There are a lot of factors that go into making a big move, and for many people, the top priority is either their job or their family. But if you’re on your own and you have job flexibility to go basically wherever you want (i.e. you work remotely), then what? This is our solution and visualisation for this dillema which helps in getting a better clarity regarding which place to choose being suitable from all the factors.
What it does
Basically, this helps the user to search for the cities according to the various inputs entered by him such as healthcare,]pollution, crime-rate etc. Clustering algorithm helps in finding and grouping those cities together. Data visualization helps further in making the results more clear which further makes it easier for the user to make the right choice of city he wants to live in.
To run the jupyter notebooks, make sure you have anaconda already set up, then.
- Clone this repository `git clone https://github.com/Ankitasareen/City-Search-Tool
- Activate conda environment in cmd
- Run the jupyter notebook City_search_tool.ipynb. To run the interactive UI for data visualisation deployed in streamlit,
- streamlit run app2.py in conda envt. *The web app is further deployed in heroku [link]https://city-search-tool-app-datathon.herokuapp.com/.
How we built it
We coded it in python and used the data visualisation tools such as seaborn, matplotlib, plotly. We used streamlit to make an interactive user interface for better data visualisation.
Challenges we ran into
*We faced problems while selecting appropriate clustering algorithm as per our needs.
*We had to learn using Streamlit in a small amount of time and implement.
*We learnt heroku deployment for webapp for the first time, faced various challenges but finally could deploy it.
Accomplishments that we're proud of
This being our first hackathon ,we were able to work in a team with proper coordination. We used Streamlit for the first time to create a interactive webapp for our project. We were able to come up with our own unique ideas which are presented in our project. We Came to learn about various clustering algorithms and Geop and Nominatim.