Tesla Stock Price Prediction using Facebook Prophet
3361 ya inscrito
3361 ya inscrito
In this 1.5-hour long project-based course, you will learn how to build a Facebook Prophet Machine learning model in order to forecast the price of Tesla 30 days into the future. We will also visualize the historical performance of Tesla through graphs and charts using Plotly express and evaluate the performance of the model against real data using Google Finance in Google Sheets. We will also dive into a brief stock analysis of Tesla and we will discuss PE ratio, EPS, Beta, Market cap, Volume and price range of Tesla. We will end the project by automating the forecasting process in such a way that you will get the forecast of any of your favourite stock with all necessary visualization within a few seconds of uploading the data. By the end of this project, you will be confident in analyzing, visualizing and forecasting the price of any stock of your choice. Disclaimer: This project is intended for educational purpose only and is by no means a piece of Financial advice. Please consult your financial advisor before investing in stocks. 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.
Time Series Forecasting
Data Visualization (DataViz)
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
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En un video de pantalla dividida, tu instructor te guía paso a paso
por VK4 de ago. de 2021
Great hands on introduction to Prophet, would enjoy a deeper dive into other functions of this library next time
por AJ7 de abr. de 2022
Nice Couse thanks Abhishek. I was able to understand the Prophet lib and with that I was able to make the predictions for bitcoin as well - https://www.prediction1.com/prediction/BTC
por MS6 de feb. de 2022
I really enjoyed this project. Beginner-friendly, clearly explained, and concise intro to FB Prophet. Thanks!
por NM18 de jul. de 2022
Good, concise course, clear instructions! What would have made it even better was hyperparameter tuning, a session on that would be useful.