You guys are really amazing, very soon your channel will get millions of like and subscribe for sure... :) Just one thing to say that, put direct link of your suggested video instead of complete series., for ex., in your videos if u r suggesting any particular topic video to watch then put link directly to that specific video instead of giving link for entire series where that video has uploaded... thanks ... keep it up, going great...
Your teaching level is very good.... Till now I didn't have any doubt but here is one doubt ....what is the difference between fit and transform method in polynomial features
thanks for great help sir. But i want to ask a question, you have applied this polynomial transformation on all the features without visualizing. So, Can we apply this polynomial regression on some features and linear regression on some features after visualize the whole data? is this possible, or will get best accuracy? Waiting for your response. Thanks
15:05 SIr, I got an error: When I train our polynomial regression :( ValueError Traceback (most recent call last) in () 1 lr = LinearRegression() 2 ----> 3 lr.fit(X_train_poly, y_train, sample_weight=None) 2 frames /usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator) 547 "Reshape your data either using array.reshape(-1, 1) if " 548 "your data has a single feature or array.reshape(1, -1) " --> 549 "if it contains a single sample.".format(array)) 550 # If input is 1D raise error 551 if array.ndim == 1: ValueError: Expected 2D array, got scalar array instead: array=PolynomialFeatures(degree=2, include_bias=True, interaction_only=False, order='C'). Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
X_train ke liye ham polynomial ko fit karne ke baad transform karte hai vo samaj aaya, lekin ham X_test ko kyu transform karte hai? usko to hamne fit bhi nahi kiya hai fir bhi....
Bhai phele aache se review kar liya kar apne content yaar ! Kitna galat batata hai logo ko ! In polynomial regression jaha pe tu explain kar raha hai Polynomial Regression ki equation ussme Y = Beta(0) + Beta(1)*X(1)^1 + Beta(2)*X(2)^2 + ... + Beta(n)* X(n)^(power degree =1,2,3,...) Bhai puri equation hi galat baata raha learners ko matlab haad hai yaar atleast phele khud sheekh lo aache then kisi aur ko sheekhao aache se !
And Khudh ne bola hai ki agar ek hi feature ho toh woh Simple linear regression hoga and then khhud hi galat baata raha hai in Polynomial regression me ussme tune ek hi feature liya hai sirf X1 ! 🙄🙄🙄 Bhai peeke baanate ho kya videos ?
Thank you for watching this video but Arpit I have explained this formula for nth degree for x1 feature Y = Beta(0) + Beta(1)*X(1)^1 + Beta(2)*X(1)^2 + ... + Beta(n)* X(1)^(n) not this Y = Beta(0) + Beta(1)*X(1)^1 + Beta(2)*X(2)^2 + ... + Beta(n)* X(n)^(power degree =1,2,3,...) Once again thanks for sharing your feedback keep learning.
Amazing sir, one of d bestest channel to learn ML in RUclips
You guys are really amazing, very soon your channel will get millions of like and subscribe for sure... :)
Just one thing to say that, put direct link of your suggested video instead of complete series.,
for ex., in your videos if u r suggesting any particular topic video to watch then put link directly to that specific video instead of giving link for entire series where that video has uploaded...
thanks ... keep it up, going great...
you explain everything perfectly sir .........Great job.....
Very good playlist for ML
Great Work, Great Video !!
great work, keep doing work bro
u r boon for people like us, plzz come with more projects
Great video sir
You are best
Best teaching Sir
Thanks 🙏 Abdul
You explain really well !
Very nice tutorial
Superrrrbbb💯
Your teaching level is very good.... Till now I didn't have any doubt but here is one doubt ....what is the difference between fit and transform method in polynomial features
I have explained in video
Very good sir
Thank you sir 🙏🏼❣️
thanks for great help sir. But i want to ask a question, you have applied this polynomial transformation on all the features without visualizing. So, Can we apply this polynomial regression on some features and linear regression on some features after visualize the whole data? is this possible, or will get best accuracy? Waiting for your response. Thanks
ace tutorial
thnks alot
sir pura aapki tarah hi notebook kiya hai par
but mse and rmse value alag aa rahi hai kyu ???
mse rmse
52 7
8 2
Please check again
wahh maja aa gya
Where is next part of this video...plz share link
How did we visualize it.
bro you missed Logistic regression in your tutorial series, please cover that topic too
Sure, will do
Hi Please do one complete project starting from web scrapping, data cleansing and model building and deployment from Kaggle.
