lims-with-recommendation-engine(SDL mini project)

A LIMS(library information management system) which recommends book using apriori algorithm.

The mini project was completed in the third year in SDL Lab by our team

Prerequisites

Install dependencies using pip3 install -r requirements.txt

start mysql using service mysql start and create database by the name of 'lims' and import database using

mysql -u root -p lims < dbexport.sql

grant privilage to user root to access database lims using following :

$ sudo mysql -u root # for new installation

mysql> USE mysql;
mysql> UPDATE user SET plugin='mysql_native_password' WHERE User='root';
mysql> FLUSH PRIVILEGES;
mysql> exit;

$ service mysql restart

If your MySql is set with your password

$ sudo mysql -u root -p

- Then grant privilage using the above statements.<br>
- Provide your MySql root password into __init__.py file ; # mysql+pymysql://root:your_password@localhost/lims<br>
- $ service mysql restart

---

if problem persists refer
https://stackoverflow.com/questions/39281594/error-1698-28000-access-denied-for-user-rootlocalhost

** Our Approach of recommender engine **

The algorithm used in the recommendation has taken its inspiration from market basket analysis. further explaination of this market basket analysis:

• if a user buy milk in a grocery store then he/she also buy bread and some other products,so this is a transation
• if several users in the grocery store buy milk and bread,
• now the algorithm will check all the transactions and will found out that several users who buy milk also buy bread
• if a new user come to store the Apriori Algorithm will recommend the user to buy bread if he/she buy mil as it has been preferred the a huge set of community.

Inspiration goes like this:

• firstly a window of time was kept to watch the transaction(books read by the user in the given window) happened
• then consider that if there were 'n' transaction happened i.e 'n' no of users issued the books in the given window and each issue on books on a particular day was ammended into the transactions.
• Consider there the 100 transactions happened in the given window and 4 books are common in the transactions which are('Joy of computing with python','DSA','ML','ADS')
• then if a new user comes and issue a DSA book then he/she will also be recommended the other three books('Joy of computing with python','ML','ADS')

using

git clone https://github.com/lims-with-autorecommendation/flask_web_App
cd flask_web_App
virtualenv venv
source venv/bin/activate
pip3 install -r requirements.txt
open the teminal and fire ``python3 run.py``    

TODO

• Redirect issue when issuing and returning book but can be sloved easily by refactoring account route.
• variable names must be changed for readabiliy, maintainability and Understandability.
• Code should be refactored to reduce number of html page in template folder.
• Make a single route for all the book and which works on addition of any book in future.
• Design better schema for database.
• Remove dependency from sqlalchemy ORM and therefore dependent code should be proted to flask-sqlalchmey.
• Better UI for account page .
• Improve Reccommendation by merging collaboration filtering,decision tree and content based methods.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.