Customer Churn Prediction using ANN | Keras and Tensorflow | Deep Learning Classification
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- Опубликовано: 26 июн 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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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.
The best explanation i got in my 12 months learning
This video actually helped a lot put things in perspective.
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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!
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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.
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dil se thank you bhai jii for making these simpler videos
Very clear and simple explanation, which any beginner will easily understand. Awesome lectures. Please keep uploading the further contents.
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Thank You
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Where are you from Pakistan?
Best Video about ANN . Concept clearning video . Thank you sir for this
Link to the notebook:
www.kaggle.com/campusx/notebook8ad570467f
mindblowing explanation sir jee
Informative and clear content. Thank you sir.
Great efforts. Thank you for each video !!
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enjoying the great content
Mind blowing sir
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Thanks for the video
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love it 😍 🥰
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nice video.
Clear explanation
Kudos bhaiyya
best explanation
Nice explanation.
Thankyou!
Perfect
love you Sir from pak
Superb
Please make video on feature selection techniques,imbalanced dataset and post in ML playlist
informative
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Best practical lecture, very simple and clear. Thanks
How to find the right number of hidden layer with nodes?
Bhai ❤
WoW! 👍
Very Nice
@25:30 sir hidden layer 1 hi h ye coding me kha show ho rha h???
Thank you sir
Bahut maja aaya
I have one doubt, Is scaling ordinal values beneficial??
Mza a gya hindi Me ml, DL sikhke
finished watching
i can't resist myself to comment
can i run same code on matlab
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.
best
Failed to convert a NumPy array to a Tensor (Unsupported object type int). Anyone getting this error in the code?
sir please start full ml,dl course (live class)
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?
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
🙏
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()
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()
🙏