Very helpful !! But i want to asky you how to deploy such code into arduino nano 33 ble sense board? And what code should be written for inference in arduino ide? Did yoi explained that in a video??
The Gini coefficient measures the inequality among values of a frequency distribution, such as the levels of income. A Gini coefficient of 0 reflects perfect equality, where all income or wealth values are the same, while a Gini coefficient of 1 (or 100%) reflects maximal inequality among values. For example, if everyone has the same income, the Gini coefficient will be 0. In contrast, if for a large number of people only one person has all the income or consumption and all others have none, the Gini coefficient will be 1
whenever I use randomForestClassifier I get this error even though nothing in my data is object could not convert string to float: 'L56434' note: Im not using the dataset you are using in this video please help
I am getting an error " __init__() got an unexpected keyword argument 'min_sample_split' " . Can you help me out and tell me how I can resolve this error?
Most underrated video...the explanation was exactly what I want... awesome 👍
Thank you so much for your kind words!
Excellent video. Very clear, helpful, well paced. Thank you. A very nice job.
You're welcome!
Thanks a lot for your excellent explanation :)
You're welcome!
Hi! It will be really helpful if you will provide the details of the datasets you have taken for the analysis.
I think you will find the dataset here: github.com/siddiquiamir/Data
Really helpful. Thanks!
You're welcome!
you saved me thanks
I am glad to hear that!
i have dataset in which the values are strings and im not able to convert them to numeric values
Can you put the error messages?
Very helpful !! But i want to asky you how to deploy such code into arduino nano 33 ble sense board? And what code should be written for inference in arduino ide? Did yoi explained that in a video??
I have not explored that part.
Nice tutorial
Thank you
what should I DO if my target variable(dependent) is continuous number? how can i do it?
You have to use random forest regressor instead of random forest classifier
Hi. I don't see the plot.show() result like yours. I just see the blue target bar not the orange one in the plot.
Can you please try once more time by following the video.
Same here
try this:
sns.countplot(x=df["target"], hue=df["target"], dodge=False)
same here. it does not show distinct classes , it sum up all classes as one
from the scikit-learn documentation, the classification_report should get the y_true first and then the y_pred and now i'm confused
For latest information, please refer the documentation:)
Where can I find the heart rate csv to follow the example?
You can find the dataset here: github.com/siddiquiamir/Data
All I can find is the heart.csv but not not heart_disease.csv
Where can I get this dataset?
You can find it here
github.com/siddiquiamir/Data
Thnks brother! I will be very greatful If I get the notebook please!
Thank you. I will check on my machine and upload it on my GitHub and share it with you.
@@StatsWire actually don't worry about it, I have copied it from the video and tried myself in jupyter notbook, Thank you anyway ☺️
Hello, I would like to know hot to get an accuracy like yours, I mean, how it´s possible to have that score?
If you have same data and if provide the same random_state number then you should get almost same result:)
What does it mean to have a gini of zero?
how could i interpret it?
The Gini coefficient measures the inequality among values of a frequency distribution, such as the levels of income. A Gini coefficient of 0 reflects perfect equality, where all income or wealth values are the same, while a Gini coefficient of 1 (or 100%) reflects maximal inequality among values. For example, if everyone has the same income, the Gini coefficient will be 0. In contrast, if for a large number of people only one person has all the income or consumption and all others have none, the Gini coefficient will be 1
From where I can get dataset?
Please find the dataset here: github.com/siddiquiamir/Data
@@StatsWire may I know your linkedin ID
It is in my bio you can find and send me invite @@Aiman-pn2dy
whenever I use randomForestClassifier I get this error even though nothing in my data is object
could not convert string to float: 'L56434'
note: Im not using the dataset you are using in this video
please help
As you can see your data has both alphabet L and numbers 56434. You need to first clean your data.
@@StatsWire how exactly?
@@AbdelrahmanHareedey You can use regular expressions
bro your heart disease data set is not there in ur github pls help
Please find it here: github.com/siddiquiamir/Data/blob/master/Heart_Disease_Prediction.csv
Even this one: github.com/siddiquiamir/Data/blob/master/heart.csv
@@StatsWire thank you soo much bro 😇😇
@@Thanatos2062 You're welcome!
where is that heart diesesase file
You can find it here: github.com/siddiquiamir/Data
@@StatsWire thanks ♥️ really appreciate this kindness
@@paretare4946 You're welcome :)
Bro I need code and data set
Plz upload it bro
Hi, sorry for the delay. Please find the dataset here: github.com/siddiquiamir/Data
@@StatsWire Which data set in the one you sent ?
@@BadboyGaming-yk1vj You can get all the dataset in the github link.
All I see is heart rate no csv that says Heart_diseases@@StatsWire
I am getting an error " __init__() got an unexpected keyword argument 'min_sample_split' " .
Can you help me out and tell me how I can resolve this error?
This is a hyperparameter, can you check the version of scikit-learn
@@StatsWire sklearn.__version__ '1.0.2'
@@Rolling_panda00 Then it should not throw any error because it is a hyperparameter
@@StatsWire it's giving the same error in regression and decision tree too
And I've tried updating but it's not as helpful as I thought.
@@Rolling_panda00 can you email me your jupyternotebook at statswire2@gmail.com