/Customer-Behavior

Primary LanguagePython


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Shopium Customer Behavior Prediction

The Following is a general presentaion of our project
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Table of Contents
  1. About The Project
  2. Getting Started
  3. Usage
  4. Contact

About The Project

  • Shopium Customer Behavior Prediction is a machine learning API made for Shopium to better understand our customers orientation.

  • This module is being developped based on a logistic regression model using Python to study our clients likes and views on offers in a relation with their ages and sex.

  • This project was designed to run on Shopium's fake database generated by Shopium Faker

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Built With

This section should list any major frameworks/libraries used to bootstrap your project. Leave any add-ons/plugins for the acknowledgements section. Here are a few examples.

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Getting Started

  • This project was designed to predict customers behavior based on their ages and sex in a relation with their liked products and offers views.

  • Logistic Regression model was trained using sklearn and Shopium's fake data to test this whole module.

  • This module will take an array of offers IDs and return an array of the recommended offers IDs for a given client based on his/her age and/or sex.




### Prerequisites

This is a list of different main modules to install before implementing our project

  • pip 
    npm install npm@latest -g
  • Tesseract 
    npm install npm@latest -g



Installation

Below is an example of how you can instruct your audience on installing and setting up your app. This template doesn't rely on any external dependencies or services.

  1. Clone the repo

    git clone https://github.com/firas122/CustBehave

  2. Install pip packages

    pip install -r requirements.txt

  3. Run the API using command above (the application will be running on localhost ip address using 5000 as port):

    python /project_directory_path/CustBehave/api.py

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Usage

  • Terminal output will include an url by default 127.0.0.1 (localhost) running on port 5000 using the path /predict

  • Send a POST request to that url with three variables DB_url which contains the url to your mongo database, user_id that represents the userwe want to make recommendations for and offers_array an array of available offers for recommendations .

  • The returned result should be an array of offers ids to recommend as the example above :





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  • And the returning JSON Object should include possible matchs with offers :

Contact

Shopium - @Shopium - shopium.local@gmail.com

Project Link: https://github.com/firas122/Scan

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