Build an Artificial Neural Network to Predict Customer Churn | Step-by-Step Tutorial

Unlock the secrets of AI and machine learning in this comprehensive step-by-step tutorial! In this video, we’ll guide you through building an artificial neural network (ANN) to predict customer churn using Python and TensorFlow. Whether you’re a beginner or looking to enhance your skills, this tutorial covers everything from installing libraries to making predictions.
Find the dataset used here: www.kaggle.com...
📚 What You’ll Learn:
1. Preprocessing and scaling data
2. Building and compiling the neural network model
3. Training and evaluating the model
4. Making predictions with real customer data
🔥 Why Watch This Video?
• Perfect for beginners and intermediate learners
• Detailed explanations of each line of code
• Visualize the training process and understand model performance
• Practical application with real-world data
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#ai #machinelearning #neuralnetworks #datascience #artificialintelligence

[@TheMaxKids]

Absolutely the best video on the topic I've seen yet. Thank you.

[@0.4sks19]

where can i find your documentation code?

[@abdulsalamaliyu2563]

Please how do you get the numeric representation of datas that you use to predict churn ?

[@TheOpenSourceChannel]

Please check the video at 4:30
There are two steps to get the numeric representation of data.
Label Encoding: It converts categorical data into numerical labels. For example Female become 0 and Male become 1.
One-Hot Encoding: It creates new binary features for each category to represent its independent effects. For example France become [1, 0, 0], Spain become [0, 0, 1], and Germany become [0, 1, 0].

[@srishrachamalla9607]

hey i have a qn should i use ML or DL when these kind of problems statements used?

[@TheOpenSourceChannel]

If you have a smaller dataset, you should start with an ML model. It is beginner friendly as you can use established ML algorithms.
If you have a large and complex dataset, and want to learn complex features and relationships from your data, then DL might be worth exploring. Because DL requires more computational resources and complex to implement.