Coding language Python – The data set is from Yahoo finance (Take the last five years in both ML models) – the rest of the needed libraries to be used in the project is Pandas, Scikit-Learn, Keras, and Tensorflow – Business problem or business question of the project is: predicting bitcoin prices with the least error to maximize returns and mitigate risk ——– There are two different ML model selections, and I am not sure which one to go with, and I would like to further discuss it with the selected tutor: The first two models are: – ARIMA – Use technical indicators features in linear regression ———- The second two models are: – ARIMA – Naive model using moving average with length one My price t-1 and say this my prediction (which will be today’s price) Without knowing the past data just use today’s price to predict tomorrow ———– What matters most here is to compare the results, analyze them, and highlight the areas where you could improve. Showcase where the prediction matches the real data and where it’s different. Backing it up with literature review of course (there will be an attached guidelines which has further details) ———– Put the code in appendix and save it in jupytr notebook and include hashtags where you explain what you did in each step save it as a pdf When writing the code in python make sure to add a hashtag before the code where you explain what are you doing at this step briefly for example: —-> # Download the Bitcoin price data from Yahoo Finance —–>start_date = ‘2016-04-05’ —–> end_date = ‘2023-04-05’ Merge the coding file with the report and save them both in one pdf if you wanted to mention the code in the report just write refer to page 2 in the appendix ——— I will attach the guidelines file as well.

Coding language Python

– The data set is from Yahoo finance (Take the last five years in both ML models)

– the rest of the needed libraries to be used in the project is Pandas, Scikit-Learn, Keras, and Tensorflow

– Business problem or business question of the project is: predicting bitcoin prices with the least error to maximize returns and mitigate risk

——–

There are two different ML model selections, and I am not sure which one to go with, and I would like to further discuss it with the selected tutor:

The first two models are:

– ARIMA

– Use technical indicators features in linear regression

———-

The second two models are:

– ARIMA

– Naive model using moving average with length one

My price t-1 and say this my prediction (which will be today’s price)

Without knowing the past data just use today’s price to predict tomorrow

———–

What matters most here is to compare the results, analyze them, and highlight the areas where you could improve. Showcase where the prediction matches the real data and where it’s different. Backing it up with literature review of course (there will be an attached guidelines which has further details)

———–

Put the code in appendix and save it in jupytr notebook and include hashtags where you explain what you did in each step save it as a pdf
When writing the code in python make sure to add a hashtag before the code where you explain what are you doing at this step briefly for example:
—-> # Download the Bitcoin price data from Yahoo Finance
—–>start_date = ‘2016-04-05’
—–> end_date = ‘2023-04-05’
Merge the coding file with the report and save them both in one pdf
if you wanted to mention the code in the report just write refer to page 2 in the appendix
———

I will attach the guidelines file as well.