Customer Churn Prediction using ANN | Keras and Tensorflow | Deep Learning Classification
ฝัง
- เผยแพร่เมื่อ 21 ก.ค. 2024
- Customer Churn Prediction using Artificial Neural Networks (ANN) involves building a model to forecast whether a customer is likely to leave or continue using a service. The ANN learns from historical data, considering factors like usage patterns, customer interactions, and feedback to make predictions.
Code - www.kaggle.com/campusx/notebo...
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✨ Hashtags✨
#CustomerChurn #ANN #PredictiveAnalytics #CustomerRetention #MachineLearning #DataScience #BusinessAnalytics #neuralnetworks
⌚Time Stamps⌚
00:00 - Intro
02:22 - The Dataset
03:28 - Code Demo
17:49 - Neural Network Architecture
32:45 - Training error reduction plot
34:40 - Outro
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really appreciate how you are sharing your knowledge for free, i have paid almost 2L in IIT Delhi for this course and really not enjoying their sessions. But your sessions are addiction, i have started feeling confident by each passing day.
Although he is a playing a role of a teacher he is requesting us... to pick a pen and paper to practice to make sure that we understand how it works. It shows how obedient he is towards others.... God Gifted teacher
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Genuinely, you are among the best educators available for ML/DL.
It is a sign of a great teacher if he/she can explain difficult concepts in simple words. Nitish fits into the category easily !!!
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Link to the notebook:
www.kaggle.com/campusx/notebook8ad570467f
thank you bhai i have been watching your videos .... got the job finally u were a big help..thanks!!!
Best explanation !! you make ML DL so easy for us.
You have really a best/great teaching skills.. hat's off sir..
The explanation is so good. I watched a lot of videos for ANN but your playlist is truely a gem. Please keep uploading more videos sir!
i have never seen such a great teacher for ML and DL on yt or in my college the way nitish sir explains is so easy to understand and it helps grasp the basics very well. Thank you so much sir for such great efforts this channel needs so much recognition and it will reach it i definitely believe that.
I HAVE ALREADY SUGGESTED THIS CHANNEL TO SO MANY OF MY OTHER PEERS WHO ARE LEANRING ML AND DL
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Excellent video with clear explanation. One of my favorite channels on ML.
Sir, could you please also venture into federated learning in conjunction with ML/DL. That would be much appreciated.
Again thank you for your great videos.
The best explanation i got in my 12 months learning
Bhaiya please continue this course!!
This is one of the best DL courses
I want a million more subscribers for your channel, such a great teacher.
Sir You actually know how human brain works, thats why you are teaching how NN works and then teaching the Backpropagation, so that we can connect the backpropagation with the neural network. Great Sir. Your teaching style is great.
This video actually helped a lot put things in perspective.
Best Video about ANN . Concept clearning video . Thank you sir for this
dil se thank you bhai jii for making these simpler videos
Very Informational and way of explanation is amazing sir...thank you
Very clear and simple explanation, which any beginner will easily understand. Awesome lectures. Please keep uploading the further contents.
bht zayada dar tha k neural network pta nhi kya bala hai lakin sir ap k teaching method sy aisy laga k ye sab kuch nhi ap ka lecture 5 second b skip nhi kr skaty har point pr kuch na kuch learning k leye hai love u sir from Pakistan
You have Style to explain ML concept in a very easy manner
Thank You
This one video was enough to motivate me to keep learning Deep.
Thanks for this great and simple explanation!!
I love your way of teaching 😊😊
Informative and clear content. Thank you sir.
mindblowing explanation sir jee
Thank you so much sir i really try to understand this keras topic maine bahut jagah dekha but samjh nahi aya maine chatgpt se bhi samajhane ko kaha uska bhi samjh nahi aya but you are grate sir ❤
Great efforts. Thank you for each video !!
learning way is too good pls upload more projects ML and DL
One of the best teacher I have ever seen . With clear concepts and simple explanation skills❤❤❤ love from pakistan 🇵🇰🇵🇰🇵🇰
Where are you from Pakistan?
thank you so much sir ,your work is highly appreciated
bhot badhiya padhaya hai bhaiya😇
Perfect sekhate hi..essa lagta every thing is so easy
Bhaisaab... Yakeen nhi hota... Boring se boring topic ko bhi ye banda itna interesting trah se padha skta h.. Can't believe 🙏🙏
Mind blowing sir
enjoying the great content
outstanding, very easy. I salut
excellent video
Thanks for the video
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Great video
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Great video 🙏
How to find the right number of hidden layer with nodes?
hatsoff sir.....
😌
Please make video on feature selection techniques,imbalanced dataset and post in ML playlist
Failed to convert a NumPy array to a Tensor (Unsupported object type int). Anyone getting this error in the code?
nice video.
Nice explanation.
I have one doubt, Is scaling ordinal values beneficial??
Kudos bhaiyya
love it 😍 🥰
You are really great
Amazing again.
@25:30 sir hidden layer 1 hi h ye coding me kha show ho rha h???
best explanation
Clear explanation
Mza a gya hindi Me ml, DL sikhke
can i run same code on matlab
Perfect
Best practical lecture, very simple and clear. Thanks
love you Sir from pak
informative
Superb
Thankyou!
Very Nice
Hi Sir! at 16:43 first you said it is input layer after that you said it is a hidden layer. I am confused. Someone please explain.
we are providing 11 inputs to the hidden layer 1 which contains 3 neurons.
Nitish and his channel #CampusX is going to be next big thing on TH-cam soon...
Bahut maja aaya
Thank you sir
finished watching
sir please start full ml,dl course (live class)
Bhai ❤
Sir ye kaise maloom hoga ki kitne hidden layer add kerne hain aur each layer mein kitne node honge, is hyperparameter ko tune kerna ka koi code hai kya ?
Video daalenge next
WoW! 👍
Your videos is really informative and easy to understand. And i have been following your playlists. In this video i found something that is not clear to me and i also tried to check twice more. My problem is while one hot encoding command used is "pd.get_dummies(df,columns=['Geography','Gender'],drop_first=True)" and just below used "df.head()" i found out is that 2 columns are missing that are "Geography_France" and "Gender_Female". Are these mistakenly missing or you intentionaly did it. Please reply Thank again for your video s Sir.
I also thought that he missed the those columns. But it might have been done to reduce the time complexity of the model. BTW I am also not sure why he did that.
Failed to convert a NumPy array to a Tensor (Unsupported object type int). Did you get this error in the code?
i can't resist myself to comment
model = Sequential()
model.add(Embedding(10000,2,input_length=50))
model.add(SimpleRNN(32,return_sequences=False))
model.add(Dense(1,activation='sigmoid'))
model.summary()
best
🙏
wow
😊😊
A little bit understandable
for me, both the architectures gave 79% accuracy only, there is no improvement in the accuracy for the second architecture
finished coding
Your model is predicting always 0 , which is wrong, accuracy is 80 plus because more than 80 Percent target values are 0
He already told in the video about imbalance of Data. In real world problem, Balance your data before training of model 😊
nice
import tensorflow
from tensorflow import keras
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
model = Sequential()
model.add(Dense(3, activation='sigmoid', input_dim=11))
model.add(Dense(1, activation='sigmoid'))
model.summary()
🙏