If you follow this tutorial you have a great portfolio project for your resume: It shows working with real world data and putting it into a ML web app that visualizes the results. Hope you enjoy it :)
Hello Patrick, you are treally amazing.. i do have faculty project that i cant solve. that is face recognition using CNN's, and integrating it into django web app,, i am ''junior'' so it is really big step for me.. so if you could help with anything it would be great.. Ty kind sir :)
I am unable to solve an error in the 78th code box of your jupytor notebook which gives me the error:- first line:-"y contains previously unseen labels: 'United States" " 2nd line:- " y contains previously unseen labels: '2' " . I am unable to find a solution to this error, it would be of great help if anyone could guide me. THANKS😊
@patloeber hello sir I need to ask u some thing in the 10th cell what is the use of filtering the Employment column if ur gonna drop it in the next line it self 🤔 12:36
This is exactly what you are looking for if you are a junior data scientist who just started a web app project. Incredibly simple and yet complex intro.
@@patloeber hello sir I need to ask u some thing in the 10th cell what is the use of filtering the Employment column if ur gonna drop it in the next line it self 🤔 12:36
@@patloeber hello sir I need to ask u some thing in the 10th cell what is the use of filtering the Employment column if ur gonna drop it in the next line it self 🤔 12:36
As I said previously I was probably within your first 1k subscribers and man I just love watching you go at these in the zone! Great content relevant no fluff just keep going love it
This is awesome. Could you create a tutorial on how to build a web app which can identify an image deployed on google cloud. One of the best ML channels. Pure Gold.
Omg thanks you soo soo so much, because I was in college try Harding to understand the concept of machine learning. Got to learn so much. Like the concept of : data cleansing, data training fitting, how to use trained AI to make prediction. Your explanation is just mind blowing. I did this project and on top added my own touch which I would've never done if were to just to try to learn from College alone.
Thanks Patrick! Was a lovely tutorial and guessing the frame can be used to design other linear regression apps easily (and other models with a little bit of work).
Hi! Shouldn't the label encoder transformers need to be used only in the target variables? But in this video you are using it for the input variables too. Correct me if I'm wrong.
I have three categorical data types and my confusion is on how to implement them given that this section of the code has just a two-dimensional array. ok = st.button("Calculate Salary") if ok: X = np.array([[country, education, expericence ]]) X[:, 0] = le_country.transform(X[:,0]) X[:, 1] = le_education.transform(X[:,1]) X = X.astype(float)
This is related to the `LabelEncoder`: # Your DataFrame processing should look like this: le_education = LabelEncoder() df['EdLevel'] = le_education.fit_transform(df['EdLevel']) le_country = LabelEncoder() df['Country'] = le_country.fit_transform(df['Country']) # Prepare your feature matrix `X` and target vector `y` X = df.drop("Salary", axis=1) y = df["Salary"] Then you can go ahead and follow him at 21:42. Quick reminder that I am using a dataset from 2023, so in my Decision Tree Regressor, I needed to change the "United States of America" it is like: # Correct the country and education level based on how they were encoded new_data = [["United States of America", "Master’s degree", 15]] # Create a DataFrame for the new data point for easy manipulation new_data_df = pd.DataFrame(new_data, columns=["Country", "EdLevel", "YearsCodePro"]) # Apply the trained LabelEncoders to the new data new_data_df['Country'] = le_country.transform(new_data_df['Country']) new_data_df['EdLevel'] = le_education.transform(new_data_df['EdLevel']) # Ensure all data is in the correct numeric format new_data_df = new_data_df.astype(float) I hope it helps.
Really thanks for this project , i have question please , on sidebar explore and predict the visualizations appeared on both of them . How can i solve that .
This is a helpful tutorial. Thank you. Can you also make a video on Interactions between different charts using streamlit? This will be useful to many(like me) I guess👍🏻
You prolly dont care but if you are bored like me atm then you can watch all of the latest movies on InstaFlixxer. Have been watching with my gf during the lockdown :)
When I recreated this app, when I predict based on Bachelor’s degree, the salary predicts well. But when I predict based on "Post grad " select, the salary prediction is not accurate. On zero experience, i get a a higher salary than when I have one year experience. This also applicable to the other selections. Only Bachelor’s degree select predicts well. Is this as a result of the outlier?
Thanks sir. Can you share the video how to published this kind of local data into github/ webapps that can be use by anyone in the world as apps/ web gui.
I am trying to deploy on streamlit cloud but throwing some weird error I guess I am using wrong versions in requirements.txt.Kindly let me know which version to be installed
whenever i try to import streamlit as st it always shows error stating "import streamlit could not be resolvedpylancereportmissingimports" any idea what the issue can be? should i open vs code from the comman prompt that im using conda for ?
