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

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.

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

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.

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

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