Credit Card Fraud Detection with Machine Learning in Python with Deployment скачать в хорошем качестве

Credit Card Fraud Detection with Machine Learning in Python with Deployment

Learn how to build a Credit Card Fraud Detection System using machine learning in Python and deploy it as an interactive web app with Streamlit! In this step-by-step tutorial, we’ll show you how to detect fraudulent transactions using powerful machine learning algorithms. 📌 What You’ll Learn in This Video: ✔ Data preprocessing and handling imbalanced datasets. ✔ Feature engineering techniques for fraud detection. ✔ Building and training machine learning models like Logistic Regression, Random Forest, and XGBoost. ✔ Evaluating model performance using precision, recall, and F1-score. ✔ Deploying the fraud detection system as a Streamlit web app. 📊 Topics Covered: 🔹 Exploratory Data Analysis (EDA) on financial transaction data. 🔹 Handling class imbalance using SMOTE or undersampling. 🔹 Model selection and hyperparameter tuning. 🔹 Developing an interactive UI with Streamlit for real-time fraud detection. 🚀 Tools and Libraries Used: 🟢 Python 🟢 Pandas, NumPy 🟢 Scikit-learn 🟢 LightGBM 🟢 Matplotlib, Seaborn 🟢 Streamlit 🟢 Geopy 🟢 SMOTE 💡 Who Is This Video For? This tutorial is perfect for data science enthusiasts, and developers who want to learn practical fraud detection techniques and deploy machine learning models in real-world applications. 🔗 Links Mentioned in the Video: 🔹 Code Repository:https://github.com/TensorTitans01/Fra... 🔹 Dataset: https://drive.google.com/file/d/1118J... 👉 Subscribe for More Machine Learning Projects! If you love machine learning, Python coding, and practical AI applications, subscribe to Tensor Titans for new tutorials every week! 🔔 Turn On Notifications to stay updated! 💬 Have Questions? Drop a comment below, and we’ll be happy to help! #CreditCardFraudDetection #MachineLearning #Python #FraudDetection #Streamlit #DataScience #TensorTitans