Great video! Can you explain from where you saved the pickle file and deployed the model? I am trying with 14 variables but I dont want to make prediction using all of them. How would that be possible?
yeah it's not optimal, you can try different features, feature selection methods, normalization etc...and of course different models and optimize the hyperparameters
Error: X[:, 0] = le_country.transform(X[:,0]) X[:, 1] = le_education.transform(X[:,1]) X = X.astype(int) X ValueError: y contains previously unseen labels: 'United States' Solution: change 'United States' to 'United States of America', seem like the CSV dataset updated.
This is related to the `LabelEncoder`: # Your DataFrame processing should look like this: le_education = LabelEncoder() df['EdLevel'] = le_education.fit_transform(df['EdLevel']) le_country = LabelEncoder() df['Country'] = le_country.fit_transform(df['Country']) # Prepare your feature matrix `X` and target vector `y` X = df.drop("Salary", axis=1) y = df["Salary"] Then you can go ahead and follow him at 21:42. Quick reminder that I am using a dataset from 2023, so in my Decision Tree Regressor, I needed to change the "United States of America" it is like: # Correct the country and education level based on how they were encoded new_data = [["United States of America", "Master’s degree", 15]] # Create a DataFrame for the new data point for easy manipulation new_data_df = pd.DataFrame(new_data, columns=["Country", "EdLevel", "YearsCodePro"]) # Apply the trained LabelEncoders to the new data new_data_df['Country'] = le_country.transform(new_data_df['Country']) new_data_df['EdLevel'] = le_education.transform(new_data_df['EdLevel']) # Ensure all data is in the correct numeric format new_data_df = new_data_df.astype(float) I hope it helps.
If you follow this tutorial you have a great portfolio project for your resume: It shows working with real world data and putting it into a ML web app that visualizes the results. Hope you enjoy it :)
Hello Patrick, you are treally amazing.. i do have faculty project that i cant solve. that is face recognition using CNN's, and integrating it into django web app,, i am ''junior'' so it is really big step for me.. so if you could help with anything it would be great.. Ty kind sir :)
I am unable to solve an error in the 78th code box of your jupytor notebook which gives me the error:- first line:-"y contains previously unseen labels: 'United States" "
2nd line:- " y contains previously unseen labels: '2' " . I am unable to find a solution to this error, it would be of great help if anyone could guide me. THANKS😊
@@prajwalbagchi3965 check the label must be changed to 'United States of America'... and for checking write this code df["Country'].unique()
@patloeber hello sir I need to ask u some thing in the 10th cell what is the use of filtering the Employment column if ur gonna drop it in the next line it self 🤔 12:36
@Saffron_SV Thank you for this, I was also stuck here for days
This is exactly what you are looking for if you are a junior data scientist who just started a web app project. Incredibly simple and yet complex intro.
This was fantastic! I built my first Python web app using exclusively this tutorial. Thank you, Python Engineer.
Great to hear!
hey what should I include in .pkl file?
@@patloeber Is this annual salary?
@@patloeber hello sir I need to ask u some thing in the 10th cell what is the use of filtering the Employment column if ur gonna drop it in the next line it self 🤔 12:36
@@patloeber hello sir I need to ask u some thing in the 10th cell what is the use of filtering the Employment column if ur gonna drop it in the next line it self 🤔 12:36
As I said previously I was probably within your first 1k subscribers and man I just love watching you go at these in the zone! Great content relevant no fluff just keep going love it
This is awesome. Could you create a tutorial on how to build a web app which can identify an image deployed on google cloud. One of the best ML channels. Pure Gold.
Thanks! Watch my latest tutorial, it's about Google Cloud deployment ;)
I'll come to this, your teaching made me think about starting to learn streamlit
Excellent video, thank you so much for sharing this!!!
What a great tutorial! Thanks for sharing this!!
@Patrick Loeber Hola!!! :) greetings from Argentina, excellent video and explanation, please continue doing them
Omg thanks you soo soo so much, because I was in college try Harding to understand the concept of machine learning. Got to learn so much. Like the concept of : data cleansing, data training fitting, how to use trained AI to make prediction. Your explanation is just mind blowing. I did this project and on top added my own touch which I would've never done if were to just to try to learn from College alone.
What a maestro! Keep your great work, man. Really appreciate this high quality stuff!
Thanks man , waiting for more
Thank you,this project actually works.Much appreciationn😇
Niceee... web dev with ML
Yep great combination :) Maybe one day I'll make a Django+ML project
@@patloeber that'll be great!
Good explanation. Thanks for sharing
Amazing video bro!!!! Thanks so much for sharing your knowledge
thanks a lot :)
i really enjoy the tutorial, thanks sir.
