NameError: name 'df' is not defined <--- Keeps showing up when i reach 17 mins on the tutorial. my file name matches my code folder and chatgpt isnt very helpful. can someone pleaseee help

Thank you!

Great video great explanations and examples

Hi,I think the 28:00 question should concat the shipment Id and subid ?!😮

thanks a lot for this project, learnt great knowledge (especially about stacked regressors and voting regressors) and how to filter out outliers and fill na values using description.txt and a little worldly experience/knowledge.

it fits for cheat list ❤

so what if the lasso drop two features who can you tell which features it dropeed when you zip teh freatures and coeef back together???

as someone who is new to AI/ML, maybe some more clear terminology defined would be helpful. A lot of resources call what you describe as 'Normalizing' as 'Scaling'. And what you call 'standardization' is referred to as 'Normalizing'. Just a little confusing but great video actually showing the difference between the 2.

Thanks Ryan, great tutorial. I was pleasantly surprised that you knew the name of the great WI batsman, Sir Garry Sobers. Are you from the West Indies ?

Came across this vid as a new python learner and tried to attempt it before watching. At first I spent far too long trying to manually create a decimal to binary converter, then finally found out about bin() and ended up with something fairly similar to your code, with a list of valid addends and a list of addends that sum to N. Then I realised that the minimum number of addends was simply the value of the largest digit in N, so if N=13254, the minimum number of addends was 5. The final function I came up with basically finds each addend by replacing the non-zero digits of (N - all previous addends). Although there's probably still room for improvement, my final code was: addends = [] N = int(input("Type a positive integer N: ")) while N > 0: next_addend = "" for digit in str(N): if digit != "0": next_addend += "1" else: next_addend += "0" addends.append(next_addend) N = N - int(next_addend) for _ in addends: print(_, end=" ") print(f" Output = {len(addends)}")

Thanks buddy

What ide are you using sir

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Hey guys I hope you enjoyed the video! If you did please subscribe to the channel! Join our Data Science Discord Here: discord.com/invite/F7dxbvHUhg If you want to watch a full course on Statistics check out Datacamp: datacamp.pxf.io/XYD7Qg Want to solve Python data interview questions: stratascratch.com/?via=ryan I'm also open to freelance data projects. Hit me up at ryannolandata@gmail.com *Both Datacamp and Stratascratch are affiliate links.

thank you👍

Hi, I can't find the simple_imputer.csv file. where can I download it? Thank you

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quick question: does Kaggle give you a rating based on speed/efficiency? I'm wondering specifically about just importing the whole libraries.

Thank you!!

Where is the data????

Please make sure all cells are visible on screen. Sometimes not able to view end of cell content.

how to plot the linear regression for multidimentional data?

Hey guys I hope you enjoyed the video! If you did please subscribe to the channel! Join our Data Science Discord Here: discord.com/invite/F7dxbvHUhg If you want to watch a full course on Statistics check out Datacamp: datacamp.pxf.io/XYD7Qg Want to solve Python data interview questions: stratascratch.com/?via=ryan I'm also open to freelance data projects. Hit me up at ryannolandata@gmail.com *Both Datacamp and Stratascratch are affiliate links.

Hey guys I hope you enjoyed the video! If you did please subscribe to the channel! Join our Data Science Discord Here: discord.com/invite/F7dxbvHUhg If you want to watch a full course on Statistics check out Datacamp: datacamp.pxf.io/XYD7Qg Want to solve Python data interview questions: stratascratch.com/?via=ryan I'm also open to freelance data projects. Hit me up at ryannolandata@gmail.com *Both Datacamp and Stratascratch are affiliate links.

Hey guys I hope you enjoyed the video! If you did please subscribe to the channel! Join our Data Science Discord Here: discord.com/invite/F7dxbvHUhg If you want to watch a full course on Statistics check out Datacamp: datacamp.pxf.io/XYD7Qg Want to solve Python data interview questions: stratascratch.com/?via=ryan I'm also open to freelance data projects. Hit me up at ryannolandata@gmail.com *Both Datacamp and Stratascratch are affiliate links.

