Hi, awesome teaching style. Much appreciated. I have 1 request, could you please share the Tutorial 8 - iris flower Logistic Regression multi - exercise code.
sir I find confusion matrix and score as 0.9333 when i took test size 0.1 but still i dont get that how to find y_test to feature name , i used data and target as x and y in t-t-s
hello sir what if we have Two labels i am stuck in one situation with x_train have 1527 columns and y_train have 2 column its giving a error of bad shape (1527, 2) how can i resolve this ?
X_train, X_test, y_train, y_test = train_test_split(flower.data,flower.target,test_size=0.2,random_state=10) with this code, i am getting a score of 1.0)
Sir I am getting error Whenever I am writting model.fit(x_train,y_train) It telling me to reshape your array using array.reshape (-1,1) After doing that also I am still getting error Sir plzzz plzzz plzzz plzzz help me fixing this....
When ever you are defining X and y define it in 2 dimensional array like y =[['bought_insurance ]] not in like y = df.bought_insurance or y = df['bought_insurance'].... NOTE: Applied to both the x and y
You didn't assign logistic Regression with model variable. you need to assign the Logistic Regression like this "model = Logistic_Regression()" and in the x_train the 'x' is capital like this "X_train"
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@@codebasicsHindi Can you start uploading video related to prompt Engineering
Superb explanation
This is second video m watching from your playlist
You are the perfect
Sir, Your teaching technique are mind blowing.
Can you please post the exercise solution so i can crosscheck it with my solution.
Hi, awesome teaching style. Much appreciated. I have 1 request, could you please share the Tutorial 8 - iris flower Logistic Regression multi - exercise code.
Very well explained thank you sir.
Sir when will you upload more video in this playlist?
Tutorial was fantastic, just the seaborn bit was a bit confusing
sir I find confusion matrix and score as 0.9333 when i took test size 0.1 but still i dont get that how to find y_test to feature name ,
i used data and target as x and y in t-t-s
ValueError: y should be a 1d array, got an array of shape (120, 4) instead.
how to solve this issue?
Got 98% Accuracy
Super!!!!
🙏👍
hello sir what if we have Two labels i am stuck in one situation with x_train have 1527 columns and y_train have 2 column its giving a error of bad shape (1527, 2)
how can i resolve this ?
Share with me your code, I'll check it out and let you know. mbukhari@pakaims.edu.pk
X_train, X_test, y_train, y_test = train_test_split(flower.data,flower.target,test_size=0.2,random_state=10)
with this code, i am getting a score of 1.0)
Sir I am getting error
Whenever I am writting
model.fit(x_train,y_train)
It telling me to reshape your array using array.reshape (-1,1)
After doing that also I am still getting error
Sir plzzz plzzz plzzz plzzz help me fixing this....
model.predict(X_test)
@@yum-yum6431 the problem is with the fitting
Than I think your x and y must be in array format try to format it in data frame.
@@yum-yum6431 how to do that can you tell me
I always face problems in fitting the training data in all machine learning algorithms.
When ever you are defining X and y define it in 2 dimensional array like y =[['bought_insurance ]] not in like y = df.bought_insurance or y = df['bought_insurance'].... NOTE: Applied to both the x and y
Got 93% accuracy!
great job
len(digits.data)
1797
model.fit(x_train,y_train)
NameError: name 'model' is not defined
why i am having this error?
You didn't assign logistic Regression with model variable.
you need to assign the Logistic Regression like this "model = Logistic_Regression()"
and in the x_train the 'x' is capital like this "X_train"
93% accuracy
score is (95.72222222222221, 0.5, 'Truth')
93% accuracy with 0.2 and 99% accuracy with 0.4
I got 100 % accuracy in iris data set
Yeaps, Although too much high accuracy will be challenged, so we have to be moderated like 90%, 94.25% etc.
I got the model score 0.9736842105263158 using Iris Dataset
93% Accuracy
That’s the way to go Mahmudul, good job working on that exercise
but logistic regression hi kyu use karna hai? 🙄
for classification
my result is 0.977777 Accuracy
Arre bhai, Democrats, republicans became Bjp, Congress hahahahahah
got 0.9666666666666667 accuraccy in excersise