Minimum Viable Product Paper

Hi, I hope you doing good, I need you to do project using all python library which will be explained

  • project proposal : short description shared with instructors Additionally, ——– in one day
  • Minimum Viable Product (MVP)
    submission 6 days
  • Written description, 6 days
  • presentation
    slides PDF, and project code
    Project presentation 6 days

Summary:

Using any appropriate data source, perform some exploratory data analysis and build one or more models (such as a classification models, natural language processing, recommendation systems, etc.) that address a useful prediction and/or interpretation problem in any domain of interest. Communicate your process and findings in a 5 minute presentation at the end of the course and a short written description.

Metis projects are broken down into 5 component parts — design, data, algorithms, tools, and communication — that are each scored individually. Below is a description of each component as it relates to this project. For more detail on how these components are graded and how extra points are rewarded, refer to the project success guide.

Design:

  • The project should be centered around a well-posed data science problem in a domain of interest that can be reasonably addressed by available data
  • All deliverable deadlines should be met, reflecting professionalism and effective scoping and workflow

Data:

  • The data can be acquired by any means: straightforward downloads, use of APIs, web scraping, etc.
  • Aim to have 10,000+ data points and 10+ features so that you can build a robust model with interesting feature engineering and selection challenges; consult with an instructor if you are considering deviating from this rule of thumb, which is often fine.

Algorithms

  • Building a data science model in python is required, including trying at least several of the models covered during the course. You should also spend extra time thinking about feature engineering. How can you come up with representations of the data that are really relevant to the business / the predictive problem, and demonstrate that the features you’ve engineered add value?
  • Rigorous model selection and evaluation (i.e. proper validation and testing) is required, using appropriate metrics when applicable
  • Other methods and metrics (beyond those covered in the course) are optional

Tools

  • Use the python packages for data science models (sklearn, xgboost, etc.) that were covered in class
  • Other tools not covered in the course are optional but welcome. We highly encourage you to use at least one and preferably more of these optional tools to address at least one of storage/infrastructure, processing, visualization, and production.
  • Major examples of applicable tools include:
    • Storage tools could include SQL or noSQL (e.g. MongoDB) databases
    • Processing tools could include Google Cloud or Amazon Web Services for cloud computing resources
    • Visualization tools could include python libraries
    • Production tools could include Flask or other web app libraries/technologies

Communication:

  • Students will deliver a 5 minute slide presentation at the end of the course. These should be given in a compelling, clear manner and include effective visuals for communicating your objectives and findings
  • You will also submit a ~1 page written description summarizing your work: it should begin with a ~100 word abstract to be followed by a breakdown of your project along the 5 major components

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