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The E-commerce Industry is rising day by day .So for this we need a Efficient E-commerce Recommendation system . So In this Project We Try to make a recommendation system which will recommend the Items to Authors,Publishers and Retailers in such a way so that they will get a personalized Feed back from user reviews. Retailer gets more profit By Understanding the customer and focusing on their purchase pattern.

License: MIT License

Python 5.59% CSS 29.34% JavaScript 2.96% SCSS 3.49% HTML 58.55% Mako 0.07%

book-recommendation-analysis's Introduction

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Issues Pull Requests Forks Stars License  

Theme approach

Today, the growth of technology has enabled everyone to make and buy items online. The E-commerce industry is expected to rise in the coming years and so there is a need for an efficient E-commerce recommendation system that suggests products to users . In our project, we try to recommend products to authors, publishers, and retailers by providing them with personalized feedback from user reviews. The retailers can also analyze the top trending books and focus on personalization for users or provide them with exciting offers which can motivate the user to make more purchases. We also provide a Heat map of users and give recommendations based on the demographic features also. So, in this Way, the retailer gets more profits by understanding the customer and focusing on their purchase pattern. # Document

Link to docs

Google Colab Links

https://colab.research.google.com/drive/1W3bnFPMFdxa1ExdYQxuYv43k5QzFrG10 https://colab.research.google.com/drive/18xQP5b_7MjMGtxNl-2XgHtH589J69B-K

To Setup

1. Activate the virtual environment
  	
python -m venv env
env\Scripts\activate

Install git lfs : referrences

to know more:https://youtu.be/9gaTargV5BY

git lfs install 
git lfs pull

pip install -r requirements.txt
python main.py

To run

Activate 
source env/bin/activate
python main.py

Db Migrate

python manager.py db init
python manager.py db migrate
python manager.py db upgrade

Website Demo

CUSTOMER SITE

Demo

RETAILER SITE

Demo

Demo

Scan the below QR to visit the Website

Bookly Store

Contribution Guidelines🏗

Are we missing any of your favorite features, which you think you can add to it❓ We invite you to contribute to this project and improve it further?

To start contributing, follow the below guidelines:

1. Fork this repository.

2. Clone your forked copy of the project.

git clone https://github.com/<your_user_name>/Book-Recommendation-Analysis.git

3. Navigate to the project directory 📁 .

cd Book-Recommendation-Analysis

4. Add a reference(remote) to the original repository.

git remote add upstream https://github.com/vikasdo/Book-Recommendation-Analysis.git

5. Check the remotes for this repository.

git remote -v

6. Always take a pull from the upstream repository to your master branch to keep it at par with the main project(updated repository).

git pull upstream main

7. Create a new branch.

git checkout -b <your_branch_name>

8. Perform your desired changes to the code base.

9. Track your changes:heavy_check_mark: .

git add . 

10. Commit your changes.

git commit -m "Relevant message"

11. Push the committed changes in your feature branch to your remote repo.

git push -u origin <your_branch_name>

12. To create a pull request, click on `compare and pull requests. Please ensure you compare your feature branch to the desired branch of the repo you are supposed to make a PR to.

13. Add appropriate title and description to your pull request explaining your changes and efforts done.

14. Click on Create Pull Request.

The geek🤓 behind this initiative:


Vikas

Book Recommendation System is a part of these open source programs:


Contributors👨‍💻👩🏽‍💻

Thanks to these wonderful people 🙌

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book-recommendation-analysis's People

Contributors

aayush-hub avatar akshay6890 avatar anirudhsai20 avatar ankit-kmar avatar arpit456jain avatar aryamanz29 avatar ashukv avatar chazuttu avatar kajalsinghbaghel avatar khareyash05 avatar manasi2001 avatar rajeshpanjiyar avatar subhangini avatar vamsimudaliar avatar vikasdo avatar vishvamporwal avatar

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