Hey guys I hope you enjoyed the video! If you did please subscribe to the channel! Join our Data Science Discord Here: discord.com/invite/F7dxbvHUhg If you want to watch a full course on Statistics check out Datacamp: datacamp.pxf.io/XYD7Qg Want to solve Python data interview questions: stratascratch.com/?via=ryan I'm also open to freelance data projects. Hit me up at ryannolandata@gmail.com *Both Datacamp and Stratascratch are affiliate links.

Hey guys I hope you enjoyed the video! If you did please subscribe to the channel! Join our Data Science Discord Here: discord.com/invite/F7dxbvHUhg If you want to watch a full course on AI check out Datacamp: datacamp.pxf.io/XYD7Qg Want to solve Python data interview questions: stratascratch.com/?via=ryan I'm also open to freelance data projects. Hit me up at ryannolandata@gmail.com *Both Datacamp and Stratascratch are affiliate links.

very good explanation keep continue

New subscriber here! Thank you for your good work. Just a quick question. To extract events held in USA, since we know we are looking for the 3 letters between the 5th last and last as USA, couldn't we use this condition: (df['Event name'].str[-4:-1]=='USA')? I used it but my dataframe returns 26524 rows which I thought might be due to difference in the version of dataset. I also tried (df['Event name'].str.endswith("(USA)")) and got the same number of rows.

at 3:01 why y=df["HOF"]?

If the str and get methods does not work for you too use this "(df['Event name'].str.extract(r'\((.*?)\)') == 'USA')"

import pandas as pd def sales_person(sales_person: pd.DataFrame, company: pd.DataFrame, orders: pd.DataFrame) -> pd.DataFrame: company=company.rename(columns={"name":"colour"}) df=orders.merge(company, on="com_id").merge(sales_person, on="sales_id") res=list(set(sales_person.name)-set(df[df.colour=="RED"].name)) return pd.DataFrame({'name':res}) This is a simpler solution for pandas any thoughts on this ?

Cool!!!

Hi, what does importance mean? with feature importance

Text editor?

what is difference between scaler.fit() and scaler.fit_transform() ⁉

It took me a while to find this video you should add that this is for multi-categorical features too, thanks for sharing this great video!!!

nice

Thank you very much.

image = item[1:].values.reshape[28, 28].astype(np.uint8) TypeError: 'builtin_function_or_method' object is not subscriptable

You're unreal man!!! Thanks!!!

so it is similar to pd.getdummies() 🤔

Hey guys I hope you enjoyed the video! If you did please subscribe to the channel! Join our Data Science Discord Here: discord.com/invite/F7dxbvHUhg If you want to watch a full course on Statistics check out Datacamp: datacamp.pxf.io/XYD7Qg Want to solve Python data interview questions: stratascratch.com/?via=ryan I'm also open to freelance data projects. Hit me up at ryannolandata@gmail.com *Both Datacamp and Stratascratch are affiliate links.

We need more and more

thank you very much 💕

Great work

nice bro, great explanation 👍

It seems the key is extracting meaningful insights from the data. People tend to have better chances of survival when they can influence others, whether through fame, wealth, or emotional appeal. Relationships play a crucial role-having family, business connections, or friends can make a difference. For example, mothers with young children might also be at an advantage. Understanding social networks and assessing the relative importance of each connection could be a valuable strategy. One could enhance the analysis by researching each passenger individually, looking into their background and determining their social standing or wealth. Resources like "Who’s Who" provide details on annual income and social status. However, is this a valid method, or does it border on cheating? GENERAL COMPETITION RULES C. External Data. You may use data other than the Competition Data (“External Data”) to develop and test your Submissions. However, you will ensure the External Data is publicly available and equally accessible to use by all participants of the Competition for purposes of the competition at no cost to the other participants. The ability to use External Data under this Section 7.C (External Data) does not limit your other obligations under these Competition Rules, including but not limited to Section 11 (Winners Obligations).

it was a very good training. Thank you for making this video. I have implemented the project myself and I am even thinking about moving forward.

Hello Ryan, thanks for making LLM contents, The video was good! I request you to attach the notebook of each module please, I searched on your git & blogs, I couldn't find it! 🥲