What is wanted for Project 2

[C-vonk]

I was planning on doing a linear regression and doing a random sample of the mean Error and running it apromimate to find an approximate of the price each day does this work or do i need to do it like you did it in your example

from sklearn.metrics import mean_squared_error
AMZN_data = data[data['symbol'] == 'AMZN']
plt.plot(AMZN_data['date'], AMZN_data['open'])
AMZN_Pre_2015 = AMZN_data[ AMZN_data['date'] < pd.to_datetime('2015-01-01')]
plt.plot(AMZN_Pre_2015['date'], AMZN_Pre_2015['open'])
#plt.show()
diffprice = np.diff(AMZN_Pre_2015['open'])
#plt.plot(AMZN_Pre_2015['date'][1:], dprice)

STD_DIFF = diffprice.std()
Mean_diff = diffprice.mean()


date_nums = AMZN_Pre_2015['date'].astype('int64')
M, B = np.polyfit(date_nums,AMZN_Pre_2015['open'],1)
Lin = np.poly1d((M, B))
Est = Lin(date_nums)yplt.plot(AMZN_Pre_2015['date'],Lin(date_nums))

M_EST_Error = mean_squared_error(AMZN_Pre_2015['open'],Est)
error = np.sqrt(M_EST_Error)

[cooperrc]

Awesome! that sounds like a great addition to the analysis.

The main thing is that whatever model you create, you're trying to discuss how accurate and precise it is at predicting stock prices. Using this regression analysis is a way to discuss the average growth rate that these random steps will accumulate to.