Product Price Prediction

This repository contains a project that focuses on predicting the price of a product based on various features such as name, description, category, and brand name. The project utilizes a GRU-based RNN (Recurrent Neural Network) for price prediction and employs an SVM (Support Vector Machine) for brand name inference.

Project Overview

The goal of this project is to develop a predictive model that can estimate the price of a product given its relevant attributes. The attributes used for prediction include the product's name, description, category, and brand name. The project involves two main components: a GRU-based RNN for price prediction and an SVM for brand name inference.

Repository Structure

The repository is structured as follows:

- .gitignore
- README.md
- gru_model.py
- predictor_class.py
- requirements.txt
- svm_model.ipynb

• The .gitignore file specifies the files and directories that should be ignored by the Git version control system.

• The README.md file provides an overview of the project, its features, and instructions on how to use the repository.

• The gru_model.py file contains the implementation of the GRU-based RNN model for price prediction. This script defines the architecture of the model, trains it on the provided dataset, and saves the trained model for later use.

• The predictor_class.py file includes the class definition for the predictor. This class encapsulates the functionality of loading the trained models and making predictions based on the input data.

• The requirements.txt file lists the dependencies required to run the project. You can install these dependencies using the pip install -r requirements.txt command.

• The svm_model.ipynb notebook demonstrates the steps for training and evaluating the SVM model for brand name inference. It utilizes the dataset from the provided Mercari link.

Getting Started

To get started with this project, follow these steps:

1. Clone this repository to your local machine using the command:

   git clone https://github.com/your-username/product-price-prediction.git
   

2. Install the required dependencies by running:

   pip install -r requirements.txt
   

3. Open the svm_model.ipynb notebook to train and evaluate the SVM model for brand name inference using the provided dataset.

4. Use the gru_model.py script to train the GRU-based RNN model for price prediction. Adjust the script according to your requirements and run it to train the model.

5. Once the models are trained, you can utilize the predictor_class.py script to load the trained models and make predictions based on the input data.

Contributing

Contributions to this project are welcome. If you encounter any issues or have suggestions for improvements, please submit an issue or a pull request.

License

This project is licensed under the MIT License.

Acknowledgments

We would like to acknowledge the authors of the original dataset from the Mercari website for providing the data used in this project.