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Time series forecasting is a common data science task that helps organizations with resource allocation, demand planning and strategy management. In this project, you'll get hands-on experience with Facebook's open source library Prophet and you will be equipped with the knowledge to carry out fast, interpretable and reliable forecasts of business time series.
You will begin by creating a data set of historical stock prices for Microsoft in Google Sheets. You will then learn how to load the sheet in Python where you'll subsequently explore and preprocess the data set.
After that, you will dive right into Prophet. You'll become familiar with the key features of Prophet, and why it is preferred over other libraries. You'll learn about the basic forecasting procedure, options for model construction, adding custom seasonalities and holidays, and hyperparameter tuning for obtaining optimal results.
This Guided Project was created by a Coursera community member.
You will begin by creating a data set of historical stock prices for Microsoft in Google Sheets. You will then learn how to load the sheet in Python where you'll subsequently explore and preprocess the data set.
After that, you will dive right into Prophet. You'll become familiar with the key features of Prophet, and why it is preferred over other libraries. You'll learn about the basic forecasting procedure, options for model construction, adding custom seasonalities and holidays, and hyperparameter tuning for obtaining optimal results.
This Guided Project was created by a Coursera community member.
