Another module down--you're almost half way there!
All that remains in Phase 2 is to put our newfound data science skills to use with a large project! This project should take 20 to 30 hours to complete.
For this project, you will use regression modeling to analyze house sales in a northwestern county.
This project uses the King County House Sales dataset, which can be found in kc_house_data.csv in the data folder in this repo. The description of the column names can be found in column_names.md in the same folder. As with most real world data sets, the column names are not perfectly described, so you'll have to do some research or use your best judgment if you have questions about what the data means.
It is up to you to decide what data from this dataset to use and how to use it. If you are feeling overwhelmed or behind, we recommend you ignore some or all of the following features:
- date
- view
- sqft_above
- sqft_basement
- yr_renovated
- zipcode
- lat
- long
- sqft_living15
- sqft_lot15
It is up to you to define a stakeholder and business problem appropriate to this dataset.
If you are struggling to define a stakeholder, we recommend you complete a project for a real estate agency that helps homeowners buy and/or sell homes. A business problem you could focus on for this stakeholder is the need to provide advice to homeowners about how home renovations might increase the estimated value of their homes, and by what amount.
There are three deliverables for this project:
- A GitHub repository
- A Jupyter Notebook
- A non-technical presentation
Review the "Project Submission & Review" page in the "Milestones Instructions" topic for instructions on creating and submitting your deliverables. Refer to the rubric associated with this assignment for specifications describing high-quality deliverables.
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Your deliverables should explicitly address each step of the data science process. Refer to the Data Science Process lesson from Topic 19 for more information about process models you can use.
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Your Jupyter Notebook should demonstrate an iterative approach to modeling. This means that you begin with a basic model, evaluate it, and then provide justification for and proceed to a new model. After you finish refining your models, you should provide 1-3 paragraphs discussing your final model - this should include interpreting at least 3 important parameter estimates or statistics.
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Based on the results of your models, your notebook and presentation should discuss at least two features that have strong relationships with housing prices.
Start on this project by forking and cloning this project repository to get a local copy of the dataset.
We recommend structuring your project repository similar to the structure in the Phase 1 Project Template. You can do this either by creating a new fork of that repository to work in or by building a new repository from scratch that mimics that structure.
Review the "Project Submission & Review" page in the "Milestones Instructions" topic to learn how to submit your project and how it will be reviewed. Your project must pass review for you to progress to the next Phase.
This project will give you a valuable opportunity to develop your data science skills using real-world data. The end-of-phase projects are a critical part of the program because they give you a chance to bring together all the skills you've learned, apply them to realistic projects for a business stakeholder, practice communication skills, and get feedback to help you improve. You've got this!