IcoOmen, a machine learning model which predicts the value of an ICO token after 6 months. This uses historic data that has been aggregated from various public websites and APIs, as well as data that has been manually collected and calculated.
Article can be found here.
- Click on the link on the top of ICOData.ipynb or click here.
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Install Docker and Docker-Compose https://docs.docker.com/compose/install/#install-compose
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Build and Run Docker-compose
docker-compose up
- Head to url specified from terminal.
- Run locally via Jupyter Notebooks.
Run the following sections of Code:
- Library Imports and creating useful functions.
- Create Folders if Necessary and download dateset.
- Loading ICO dataset into variables.
- Encoding and Splitting of Data.
- Linear Regression/Neural Network.
- Load Saved Linear Regression Models and Print out performance.
- Use Model to make prediction - Value of ICO after 6 months.
Replace Example ICO with your ICO to predict. Use dataset/Country_Number_Mapping - Sheet1.csv to map a country to a number.# Load model with best rMse and make prediction fileName = "results/" + "bestRegressionModel_" + str(LineaReggressionMetrics.ROOT_MEAN_SQUARED_ERROR.name) + ".sav" bestRegression = joblib.load(fileName) #Example ICO #price_usd,price_btc,total_supply,market_cap_usd,available_supply,usd_raised,eth_price_launch,btc_price_launch,ico_duration,month,day,country example_x = np.array([1.71456,0.00019931,1000000000,905793616,528295082,24000000,297.63,3420.4,7,8,9,182]) y_pred = makePrediction(bestRegression,example_x) print("Predicted value of example ICO after 6 months: ",y_pred )