Bank Customer Churn Prediction Using 6 ML Models + ANN | Part 6 Splitting Dataset

🚀 In this video, I build a complete Bank Customer Churn Prediction project from scratch using Machine Learning and Deep Learning techniques in Google Colab.

This project includes:
✅ Data Preprocessing
✅ Exploratory Data Analysis (EDA)
✅ Feature Engineering
✅ Feature Scaling
✅ Model Training & Evaluation
✅ Comparison of Multiple Models

📌 Models Used:

1. Logistic Regression
2. K-Nearest Neighbors (KNN)
3. Random Forest
4. Naive Bayes
5. XGBoost (XGB)
6. Artificial Neural Network (ANN)

🛠️ Technologies:
Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, TensorFlow/Keras, XGBoost

📂 GitHub Repository:
https://github.com/ArtemisFwl/Bank_customer_churn_prediction

Google Drive Link: https://drive.google.com/drive/folders/1qHG6zP_b7x5OZ6JfAC2bmopIKrSkM_4W

⭐ If you enjoyed the video, don’t forget to Like, Share, and Subscribe for more AI/ML and Deep Learning projects.

#MachineLearning #DeepLearning #ANN #XGBoost #CustomerChurn #PythonProject #DataScience #AI #NeuralNetwork #MLProject

Видео Bank Customer Churn Prediction Using 6 ML Models + ANN | Part 6 Splitting Dataset канала The Neural Network

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The Neural Network

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