[Project Addition]:Market basket Analysis

[manishh12]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Market Basket Analysis
:red_circle: Aim : Market basket analysis aims to identify patterns of co-occurring items in customer transactions, helping businesses optimize product placement, cross-selling, and marketing strategies to enhance sales and customer satisfaction.
:red_circle: Dataset : https://www.kaggle.com/datasets/rashikrahmanpritom/groceries-dataset-for-market-basket-analysismba/data?select=Groceries+data.csv
:red_circle: Approach : I will be implementing Apriori Algorithm for initial stage but later i will try to implement using FP-Tree.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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:red_circle::yellow_circle: Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name : Manish Kumar Gupta

• GitHub Profile Link : https://github.com/manishh12

• Email ID :manishdid360@gmail.com

• Participant ID (if applicable):manishh12

• Approach for this Project : Association Rule Mining: I will use the MLxtend library to perform association rule mining, specifically the Apriori algorithm, to find frequently occurring itemsets in the dataset. I will generate association rules based on metrics like support, confidence, and lift. I will calculate support values to identify itemsets that occur frequently together. I will calculate confidence values to measure the likelihood of one item being purchased given that another item is purchased. I will calculate lift values to determine the strength of association between items, considering the effect of itemset size on confidence. I will filter rules based on certain thresholds for support, confidence, and lift to extract meaningful associations.

• What is your participant role? (Mention the Open Source program)Contributor in GSSOC'24

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  Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[abhisheks008]

Hi @manishh12 thanks for showing up with this issue. As per the code of conduct you can work on one issue at a time.

[manishh12]

Hi @manishh12 thanks for showing up with this issue. As per the code of conduct you can work on one issue at a time.

Okay Sure, I'll address the tasks one by one as I complete them.