This project contains the illustration of how the model works on a simple web page using Flask; a python framework. The app has been deployed on Render, a unified cloud platform used for building and running all types of apps and websites. On the web page, one will be able to enter the level of each feature (integers) and submit those level through a form present on the app. After that the data is fed into the trained model and gives the output or the prediction. The project also contains a jupiter notebook file that shows steps followed in trainining the data used on the web page which is found in model folder.
Note:
- Find the link to the video walkthrough [Here]https://www.youtube.com/watch?v=BNc98tS6-ys()
- Deployed version of the web pages Here
- Contribution score sheet Here
This project has used the some packages like numpy to transform the user inputs into a numpy array and other important packages which have to be installed to run this web app locally present in requirements.txt file.
To run the project locally, there is a need to have Visual Studio Code (vs code) installed on your PC:
- vs code: It is a source-code editor made by Microsoft with the Electron Framework, for Windows, Linux, and macOS.
- Clone the project
git clone https://github.com/UmuhireJessie/cancer-prediction.git
- Open the project with vs code
cd cancer-prediction
code .- Run the project
python app.py- Use the link printed in the terminal to visualise the app. (Usually
http://127.0.0.1:5000/)
- Jessie Umuhire Umutesi
- Patrick Mushimiye
- Mum Kiir