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Anomaly Detection using Autoencoders | Credit Card Transactions | TensorFlow & Keras Tutorial Hindi

🎯 Anomaly Detection in Credit Card Transactions | Autoencoders in TensorFlow/Keras

💻 My Kaggle Profile:
🔗 https://www.kaggle.com/codingloading

💻 My GitHub Repository:
🔗 https://github.com/DataScientist00

💻 Explore the Full Project Code:
🔗 https://www.kaggle.com/code/codingloading/reconstruct-detect-autoencoders-for-anomalies

📁 Dataset Source:
🔗 https://www.kaggle.com/datasets/dhanushnarayananr/credit-card-fraud

📌 In this video, I walk you through a complete deep learning pipeline for detecting anomalies in credit card transactions using Autoencoders. The project leverages the popular Credit Card Fraud Detection dataset from Kaggle to identify fraudulent transactions through unsupervised learning.

We start with data preprocessing and normalization, move on to building and training an Autoencoder for anomaly detection, and finally evaluate model performance using reconstruction error and ROC-AUC metrics.

📊 Whether you're exploring anomaly detection, unsupervised learning, or advanced neural network architectures, this video covers everything from data preparation to fraud detection insights.

🧠 What You’ll Learn:
✅ Preparing and normalizing credit card transaction data
✅ Building an Autoencoder model using TensorFlow/Keras
✅ Training the model to detect anomalies (fraudulent transactions)
✅ Evaluating results using reconstruction error and metrics
✅ Visualizing fraud detection performance
✅ Gaining insights into unsupervised anomaly detection

🚀 Ideal For:
📚 Machine Learning Enthusiasts
🧠 Deep Learning Practitioners
💡 AI & Data Science Learners
📈 Fraud Detection Researchers
💻 Anyone exploring anomaly detection with neural networks

📌 Keywords for SEO:
#AnomalyDetection #Autoencoder #CreditCardFraud #TensorFlow #Keras #DeepLearning #MachineLearning #KaggleProjects #UnsupervisedLearning #DataScience #AIProjects #FraudDetection #NeuralNetworks #PythonCode #CodeWithMe #MLProject #KerasTutorial #TensorFlowProjects

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