Predicting Gas Price Based on Income Status and location

Inspiration:

• Concerns regarding gas prices on the rise and what it might entail for LA residents of varying income levels.

What it does

• Looking into the relationship between average income level by zipcode and whether there is a correlation with gas prices within LA County.

How we built it

• Using a Jupyter Notebook to organize analysis and committing to git.

Challenges we ran into

• Smaller dataset, therefore, searching for the right models to accurately test the data.

Accomplishments that we're proud of

• Being able to use visualizations to understand how correlations subverted our understanding of gas prices and average income level by zip code.

What we learned

• Learned prioritizing time for each aspect of the analysis
• Spend more time validating data to make sure we are working with the right materials to answer our question.

What's next for Predicting Gas Price Based on Income Status and location

• Using an API to get real-time data and possibly expanding our hypothesis to cover the entire state of California.

Built With

• github
• jupyter
• kaggle
• melissa
• python