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Smart disease prediction system made using traditional machine learning algorithms and to create an user interface using streamlit. πŸš€

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Predico: The smart health predictor

Overview

To create a smart disease prediction system made using traditional machine learning algorithms and to create an user interface using streamlit.

Technical aspect

  1. Created and evaluated a model using descision tree, random forrest and gradient boost.
  2. Building and hosting a streamlit based web app on heroku (coming soon)

Directory tree

β”œβ”€β”€ dataset
β”‚   β”œβ”€β”€ test_data.csv
β”‚   └── training_data.csv
β”œβ”€β”€ saved_models
β”‚   └── random_f.joblib
β”œβ”€β”€ app.py
β”œβ”€β”€ main.py
β”œβ”€β”€ config.yaml
β”œβ”€β”€ LICENSE
└── README.md

To do

  1. Create the heroku hosting files
  2. Add dark mode to streamlit UI using overflow
  3. Creating UI to read into given diagnosis

Technologies used

Read a brief report of the following project here

Contributions are welcome please consider making a pull request!

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Smart disease prediction system made using traditional machine learning algorithms and to create an user interface using streamlit. πŸš€

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