Inspiration

When we were trying to find a job online, we found many websites didn't cover all the information we care about. When we look for jobs, we are not only concerned with the work itself, but also with the cost of living, population density and safety of the city in which we are going to work. So we are going to create a platform that can help us achieve this function, which will help us find the ideal job and city to live!

What it does

Provides candidates with an enhanced talent acquisition experience. Better job, better live!

How we built it

Build backend algorithms in Python with open data from other online sources and integrate all algorithms with Google Cloud Platform Design a user friendly web server for dreamjob.ai

Challenges we ran into

How to use the open data, such as climate information, housing pricing and crime data, to empower our recommendation algorithms and Google Cloud Platform to increase quality of job searching

Innovation

Backend algorithm! Scientific way to figure out which job and which city is the best choice for the candidates. And it increases quality hire, decrease time to find a ideal job and continue to improve matching over time

What we learned

How to use Google Cloud Platform

What's next for dreamjob.ai

We can optimize our dreamjob.ai website and add more new features to better help candidates find their dream jobs.

dreamjob.ai

Built With Python 3.7, Bash, Zillow API, Google Cloud Platform, HTML, PHP, Flask, Bootstrap

Contributer Zhengqiao Zhao, Chen Chen, Lin Li, Siling Chen

Conclusion

Our dreamjob.ai efficiently and scientifically provides candidates with an enhanced talent acquisition experience.

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Updates

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Inspiration

⋅⋅⋅When we were trying to find a job online, we found many websites didn't cover all the information we care about. When we look for jobs, we are not only concerned with the work itself, but also with the cost of living, population density and safety of the city in which we are going to work.

⋅⋅⋅So we are going to create a platform that can help us achieve this function, which will help us find the ideal job and city to live!

What it does

⋅⋅⋅Provides candidates with an enhanced talent acquisition experience

How we built it

⋅⋅⋅We used python to build backend algorithms and integrate all algorithms with Google Cloud Platform.

Challenges we ran into

⋅⋅⋅How to make a user friendly interactive interface and how to integrate.

Accomplishments that we're proud of

⋅⋅⋅Interactive way to reduce the search space Scientific way to search for the best hydrant

What we learned

⋅⋅⋅How to build a web server and use Google Cloud Platform

What's next for Fire Hydrant Finder

⋅⋅⋅We can optimize our website and add new features.

* dreamjob.ai *

⋅⋅⋅Built With ⋅⋅⋅Python 3.7 ⋅⋅⋅Dash ⋅⋅⋅Zillow API ⋅⋅⋅Google Cloud Platform ⋅⋅⋅Contributer ⋅⋅⋅Zhengqiao Zhao ⋅⋅⋅Chen Chen ⋅⋅⋅Lin Li

⋅⋅⋅Siling Chen

Goal of the Project

⋅⋅⋅Provides candidates with an enhanced talent acquisition experience

Why we want to do it Social Need Fit Water department has all the locational data about the water hydrants instead of Fire department. Because of this asymmetric information issue, in any event of emergency, it will cause too much time for Fire Department to figure out the nearest working hydrants. Significant Social Value Our product aims to save lots of time for fire department to find out the nearest working water hydrant greatly. They don’t need to call the Water Department to ask for that information and they don’t need to waste time on the water hydrants that is out of service. Every accurate location we generate could actually help one person, one family in any emergent issue. Every second we save does really matter!!! Data American Water provide the dataset of hydrant information for NJ. The data set contains latitude, longitude, out of service and critical notes.

Our Approach

We use scientific way to calculate the distance between two locations and interact with Google Map API to plot the location pin in our web server.

Innovation

Backend algorithm! ⋅⋅⋅Scientific way to figure out which job and which city is the best choice for the candidates.

Conclusion

⋅⋅⋅Our product efficiently helps Fire Department to find the nearest working hydrant in NJ area. In the future we can generalize this product into other States and other industry area.

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