Lung-Cancer-Model-Deploy-to-Live-Production

Deploying Lung Cancer Machine Learning model to web Applications

Using scikit-learn, pickle, Flask, Microsoft Azure and ipywidgets to fully deploy a Python machine learning algorithm into a live, production environment

Steps

• Step 1: Develop a Machine Learning Algorithm
• Step 2: Make an Individual Prediction from the Trained Model
• Step 3: Develop a Web Service Wrapper
• Step 4: Deploy the Web Service to Microsoft Azure
• Step 5: Building a Client Application to Consume the Azure-deployed Web Service

Attribute details:

1. Gender: M(male), F(female)
2. Age: Age of the patient
3. Smoking: YES=2, NO=1.
4. Yellow fingers: YES=2, NO=1.
5. Anxiety: YES=2, NO=1.
6. Peer_pressure: YES=2, NO=1.
7. Chronic Disease: YES=2, NO=1.
8. Fatigue: YES=2, NO=1.
9. Allergy: YES=2, NO=1.
10. Wheezing: YES=2, NO=1.
11. Alcohol: YES=2, NO=1.
12. Coughing: YES=2, NO=1.
13. Shortness of Breath: YES=2, NO=1.
14. Swallowing Difficulty: YES=2, NO=1.
15. Chest pain: YES=2, NO=1.
16. Lung Cancer: YES, NO.