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