AprioriDataVisualization

It is a Data Mining Project in order to handle data and present it on a webpage.

The UI design is still in progress, here's a sneak peek of the frontend part

Association Rules

Association Rules has 3 important rules:

• Support: This measure defines the likeliness of occurrence of consequent on the cart given that the cart already has the antecedents.
• Cofidence: This measure gives an idea of how frequent an itemset is in all the transactions
• Lift: Lift controls for the support (frequency) of consequent while calculating the conditional probability of occurrence of {Y} given {X}.

There is another rule known as conviction:

• Conviction: It compares the probability that {X} appears without {Y} if they were dependent with the actual frequency of the appearance of {X} without {Y}.

Aim

My Aim was to give user all these details when a product is selected, so I made something like this:

All the data that you are seeing in this image actually comes from the algorithm that we have ran on the backend app.py file

How to run

1. Clone the repo
2. Go to the repo directory
3. Run backend:
   1. Open Terminal
   2. Type cmd: python app.py
   3. If it gives errors like module not found, install the module using pip install module. Replace module with the error module.
4. Run Frontend:
   1. Create new terminal
   2. Go to client folder
   3. Type cmd: npm install
   4. Type cmd: npm start
5. Enjoy

If you like this repo, check out my profile at github.com/RohanPunjani.

Thank You for making it this far XD