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A comprehensive machine learning project using Facebook's Prophet to forecast future sales. The model utilized historical data and effectively accounted for various factors, including seasonality effects, demand fluctuations, holiday impacts, promotional activities, and competitive influences.
This Project analyses the carbon footprint of the U.S. commercial sector using three machine learning models. A combination of energy consumption data and carbon dioxide emission data was used to achieve the carbon footprint variable.
A python Project that use syfinance, pandas, and webbrowser to display information about Stocks and exporting them to .CSV file. Also predict Stock prices
Prophet time series forecasting model was developed by Facebook and is a powerful tool for predicting future events. Here's how to use it to forecast & understand your Data
Predict cash that should be allocated to ATM is crucial. If the ATM is lack of money, customer couldn't get the cash they need then customer satisfaction will decrease. With prophet, model created by Facebook data team, we could conduct time series forecasting to decide amount of money needed.
Este repositório tem como objetivo demonstrar uma análise preditiva com séries temporais utilizando Machine Learning. O dataset utilizado é relacionada a uma estação metereológica localizada na cidade de Jena, Alemanha.
Neste repositório é feita uma análise preditiva com séries temporais com o objetivo de prever o consumo de energia elétrica em uma residência nos próximos 365 dias do ano.