scottarterbury / project-4

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project-4's Issues

Creating documentation

Provide notations in code, create a readme, show what data sources we used, how we decided on which data to use.

Building the ML model

Testing

Test code and analysis.

Data Analysis

Proposal

Provide the proposal to the Instructor for approval.

Create Workflow

The following project guidelines focus on teamwork, your project proposal, data sources, and data cleanup and analysis. Although you will divide the work among the group members, it’s essential to collaborate and communicate while working on different parts of the project. Be sure to check in with your teammates regularly and offer support.

Present to the class

Keeping track of the explorations and cleanup steps in a Jupyter notebook to keep organized and make it easier to present your work.

After cleaning the data and we are ready to start crunching numbers, track our work in a Jupyter notebook dedicated to analysis. Focusing our analysis on multiple techniques, such as aggregation, correlation, comparison, summary statistics, sentiment analysis, and time-series analysis. Include plots during both the exploration and analysis phases. Creating plots along the way can reveal insights and interesting trends in the data that we might not notice if you wait until we prepare for our presentation.

Data fetching

Provide sources.

Creating the presentation

Create Deck Slides with visuals.