Keras with Flask Implementation on the web
You must have Scikit Learn, Pandas, matplotlib, tensorflow (for Neural Network Model) and Flask (for API) installed.
This project has four major parts :
1.model.py - This contains code fot our Machine Learning model to predict employee salaries absed on trainign data in 'train_data_evaluation_part_2.csv' file.
2.app.py - This contains Flask APIs that receives Bookings CheckedIn details through GUI or API calls,Visualization, computes the precited value based on our model and returns it.
3.request.py - This uses requests module to call APIs already defined in app.py and dispalys the returned value.
4.templates - This folder contains the HTML template to allow user to enter Customer booking detail and displays the predicted Bookings CheckedIn.
- Ensure that you are in the project home directory. Create the machine learning model by running below command -
python model.py
This would create a serialized version of our model into a file model.h5
2.Run app.py using below command to start Flask API
python app.py
By default, flask will run on port 5000.
3.Navigate to URL http://localhost:5000
Enter valid numerical values in all 29 input boxes and hit Predict.
If everything goes well, you should be able to see the predcited Bookings CheckedIn vaule on the HTML page!
You can also send direct POST requests to FLask API using Python's inbuilt request module Run the beow command to send the request with some pre-popuated values -
python request.py