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Вернуться к Tesla Stock Price Prediction using Facebook Prophet

Отзывы учащихся о курсе Tesla Stock Price Prediction using Facebook Prophet от партнера Coursera Project Network

4.4
звезд
Оценки: 43
Рецензии: 10

О курсе

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....

Лучшие рецензии

AJ

7 апр. 2022 г.

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

MS

6 февр. 2022 г.

I really enjoyed this project. Beginner-friendly, clearly explained, and concise intro to FB Prophet. Thanks!

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1–13 из 13 отзывов о курсе Tesla Stock Price Prediction using Facebook Prophet

автор: natalie G

13 июня 2021 г.

Fellow New Zealander in the USA here. I love your instructing, teaching and this concept so much! As a Technological Entrepreneur female Nerd, I am looking for more from you and will definitely use this on my stocks over and over again. By fire and By force I have installed this into my brain. I am bouncing and defeating all my competitors! Computer, AI and Data science rocks!!!!!

автор: Sahil S

29 дек. 2021 г.

This is a great project that focuses on using machine learning to forecast stock prices using real-world examples and financial terminology. I highly recommend it to anyone who wants to start stock trading!

автор: Avinash J

7 апр. 2022 г.

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

автор: Mohamed S

7 февр. 2022 г.

I really enjoyed this project. Beginner-friendly, clearly explained, and concise intro to FB Prophet. Thanks!

автор: Keyur S

4 дек. 2021 г.

This was a very well designed and guided project - would love doing something similar on AI and ML

автор: Horacio B R

28 июля 2021 г.

E​xcellent

автор: Joshua

8 июня 2021 г.

Nil

автор: अच्छे व

6 мар. 2022 г.

It's a very good course for those who are just started in ML(Trading). It start's from basic and I think well mantain course to automate process in the end.

автор: Ajith B

16 июня 2021 г.

A very good project indeed. I learnt Facebook Prophet and was an eye opener for me who doesn't know anything about stocks

автор: Vladyslav K

5 авг. 2021 г.

Great hands on introduction to Prophet, would enjoy a deeper dive into other functions of this library next time

автор: Kleider S V G

4 мар. 2022 г.

Clear and concise!

автор: Jair C

28 сент. 2021 г.

i​t´s so basic.

автор: Martín J M

2 янв. 2022 г.

I​ts good if you have no idea about how to use python por: plotly, pandas, prophet etc. Maybe the pace and content are adequate for an hour class.

I​ would say its rather superficial. For instance, it teaches facebook prophet to predict if a Tesla stock increases or not in the near future. However, how is this any better than simple eye inference? Would have been better to showcase an example where it predicts an inflexion in the stock, not a monothonical extrapolation (which anyone could do by naked eye). Or talk briefly on how the prohpet fit works, its limitations, etc.