Generate competitive prices

Project Title: Fair Price Generation through Neural Networks and Interactive Application

Description: In this project, I developed a neural network model using the PyTorch library, specialized in deep learning, to tackle the challenge of generating fair prices based on a set of key variables. The idea behind this project is to provide users with a powerful and accurate tool to estimate fair prices in different contexts.

Objective: The main objective of the project was to create a system that could analyze and learn from a dataset containing relevant variables such as product features, market data, and other influential factors. The neural network model was trained to predict fair prices taking these variables into account.

Scope: The scope of the project encompassed research and the selection of relevant data, the creation and training of the neural network model using PyTorch, and the implementation of a user-friendly interface using Streamlit. The user interface allowed users to input specific data and receive real-time price predictions.

Key Achievements:

• Successful development of a PyTorch-based neural network model capable of generating fair prices.

• Integration of the neural network into an interactive application using Streamlit for an intuitive user experience.

• Validation and refinement of the model through extensive testing and adjustments.

• Providing users with a valuable tool for informed pricing decisions.

This project combines the power of artificial intelligence with the user-friendliness of a web application to offer accurate and practical solutions in fair price estimation. It was a rewarding experience to apply deep learning techniques and develop an application that can benefit a diverse range of users in pricing decision-making.

GitHub Repository