Utilized sentiment-based features to predict cryptocurrency returns, models used: Random Forest Classifier, Random Forest Regressor, and VAR time-series model
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Updated
Jan 1, 2021 - Python
Utilized sentiment-based features to predict cryptocurrency returns, models used: Random Forest Classifier, Random Forest Regressor, and VAR time-series model
Beer national sales forecasting
The WasteTrack Time-Series API project is a web application developed to track and visualize waste production over time. It uses Flask, a Python web framework, to build the backend server and provides a user-friendly interface to interact with the waste data.
Arbitrage-free Dynamic Generalized Nelson-Siegel model of interest rates following Christensen, Diebold and Rudebusch; and its estimation using the Kalman filter / maximum likelihood.
Python software & data for analyzing the City of Philadelphia's Five Year Plan
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