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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.

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Product Matching Using Machine Learning

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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. 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.

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:

  1. Data Collection and Preprocessing
  2. Exploratory Data Analysis
  3. Model Development and Testing
  4. Model Fine-tuning and Evaluation
  5. Model Deployment in Test Environment
  6. 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:

  1. Month 1: Data Collection and Preprocessing
  2. Month 2: Model Development and Testing
  3. Month 3: Model Deployment and Optimization
  4. Month 4: GUI devleopement using Flask/Fastapi

Note: Datasets in this repository are intended for research and educational use only.

Dear users, We would like to bring to your attention that the datasets provided in this repository are solely meant for research and educational purposes. These datasets have been carefully curated and compiled to facilitate scientific exploration, analysis, and learning.

While we encourage the use of these datasets to further knowledge and understanding in various fields, we want to emphasize that their usage should be limited to research and educational endeavors. It is essential to respect the terms and conditions associated with each dataset and adhere to any applicable licenses or permissions.

As you engage with the datasets, we kindly request that you keep the following guidelines in mind:

  • Research Use: The datasets are intended for conducting research and analysis. They can be utilized to investigate and develop new methods, algorithms, models, or techniques. Please ensure that your work aligns with the principles of responsible and ethical research.

  • Educational Use: Students, educators, and researchers are encouraged to leverage these datasets for educational purposes, such as teaching, coursework, or academic projects. They can serve as valuable resources for understanding real-world scenarios and conducting hands-on experiments.

  • Respect Data Usage Restrictions: Some datasets may have specific terms of use, licenses, or restrictions associated with them. It is crucial to adhere to these requirements and honor any limitations on data access, redistribution, or commercial use.

  • Attribution: When utilizing these datasets, it is recommended to provide appropriate attribution. Cite the original sources and acknowledge the efforts of the data providers to promote transparency and intellectual integrity.

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Remember that the datasets made available here are the result of extensive efforts, collaboration, and data sharing. By adhering to these guidelines, we can collectively contribute to a culture of responsible data usage and foster a positive and impactful research and educational community.

Should you have any questions or concerns regarding the datasets or their usage, please feel free to reach out to us. We are here to support and assist you in your research and learning endeavors.

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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.

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