Submission:
- Please submit your project via GitHub and send a private message on Slack to both Dan and Ivan with a link to it.
Our goal for this project is to develop a working technical document that can be shared amongst your peers. Similar to any other technical project, it should surface your work and approach in a human readable format. Your project should push the reader to ask for more insightful questions, and avoid issues like, "what does this line of code do?"
From a presentation perspective, think about the machine learning applications of your data. Use your model to display correlations, feature importance, and unexplained variance. Document your research with a summary, explaining your modeling approach as well as the strengths and weaknesses of any variables in the process.
You should provide insight into your analysis, using best practices like cross validation or any applicable prediction metrics (e.g., MSE for regression; accuracy/AUC for classification). Remember, there are many metrics to choose from, so be sure to explain why the one(s) you are using is reasonable for your problem.
Look at how your model performs compared to a baseline model, and articulate the benefit gained by using your specific model to solve this problem. Finally, build visualizations that explain outliers and the relationships of your predicted parameter and independent variables. You might also identify areas where new data could help improve the model in the future.
Objective: Detailed Jupyter technical notebook with a summary of your statistical analysis, model, and evaluation metrics.
- Requirements:
- Create Jupyter notebook with code, visualizations, markdown and fully ran top to bottom.
- Summarize your exploratory data analysis.
- Frame code so as to enhance your explanations.
- Explain your choice of validation and prediction metrics.
- Visualize relationships between your dependent variable and two of the strongest independent variables.
- Identify areas where new data could help improve the model.
- Two common ways to start models:
- "Kitchen Sink Strategy": throw all the variables in and subtract them out, one by one.
- "Single Variable Strategy": start with the most important variable and slowly add in while paying attention to performance.
- It may be worth exploring both to understand your data and problem. How slow is building and predicting the model with all the variables? How much improvement is made with each variable added?
- Recall that your variables maybe need transformation in order to be most useful.
- Algorithms have different requirements (say, random forest vs. logistic regression), and one may work better for your data than another.
- Strike a balance between writing, code, and visual aids. Your notebook should feel like a blogpost with some code in it. Force yourself to write and visualize more than you think!
- This deliverable combines unit Projects 3 and 4 from earlier in the course; however, now you will be using your own data! But feel free to refer to any resources and feedback provided during those projects.
- You can find previous General Assembly Presentations and Notebooks at the GA Gallery.
The rubric is available here.