Thanks Patrick! Was a lovely tutorial and guessing the frame can be used to design other linear regression apps easily (and other models with a little bit of work).
This was amazing!! Thanks for this tutorial, loved it
No such file or directory: 'saved_steps.pkl' please help
Hi! Shouldn't the label encoder transformers need to be used only in the target variables? But in this video you are using it for the input variables too. Correct me if I'm wrong.
Thanks, this was very helpful!
can you tell me why this is incorrect ??
X[:, 0] = le_country.transform(X[:,0])
X[:, 1] = le_education.transform(X[:,1])
X = X.astype(float)
X
It's nice to see you in this video bro......nice video too..bro..keep going 🔥🔥🔥
thanks :)
Thanks a lot it was detailed enough to help!
WOOOOOOOOOOOWWWWWWWWWWWWWWWWWWWWWW!!!!!!!!
YOU ARE GREAT! STREAMLIT ALSO AWESOME! IT MAKES EASIER TO LIVE :D
Awesome explanation.
This is amazing! Thanks for sharing
Cool. Added to my watchlist.
Thank You 🙌
You Are My Hero 💖
Great work
this is awesome! Thank you
I have three categorical data types and my confusion is on how to implement them given that this section of the code has just a two-dimensional array.
ok = st.button("Calculate Salary")
if ok:
X = np.array([[country, education, expericence ]])
X[:, 0] = le_country.transform(X[:,0])
X[:, 1] = le_education.transform(X[:,1])
X = X.astype(float)
This is related to the `LabelEncoder`:
# Your DataFrame processing should look like this:
le_education = LabelEncoder()
df['EdLevel'] = le_education.fit_transform(df['EdLevel'])
le_country = LabelEncoder()
df['Country'] = le_country.fit_transform(df['Country'])
# Prepare your feature matrix `X` and target vector `y`
X = df.drop("Salary", axis=1)
y = df["Salary"]
Then you can go ahead and follow him at 21:42. Quick reminder that I am using a dataset from 2023, so in my Decision Tree Regressor, I needed to change the "United States of America" it is like:
# Correct the country and education level based on how they were encoded
new_data = [["United States of America", "Master’s degree", 15]]
# Create a DataFrame for the new data point for easy manipulation
new_data_df = pd.DataFrame(new_data, columns=["Country", "EdLevel", "YearsCodePro"])
# Apply the trained LabelEncoders to the new data
new_data_df['Country'] = le_country.transform(new_data_df['Country'])
new_data_df['EdLevel'] = le_education.transform(new_data_df['EdLevel'])
# Ensure all data is in the correct numeric format
new_data_df = new_data_df.astype(float)
I hope it helps.
such a good video. Thanks for sharing bro
Nice can you also do interaction of FAST API with streamlit for this ML problem. Thank you
Yeah FastAPI is on my list
I second that. 😀
Nice
How we can calculate accuracy of above project??
Really awesome tutorial
Thanks for this one, it helped a lot
Hello
have you made this project ??
Yep
What an awesome tutorial.
Great tut and great accent
Really thanks for this project , i have question please , on sidebar explore and predict the visualizations appeared on both of them .
How can i solve that .
from sklearn import linear_model
linear_reg = linear_model.LinearRegression()
Awesome content 🙂👍
thank you Patrick
Great tutorial, however in which step shall I add a streaming source datasets instead of the static one which you have presented here ? Best
Excellent video!!!
Glad you liked it!
@patloeber, what if do the label encoding in for loop ,what should i do after importing the model
This is a helpful tutorial. Thank you. Can you also make a video on Interactions between different charts using streamlit? This will be useful to many(like me) I guess👍🏻
i'll have a look at it
You prolly dont care but if you are bored like me atm then you can watch all of the latest movies on InstaFlixxer. Have been watching with my gf during the lockdown :)
@Jax Samir yea, been using instaflixxer for since november myself =)
which type of dataset is this? supervised or non - supervised or semi supervised?
Huge perfect and get new skill . Thank you.
glad you like it
Great material! Would you mind making a future video on other data preprocessing/cleaning techniques? Would be great to systematize an approach.
yeah I want to do this in the future
Awesome
Can you please make more videos on it. It's a request.
thanks for the feedback! I try :)
can you show how you deployed it coz i am having issue while deploying it
Even after using terminal to install scikit learn jupyter does not detect it and says no module named sklearn?
When I recreated this app, when I predict based on Bachelor’s degree, the salary predicts well. But when I predict based on "Post grad " select, the salary prediction is not accurate. On zero experience, i get a a higher salary than when I have one year experience. This also applicable to the other selections. Only Bachelor’s degree select predicts well. Is this as a result of the outlier?
