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Thanks for this high-quality video. Can you give the impact of the splitting in the semantic of the summarization? Assuming that it's not intelligent tokenization you applied, batches of 850 tokens can overlap in terms of the meaning of the global doc.
when i run the code on jupyter lab it was working in the first couple tries, but now i keep getting an error early in the code. for some reason i get a index out of boudns error for the soup.select(table.stats_table) part of the code. it was working perfeclty before and showed all the links and eveyderhitng, and out of nowehre it stopped and i keep getting this error. Can anyoen explain why please? Thanks
I've always been interested in binary options, but I never knew where to start. Thanks to you and this video, I finally felt confident enough to give it a try.
Hi! I'm very admirative of you math champions, as the math dumb I am! Although, S&P500 is an index, and, unless I understand backwards, it is supposed to climb by nature since it is made of the best behaving shares of the market... I've tested one of my shares (ORA.PA), just to see if I'm not missing something. The result is very... depressing! Only 1/3 is successful (0: 184, 1:66) Then I wanted to test if the reciprocal had better chances (otherwise, it would mean tomorrow has no connection with today, and gambling would be as efficient, did I guess right?) I concluded I should mostly have a bearish strategy on that particular share (1: 154, 0:96) I want to explore (but it's just a guessing game, because my statistics math level is close to 0) and see if, as often said, the crossing of the MACD over its signal, especially under 0, is often a good buying signal, even if it's not really so frequent. Anyway, you've triggered my curiosity regarding machine learning towards shares. I'm sure, combined with some supervision and provided you can match a strategy with a corresponding share, chances are you improve your rentability and at least have a better feeling and confidence in your success. Thank you for that.
Just an FYI, in Spyder, teams.corr() didn't work because you have strings in there, so you have to remove the teams and country column for the method to work
I loved ur video it is so well-explained!! I only used scikit-learn but now I understand better how it's works. But I have a question: why is it not good no use height and wight to use as feature?
so ive just came across your channel a few days back and i just love it, yhe way you explain things and show without any edits, ets. cant wait to try your method becouse ive tried other methods from a experienced trader and lost my earning from that month (about 700 usd) i was so angry. but i said it is what it is and moved on, ill rather say o well then what if. THANK YOU SO MUCH!!!🏕
Thank you for the amazing video! However, when I tried running this, I received a value error teams.corr()["medals"] This seems to be because the "Team" and "Country" column are in string, and hence making it impossible to get a corr value. So i removed them just to obtain the corr values. But it seems to work for you without filtering the string type columns out. Any ideas why?
New subscriber here!!, thanks for your time. I'm working (surely wrong 😅) to find the correct model or algorithm to allocate a number of workers (+300) in a number of workstations. I've al the data ready, but not sure how to deal with rhe model or train this kind of data. It would be much appreciated if can point some tips for that. Every week I've to make the Next week plan and it's a lot of work and pain in the ass 😂
Oh NOO !!!! the market has been cracked by an Indian dude and is on youtube.....The Kings are trembling right now because they dont know where they will store their money...back to castle vaults guarded by Dragons....oh vikasparuchuri what have you done to the world with Dataquest.?????....now everyone is going to be rich.
Hi, during the parsing part, when I run the code till if len(games) % 100 == 0: print(f"{len(games)} / {len(box_scores)}"), it keeps telling me the error: html5lib not installed, even if I have installed it myself. Could you help me with it?
Never could get Chromium to work. I looked everywhere to find a solution for a very long time. So I ended up using Firefox as well. Does anyone have a solution to the chromium issue to direct to me. I really want to figure that out. Great job with the video! Very intuitive. Wish more content was on the regular.
This is very nice way to get started using data science with the markets. This gives a nice framework to get started. And attempt to expand the predictors (on RSI based or Change in Open Interest , some correlation with the major stocks composing that index) . Thank you for sharing.
It is poor quality content. The technology is obsolete and the methodology is laughable. I would be disappointed to see any such lesson taught in an undergraduate program.
One correction, not relevant to the actuall regression, but should be said nonetheless. The number of medals one athlete can win is not limitted to one, rather it is limited to the number of events the athlete competes in (maximum of one per event). In fact, numerous athletes have one multiple medals in one Olympics. Just wanted to clarify that. Of course, from a certain number of athelets, it will be impossible for a smaller team to compete in as many events as the large team, making it more likely that the larger team wins more medals.
If you went as far as to do it from scratch, then you might have just as well done it in anything other than Python. All that Python has in this field is libraries. Because it's not like Python has any advantage when it comes to linear algebra. If anything, it will be slower than most other things.
Thank you very much sir for this inspiring tutorial. Please I want to build a recommender system, "The aim of this study is to design and implement a Recommender System for clothing styles based on user body type derived from user body measurements." Please can you help with this kind of project or how can I go about it from getting the dataset to completion. Thank you
