This application provides insights into the NFT market by allowing users to predict NFT prices, analyze market trends, view top sellers and buyers, explore NFT categories, and browse a curated gallery of NFTs.
- Price Prediction: Uses Linear Regression, KNN, and LSTM to predict the price of an NFT.
- Market Analysis: Displays the top NFT collections by sales volume.
- User/Trader Analysis: Showcases the most active sellers and buyers in the NFT market.
- NFT Categories:Visualizes the distribution of different categories of NFTs.
- NFT Gallery: Explore a curated selection of NFT images.
- Clone the repository:
git clone [https://github.com/lkasym/NFT_Analysis]
- Navigate to the directory:
cd path_to_directory
- Install the required libraries:
pip install -r requirements.txt
Run the Streamlit app with:
streamlit run streamli_nft.py
Open your browser and go to http://localhost:8501 to view the app.
The data used in this application comes from Processed_OpenSea_NFT_1_Sales.csv. It contains detailed information about NFT sales, including asset names, sale dates, prices, sellers, and more.
The prediction models are trained on data from 2019-2021. Predictions might not be accurate for the current date. Always conduct your own research before making any investment decisions.
Contributions are welcome! Please open an issue or submit a pull request.
This project is licensed under the MIT License.