Linear Regression Model Techniques with Python, NumPy, pandas and Seaborn

Hello Matt, first of all, thank you for your work. I have tried to reproduce this exercise and when executing the line

'data = pdr.get_data_yahoo(stocks, start)'

I get the following error message:

'TypeError: string indices must be integers, not 'str'.

I have tried to solve it in different ways, but nothing, it always gives me that message. I downloaded your code in case there was something written incorrectly, and it gives me the same error. Any suggestions?

import pandas_datareader as pdr is not working , what shoul di do

Thank you! Is this also good for predicting a range? Say 30 periods out?

Thank you for your work and the video!

what to do if the x-axis is time series ?

Very Nice, Hello Matt, in the section "trend as a technical indicateur", how to put the value of the trend line in a dataframe to detect later when it cross the curve ? Thank you. 👍👍

Very good Thank you.

Thank you for the video!! How would I add another more trend lines to subset set specific times or dates? I am trying to plot my X and Y for 9 am 10am 12pm and have multiple trend lines. Hope that makes sense, thank you in advance!

Thank you Matt. Would like to know what is different btw "np.log().diff()" and "pct_change().cumsum()", I've tried those two syntax then got a different result. Could you pls solve this question? Thanks again Matt.

i got two points

Hi there, firstly great video! How would it be possible to add another variable to the trend line? So essentially it would be multiple linear regression

Matt, if I wanted to take the first derivative of the linear regression function, how could I execute that? is there a certain package?

Thank you Matt. You explain things so well. I've watched several of your videos and intend to watch more.

Thank you sir for explaing the code in the detail

i get error: "ModuleNotFoundError: No module named 'pandas_datareader'"

Thank you for this. I was looking for a way to code a trend line. With many traders it becomes subjective. This makes it more precise.

Could you possibly provide a link to the data set you actually used in this tutorial? I would like to follow it locally on my PC as I go through this. Thank you.

Correlation != Causality !!!

Hi I am running into this error:

No data fetched using 'YahooDailyReader'