Please go through channel there you will get end to end projects
where can I get the datasets?
Please go through description box
Sir , please share market ki report automatic download kese kare
sir dont we have curse of dimensionality here because of too many features , also tell me how to check overfitting /underfiting ?
Do cross validation and test your model on testing data
@@IndianAIProduction okay
15:05
SIr, I got an error: When I train our polynomial regression :(
ValueError Traceback (most recent call last)
in ()
1 lr = LinearRegression()
2
----> 3 lr.fit(X_train_poly, y_train, sample_weight=None)
2 frames
/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
547 "Reshape your data either using array.reshape(-1, 1) if "
548 "your data has a single feature or array.reshape(1, -1) "
--> 549 "if it contains a single sample.".format(array))
550 # If input is 1D raise error
551 if array.ndim == 1:
ValueError: Expected 2D array, got scalar array instead:
array=PolynomialFeatures(degree=2, include_bias=True, interaction_only=False,
order='C').
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
same error if you find any soln of this then please tell me
sir.images pe kaise kaam.karega please image related video send kro na sir please
Please watch other videos from channel page
I think he had trained model on complete data, ie including test data also. Otherwise simply you can't get such accuracy
How can we know number of hidden layers used in this model and number of neuron?
I got 100% accuracy
MSE our RMSC 0. ke pass hoga tab best hoga na ye tho error jyada bata raha hai
0 ke paas hua toh worse model hota hai
In the case of overfiting, we always get very low training error and very high test error. We can not get 100% accuracy in the case of overfiting.
Wright
@@IndianAIProductionSorry ..... I have edited.
sir how to convert range value into single mean value of both????
ex. 1120-1160 into 1140
i need to change the whole column
Yes mean value of the both or even column too
X_train ke liye ham polynomial ko fit karne ke baad transform karte hai vo samaj aaya, lekin ham X_test ko kyu transform karte hai? usko to hamne fit bhi nahi kiya hai fir bhi....
sir data set available nahi ha
not found aa raha ha
Go through description box
Sir Mera mse = 14.026379 and rmse = 3.74518077 ahara h toh apka jaisa same answer nhi ahaya h
Alag alag data points use hue hai aap dono ke training testing sets mein isliyeq
Nice sir😂😂😂😂
Please sir upload a video in datapolynomials linear regression used this data how to convert category data to numerical ...
Please watch data preprocessing tutorial link in description box
Sir Unsupervised and Reinforcement Learning ke videos ke link please.
Will make series on it in future
hello sir ,I got R^2 value=0 .9086932170235822. what does that mean? is it good or bad
Good model
Bhai phele aache se review kar liya kar apne content yaar ! Kitna galat batata hai logo ko ! In polynomial regression jaha pe tu explain kar raha hai Polynomial Regression ki equation ussme Y = Beta(0) + Beta(1)*X(1)^1 + Beta(2)*X(2)^2 + ... + Beta(n)* X(n)^(power degree =1,2,3,...) Bhai puri equation hi galat baata raha learners ko matlab haad hai yaar atleast phele khud sheekh lo aache then kisi aur ko sheekhao aache se !
And Khudh ne bola hai ki agar ek hi feature ho toh woh Simple linear regression hoga and then khhud hi galat baata raha hai in Polynomial regression me ussme tune ek hi feature liya hai sirf X1 ! 🙄🙄🙄 Bhai peeke baanate ho kya videos ?
Thank you for watching this video but Arpit I have explained this formula for nth degree for x1 feature Y = Beta(0) + Beta(1)*X(1)^1 + Beta(2)*X(1)^2 + ... + Beta(n)* X(1)^(n) not this Y = Beta(0) + Beta(1)*X(1)^1 + Beta(2)*X(2)^2 + ... + Beta(n)* X(n)^(power degree =1,2,3,...)
Once again thanks for sharing your feedback keep learning.
@@IndianAIProduction keep going !