coolstorm

CoolStorm predice fallas en coolers industriales antes de que ocurran, esto ayuda a reducir costos por reemplazos inesperados, con un amplio análisis del mercado.

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Inspiration

we have always been interested in how technology can help us predict and prevent problems before they happen. This project gave us the opportunity to apply that passion to a very practical issue: keeping critical equipment running smoothly in everyday business operations

What it does

It processes operational data from the cooler such as temperature, and the frequency and duration of door openings and compares it with historical failure records. Based on these patterns, it estimates the probability of a failure, enabling preventive maintenance ahead of time.

How we built it

whit python, we found out and discovered algorithms to predict the posible failures in coolers, we also structured a diagram first to get a more clearly idea

Challenges we ran into

the little mistakes in the program stopped us many times one of those retard our progress for an hour, the structure was also hard to define

Accomplishments that we're proud of

We learned many new commands and functions that can be used in Python. We are proud that not knowing something didn’t stop or frustrate us instead, through research, videos, and support from AI, we were able to create a program like CoolStorm

What we learned

Throughout this project, we developed key technical skills in Python, learning to use powerful tools such as data cleaning functions, machine learning libraries, and data visualization modules. We also improved our teamwork, working more fluidly and efficiently as a group. Additionally, we explored basic concepts of cost estimation and carried out a simple market analysis to better understand the potential value and impact of our solution.

What's next for coolstorm

We are excited to keep learning and exploring programming languages, and how they can help us solve real-life problems and develop practical, everyday solutions.

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