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ML Model Deployment using flask

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Mahaveer Rulaniya edited this page Jun 17, 2021 · 1 revision

This project elaborates how Machine Learn Models are deployed on production using Flask API

Flask

Prerequisites

  • Scikit Learn
  • Pandas
  • Numpy
  • Flask

Deployment Structure

This contains four major parts -

  1. model.py - This contains code for our Machine Learning model for prediction.
  2. app.py - This contains Flask APIs that receives employee details through GUI or API calls, computes the precited value based on our model and returns it.
  3. request.py - This uses the requests module to call APIs already defined in app.py and displays the returned value.
  4. templates - This folder contains the HTML template to allow the user to enter employee detail and displays the predicted employee salary.

Project Details || Website || Learning Resources || Motivatin of the Project || Deployment using Flask

Project Sections

  1. Agriculture
  2. Healthcare
Name Domain Data-set
Crop Recommendation Agriculture Crop-data
Fertilizer Prediction Agriculture fertilizer-data
Plant Disease Agriculture plant-disease-data
Stroke Prediction Healthcare stroke-data
Life Expectancy Healthcare exptency-data
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