This is a demo project to elaborate how Machine Learn Models are deployed on production using Flask API
You must have Scikit Learn, Pandas (for Machine Leraning Model) and Flask (for API) installed.
This project has four major parts :
- model.py - This contains code fot our Machine Learning model to predict Air Quality Index absed on training data in 'RealCombile.csv' file.
- app.py - This contains Flask APIs that receives Air Quality details through API calls, computes the precited value based on our model and returns it.
- templates - This folder contains the HTML template to allow user to enter details regarding atmosphere and displays the predicted Air Quality Index.
- Ensure that you are in the project home directory. Create the machine learning model by running below command -
python model.py ( model can be anything like Linear Regression,Random Forest,Decision Tree,XgBoost etc)
This would create a serialized version of our model into a file model.pkl
- Run app.py using below command to start Flask API
python app.py
By default, flask will run on port 5000.
- Navigate to URL http://localhost:5000
You should be able to view the homepage as below :
Enter valid numerical values in all 8 input boxes.
After Enterting valid inputs into 8 input boxes hit Predict.
If everything goes well, you should be able to see the predcited AQI vaule on the HTML page!
