Review Bot Api

Introducton

ML Based Review Api designed to handle reviews given by User(customers) and Predict whether the Review is positive or negative

In addition to the pure API implementation from Scratch, a number of high-level classes to make the development of API easy and straightforward.

Dependencies Required Modules

1. nltk
2. regex
3. scipy
4. numpy
5. pandas
6. sklearn
7. Flask
8. Flask-RESTful
9. FLask-SQLALchemy
10. uwsgi
11. psycopg

Methodology/Principal

It consists of two important steps : Creating and Production

1. Creation

Train the Model Using Historical Dataset and test Accuracy the Model

How to train and test the Model

2. Production

Serialization & Deserialization

In the context of data storage, serialization is the process of translating data structures or object state into a format that can be stored.

This is done to reduce the size and complexity of dataset and which reduces the time of re-execution.

Creating an API using Flask

There are three important parts in constructing our wrapper function, Apicall():

• Getting the request data enter by user (for which predictions are to be made)
• Loading our pickled estimator
• jsonify our predictions and send the response back

Deployment

Heroku Cloud Station

Heroku is a cloud platform based on a managed container system, with integrated data services and a powerful ecosystem, for deploying and running modern apps.

Deployment Involves following process:

• Create Application
• Provid GitHub Connection
• Select Python as Build Packages
• Heruko Postreg:: DataBase
• Deploy Application