use Spyder (or similar) for developing the code, but then use Jupyter notebook to create the final presentation. Below are some illustrations of reports which use this approach:

  • Using Python to see how the Times writes about men and women
  • An open science approach to a recent false-positive between solar activity and the Indian monsoon
  • Kaggle Competition | Titanic Machine Learning from Disaster
  • An example machine learning notebook
  • An exploratory statistical analysis of the 2014 World Cup Final

Your report should be approximately 10 pages in length (tricky to say, given the format, but based on the generated pdf or printed html), but the emphasis should be on the appropriate application of techniques and critical interpretation of results.

Attachment:- Assignment Files.rar

Reference no: EM132237925

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Reviews

len2237925

2/19/2019 11:21:30 PM

Think carefully about the problem you want to work on and the main question you are trying to answer. And take you time to make sure you understand the data. It is also not necessary to use every attribute - you may find yourself working with many or just a few. The emphasis in this assignment is also much on the process: if you find that the techniques you have chosen don't work very well or fail to produce particularly interesting results, then this is not a problem provided you followed the appropriate steps to understand and prepare the data and select appropriate models and can provide some insights or explanations into why your model failed.

len2237925

2/19/2019 11:21:24 PM

You can work on either a classification or regression tasks but should aim to apply a range (around 2-4) of techniques including the basic ones (which might be useful as baseline comparisons) and also the more sophisticated ones covered in this class, but the emphasis should be on using the techniques appropriately and interpreting the results. Your report should be approximately 10 pages in length (tricky to say, given the format, but based on the generated pdf or printed html), but the emphasis should be on the appropriate application of techniques and critical interpretation of results.

len2237925

2/19/2019 11:21:18 PM

This assignment is out of 25 and will follow the following marking scheme: Identifying and providing a clear statement of the problem you wish to explore (3) Summarising and visualising your data set (5) Preparing your dataset for analysis (data cleansing, choice of features) etc. (5) Choice of models, application, evaluation and validation (8) Interpretation and explanation of the results of your models and implications of these for your initial problem (4).

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