python代写 – CAPSTONE PROJECT代写 – dataset代写 – ALY6140
python代写

python代写 – CAPSTONE PROJECT代写 – dataset代写 – ALY6140

CAPSTONE PROJECT REQUIREMENTS

ALY6140 SUMMER 2020

 

 

python代写 IMPORTANT: Throughout all sections below, you should use markdown text cells in Jupyter to explain your analysis–from begining to end–including···

 

Captstone Project: Component Requirements  python代写

IMPORTANT: Throughout all sections below, you should use markdown text cells in Jupyter to explain your analysis–from begining to end–including introduction, explanation of analysis, charts, important insights and conclusions.

Data extraction:

  • Downloada publicly available dataset–the dataset must be publicly available
  • Describethe dataset
  • Describethe story (topic) that you intend to explore and the related questions you will be answering with the dataset

 

Data cleanup  python代写

  • Write(a) script(s)/function(s) to clean up the
  • Thismight involve:
    • cleaningup columns
    • removing/fillingmissing data
    • transformingdata
    • convertingfrom one data type to another
    • creatingnew variables/columns/features
    • imputation
    • textcleaning

 

Data visualization  python代写

  • Visualizeimportant dimensions of the data
  • Outlinea few trends you can see

 

Descriptive Analysis

  • Usevarious statistical and numerical methods you have learned in the program (Descriptive Statistics, Statistical Inference, Hypothesis Testing, ) to build your story and to analyze the questions you set out to answer
  • Explainyour analysis and the results of your analysis
  • Datavisualizations and the Descriptive Analysis are closely related and can be used together to build out the main content of your story and analysis

 

Predictive analytics  python代写

  • Once you have laid out your Descriptive Analysis, extend your story by supporting and extending yourconclusions using Predictive Analytics

 

Capstone Project: Grading

Capstone Project accounts for 40% of your course grade You must submit the following:

  1. a Jupyter notebook file titled Group_X_Capstone_Project–note the file extension ipynb – it shouldcontain the formal discussion of your Capstone Project components in clearly marked sections that match the Component Requirements – it should show number results, tables, visualizations, and any other related output of your analysis – it should import your Python module / script file and import the functions and other relevant code you define in it – every code cell in your Jupyter notebook should be executable and should execute without error
  2. a Python script file titled Group_X_Module.py – it should contain all or most of your code and functionsthat you will be calling in your Jupyter notebook – understandably, not all Python commands and functions used in the Jupyter notebook have to be in your Python script–for example, you might call some pandas or numpy functions in your

 

Python Libraries Requirements: python代写

Any package that has been discussed or mentioned in class is fair game to use. Those include:

  • pandas
  • scikit-learn
  • numpy
  • scipy
  • bookeh
  • matplotlib
  • seaborn
  • statsmodels
  • scipy
  • all basePython libraries

If you are in doubt, please ask. If you intend to use any visualization packages or other modeling libraries that are not on this list, please discuss with me first.

 

IMPORTANT: Any and all visualizations in your Capstone Project should be done only with the following 3 libraries (as discussed in class): 

  • bookeh
  • matplotlib
  • seaborn

 

Grading Rubric (Total of 50 points) python代写

  1. AnalysisDescription & Data extraction(17 pts)

 

  • Isthe dataset publicly available and downloadable/accessible (4 pts)
  • Isthe dataset properly described (3 pts)
  • Isthere code to download the dataset (3 pts)
  • Isthe analysis / research goal of your project properly described and defined–you should have a clear, logical, and cohesively-described statement about the goals of your analysis, the questions you will investigate and the way they are ultimately connected to answer the main goal/purpose of your Capstone Project  (7 pts)
python代写
python代写
  1. Datacleanup (5 pts)  python代写

  • Isthere some cleanup of the dataset (5 pts)
  • Mayinclude one or all of the following:
    • renamingcolumns
    • cleaningup/filling in missing values
    • selectingsubset of relevant columns
    • removing/fillingmissing data
    • transformingdata
    • convertingfrom one data type to another
    • creatingnew variables/columns/features
    • imputation
    • textcleaning
  1. Datavisualization (7 pts)  python代写

    • Visualizeimportant dimensions of the data
    • Outlinea few trends you can see
    • Usegraphics to support your story and analysis/conclusions
  2. DescriptiveAnalytics (16 pts)
  • Drawconclusions from the data using descriptive/predictive analysis
  • Areyour hypotheses supported by data/visualization?
  • Arethere surprising trends in the data
  • Arethere any potential recommendations
  1. PredictiveAnalytics (5 pts):
  • Utilizeone or more sklearn methods to do predictive analytics on your data (5 pts)
  • Predictiveanalysis should be aimed at answering your question of interest and providing further support of your conclusions and insights

 

Reminder of Academic Integrity Standards and Policies  python代写

This is a reminder of the importance of abiding by the rules of academic integrity when completing and submitting work for this class. Here are a couple of links (in addition to the discussion and rules outlined in the Syllabus) that are very helpful. I strongly suggest you review them and follow up if needed to clarify any of the rules or information contained therein.

http://www.northeastern.edu/osccr/academic-integrity-policy https://cps.northeastern.edu/academic-resources/academic-integrity

You should not submit as your own the work of others–this includes but is not limited to copying other people’s submissions or copying and pasting from external resources into your submissions. That is not acceptable.

Please use this opportunity to review and study the academic integrity policy.

 

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