Deploy Machine Learning Model with backend(flask) and frontend(Angular) -Part1

In this tutorial, we will learn how to deploy a simple machine learning model into a real life application.
Here, we will use titanic data to analyse and create a titanic ML model which can predict whether a passenger will survive or not.
Imagine, if you were a passenger of titanic and you traveled on that time. You want to know whether you would survive or not if you were there on that time.

Our aim will be to create a website where a user can input his/her details and finally it will show whether that passenger would survive or not with the help of a backend and ML model.

1. Create a jupyter notebook and analyse titanic dataset. Process and finally build,train a machine learning model.
2. Create a backend with flask which can provide the prediction using the trained model from the jupyter notebook.
3. Lastly, create a frontend where a user can provide his/her details and can get a prediction from the backend whether he or she would survive if the user would be on the titanic during 1912. 

The whole project can be found at github

This is the frontend of our titanic project which is deployed here

The backend is deployed here.

Step-1: Data Analysis [Find the notebook from here]

Our data looks like this.

Cleaning data

Build the machine learning model from the data

So, we have the trained model and it is saved as svm_model.pkl
In the next step, we will use this model in our backend with flask.

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