Chevron Hot Dogs

Inspiration

We were inspired by our passions for data science, resource conservation, and hot dogs. We love hotdogs. When we heard about Chevron's challenge we knew immediately that it was perfect for us. We know that our model will be extremely useful in both conserving hotdogs and saving money.

What it does

We aim create an accurate cook plan for Chevron’s facilities in order to minimize hotdog waste.

How we built it

We coded in Python, used sklearn to create a prediction model, and used matplotlib to create the visualizations.

Challenges we ran into

We struggled to increase the accuracy of our prediction model, which was initially at 38.8% accuracy. We also struggled to get accurate predictions and effective clustering with our kmeans clustering algorithm.

Accomplishments that we're proud of

We had very limited experience with data science and machine learning, so we're proud of learning model predictions in a short amount of time.

What we learned

We learned how to create a more accurate linear regression model and how to create a kmeans clustering model. We also learned how to use pandas, sklearn, and matplotlib. We learned how to use sklearn.

What's next for Chevron Hot Dogs

We hope to continue to further increase our accuracy and predictive abilities. Our ultimate goal is that our algorithm is used someday with hot dog vendors all over the United States.

Built With

• matplotlib
• pandas
• python
• seaborn
• sklearn