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.

1. Clone the repository:

git clone [https://github.com/lkasym/NFT_Analysis]

2. Navigate to the directory:

cd path_to_directory

3. 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.