Tesla Stock Price Prediction using Facebook Prophet
3021 ya inscrito
3021 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)
Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.
En un video de pantalla dividida, tu instructor te guía paso a paso
por AB15 de jun. de 2021
A very good project indeed. I learnt Facebook Prophet and was an eye opener for me who doesn't know anything about stocks
por KS4 de dic. de 2021
This was a very well designed and guided project - would love doing something similar on AI and ML
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 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.