https://github.com/paritoshtripathi935/product-matching

The topic is about product matching via Machine Learning. This involves using various machine learning techniques such as natural language processing, image recognition, and collaborative filtering algorithms to match similar products together.
https://github.com/paritoshtripathi935/product-matching

amazon-scraper collaborative-filtering data-science django flipkart-scraper-python langchain machine-learning nlp opencv product-matching python

Last synced: about 3 hours ago
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The topic is about product matching via Machine Learning. This involves using various machine learning techniques such as natural language processing, image recognition, and collaborative filtering algorithms to match similar products together.

• Host: GitHub
• URL: https://github.com/paritoshtripathi935/product-matching
• Owner: paritoshtripathi935
• Created: 2023-02-19T15:25:40.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-19T08:18:00.000Z (6 months ago)
• Last Synced: 2024-05-30T12:40:24.318Z (6 months ago)
• Topics: amazon-scraper, collaborative-filtering, data-science, django, flipkart-scraper-python, langchain, machine-learning, nlp, opencv, product-matching, python
• Language: Python
• Homepage: https://paritoshtripathi935.github.io/Product-Matching/
• Size: 8.45 MB
• Stars: 11
• Watchers: 3
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        


  

  

  

  

  

	
Product Matching Using Machine Learning

	


	
The topic is about product matching via Machine Learning. This involves using various machine learning techniques such as natural language processing image recognition and collaborative filtering algorithms to match similar products together. To implement this project a low-level project structure is suggested with different folders for data notebooks source code and testing. The sequence of model implementation and useful Python libraries for product matching via Machine Learning are also described. Finally a 3-month timeline is presented for the development to deployment of the product matching project.

  

    

      


        
Home

        
Description

        
Project Structure

        
Model Implementation

        
Python Libraries

        
Timeline

      

    

  

Project Structure

The project is structured into several folders, including:



	


data: This folder contains the raw and processed data used in the project, as well as any trained models.

	


notebooks: This folder contains Jupyter notebooks for data exploration, data visualization, and model testing.

	


src: This folder contains all the source code for the project, including preprocessing scripts, model implementation, and utility functions.

	


tests: This folder contains unit tests for the implemented models.



Model Implementation

The project uses various machine learning techniques such as natural language processing, image recognition, and collaborative filtering algorithms to match similar products together. The implementation of the models is done in the following sequence:



	
Data Collection and Preprocessing

	
Exploratory Data Analysis

	
Model Development and Testing

	
Model Fine-tuning and Evaluation

	
Model Deployment in Test Environment

	
Model Performance Optimization



Python Libraries

The following Python libraries are useful for product matching via Machine Learning:



	


Scikit-Learn: for implementing various machine learning models.

	


Pandas: for data manipulation and preprocessing.

	


NumPy: for numerical operations.

	


Matplotlib and Seaborn: for data visualization.

	


TensorFlow: for deep learning models.



Timeline

The product matching project can be completed within a 3-month timeline with the following plan:



	
Month 1: Data Collection and Preprocessing

	
Month 2: Model Development and Testing

	
Month 3: Model Deployment and Optimization

	
Month 4: GUI devleopement using Flask/Fastapi



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