Thanks sir. Can you share the video how to published this kind of local data into github/ webapps that can be use by anyone in the world as apps/ web gui.
THANK YOU SO MUCH SIR!
Great Video
Hello Sir, I had face a problem during practice regressor problem. I don't understand how to solve a problem ! (regressor is not defined)
Why you used label encoder for country column ?. I think we have to use one hot encoder because it is nominal data.
Great! Looking for more like this. Thanks.
Glad you liked it!
you used label encoder and gave that value, but what if we did one hot encoding, then how will we implement that in streamlit ?
Hey @Patrick Loeber can you provide the dataset of this project???
How do we change the styling and structure of the web application as in HTML and CSS?
why not build pipeline for automating data preparation for prediction
This is amazing!!
thank you!
awesome as always :-)
Thanks :)
I like the way you set up the iterm2. Can you make a video how you set up that?
I pretty much followed this article: opensource.com/article/20/8/iterm2-zsh
A truly masterpeace
I am trying to deploy on streamlit cloud but throwing some weird error I guess I am using wrong versions in requirements.txt.Kindly let me know which version to be installed
whenever i try to import streamlit as st it always shows error stating "import streamlit could not be resolvedpylancereportmissingimports" any idea what the issue can be? should i open vs code from the comman prompt that im using conda for ?
sir u haven't given the pkl file?
Great video! Can you explain from where you saved the pickle file and deployed the model? I am trying with 14 variables but I dont want to make prediction using all of them. How would that be possible?
Awesome !
can we build a ML project using jupyter Notebook? if yes how can i deploye on web app?
Can we consider this prediction as Real Time prediction?
You're a Gem !
Thanks :)
I can't understand one thing, how did you select those columns without any correlation?
KING 👑
Nice video, thanks :)
Glad you like it
Really good project.
Glad you like it
i keep getting an error implementing the pickle library using all you used in this tutorial
How do we deploy the data so we can share it publicly with everybody
No such file or directory: 'saved_steps.pkl' please help
Thank you
How to use machine learning model (pickle / jooblib ) and make Graphs and log in /register and house price Prediction in django
We want more videos like this..plzzz
sure :)
@@patloeber thank u sir🙏
Thank you very much. I learnt a lot following this tutorial. You are amazing.
glad it was helpful!
PERFECT!
Great material! What could be done to achieve a lower error ? That one obtained seems too big, don’t you think?
yeah it's not optimal, you can try different features, feature selection methods, normalization etc...and of course different models and optimize the hyperparameters
Can you make a video on how to make front end using react instead of streamlit . Thanks
How well will this scale?
Awesome.
magnificent!
I ran into a problem. After creating the drop-down, whenever I select a list, the whole entire thing disappears.
where we collect the data set
can you tell me which machine learning algorithm is used in this code
LinearRegression and RandomForestRegressor
@@patloeber thank you so much can u tell me specifically where each is used
data = pickle.load(file)
_pickle.UnpicklingError: invalid load key, '?'.
can anyone help me out with this error?
is it possible to send the link someone? or it's working only local?
Thank you so much can you do fingerprint matching web ?
Will have a look at this
@@patloeber thank you so much
how to create a conda environment?
Error:
X[:, 0] = le_country.transform(X[:,0])
X[:, 1] = le_education.transform(X[:,1])
X = X.astype(int)
X
ValueError: y contains previously unseen labels: 'United States'
Solution: change 'United States' to 'United States of America', seem like the CSV dataset updated.
it keeps telling me " name 'le_country' is not defined "
plz help
This is related to the `LabelEncoder`:
# Your DataFrame processing should look like this:
le_education = LabelEncoder()
df['EdLevel'] = le_education.fit_transform(df['EdLevel'])
le_country = LabelEncoder()
df['Country'] = le_country.fit_transform(df['Country'])
# Prepare your feature matrix `X` and target vector `y`
X = df.drop("Salary", axis=1)
y = df["Salary"]
Then you can go ahead and follow him at 21:42. Quick reminder that I am using a dataset from 2023, so in my Decision Tree Regressor, I needed to change the "United States of America" it is like:
# Correct the country and education level based on how they were encoded
new_data = [["United States of America", "Master’s degree", 15]]
# Create a DataFrame for the new data point for easy manipulation
new_data_df = pd.DataFrame(new_data, columns=["Country", "EdLevel", "YearsCodePro"])
# Apply the trained LabelEncoders to the new data
new_data_df['Country'] = le_country.transform(new_data_df['Country'])
new_data_df['EdLevel'] = le_education.transform(new_data_df['EdLevel'])
# Ensure all data is in the correct numeric format
new_data_df = new_data_df.astype(float)
I hope it helps.
hello please i keep getting error when i load streamlit. Please help me i neede it urgently