Bitcoin Price Prediction using Facebook Prophet

In this 1.5-hour long project-based course, you will learn how to create a Facebook Prophet Machine learning Model and use it to Forecast the Price of Bitcoin for the future 30 days. We will begin by importing all the necessary libraries including Facebook Prophet. Then we will import our dataset and analyze it. Then we will start creating visualizations in Plotly express in order to understand the historical performance of Bitcoin. We will then prepare our data for Facebook Prophet and create a Facebook Prophet Machine learning Model. We will then fit our prepared data to the Facebook Prophet Model and command it to make a Forecast for the future 30 days. We will then Visualize the Forecast using the Prophet’s internal visualization tools and then download the Forecast data. In the final section, we will go to Google Sheets and learn to extract Financial data of Bitcoin using Google Finance. We will then import the Forecast data into Google Sheets and compare it against the actual data and evaluate the performance of the Model. Please note that although this project deals with Bitcoin and teaches to make Price predictions, it is for educational purposes only and should not be taken for a piece of Financial advice since Cryptocurrencies like Bitcoin are extremely volatile and speculative. Basic knowledge of Python programming language is recommended but even those with no prior programming experience will be able to complete this project. You will need a Google account to complete this project. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

• Project Overview
• By the end of this project, you will be able to create a Facebook Prophet Machine learning Model and use it to predict the future price of Bitcoin. The same skills can also be applied to any other time-series data like Stock Prices or any other Cryptocurrencies. We will begin by importing all the necessary libraries including Facebook Prophet. Then we will import our dataset and analyze it. Then we will start creating visualizations in Plotly express in order to understand the historical performance of Bitcoin. We will then prepare our data for Facebook Prophet and create a Facebook Prophet Machine learning Model. We will then fit our prepared data to the Facebook Prophet model and command it to make a Forecast for the future 30 days. We will then Visualize the Forecast using the Prophet’s internal visualization tools and then download the Forecast data. In the final section, we will go to Google Sheets and learn to extract financial data of Bitcoin using Google Finance. We will then Import the Forecast data into Google Sheets and compare it against the actual data and evaluate the performance of the Model.