Kaggle PROJECT: "Credit Card Fraud Detection" | Industry Relevant AI ML Course

📞 Want 1:1 personal mentorship with me? Book a session on Topmate here: [https://topmate.io/sonuyadav5504] Whether you're a student, working professional, developer, or complete beginner, this detailed module will build a strong foundation for the rest of the course. In this video, we implement Credit Card Fraud Detection from scratch using Logistic Regression in Python on the Kaggle dataset. Instead of using sklearn’s built-in model, we build everything manually: Linear score computation Sigmoid function Log-loss Gradient Descent updates Class imbalance handling Precision, Recall, F1-score ROC Curve & AUC This project helps you deeply understand how fraud detection systems work in real-world financial applications. 📊 Dataset Used: Kaggle Credit Card Fraud Detection 📌 Skills Covered: Logistic Regression from scratch Handling Imbalanced Data ROC-AUC vs Accuracy Model Evaluation Techniques End-to-End ML Project This is perfect for: Data Science Beginners Machine Learning Students GATE DA Aspirants Placement Preparation Portfolio Projects If you want to master ML fundamentals instead of just using libraries, this video is for you. By the end, you’ll know how to clean data the right way and build leakage-free ML pipelines that generalize well. 0:00 Credit Card Fraud Detection Project Introduction 0:10 Logistic Regression from Scratch Project 0:35 Multi Class Logistic Regression Quick Revision 1:08 Binary Logistic Regression Recap 2:24 Multi Class Classification with Softmax 3:34 Softmax Function Explained 4:25 Multi Class Loss Function Explained 5:43 Binary Case from Multi Class Loss 8:15 Four Class Softmax Example 9:43 One Hot Encoding for Multi Class Targets 11:04 Gradient Descent Update for Multi Class Logistic Regression 11:54 Credit Card Fraud Detection Dataset Overview 12:28 Class Imbalance Problem in Fraud Detection 13:03 Weighted Loss for Imbalanced Classes 13:26 Credit Card Dataset Features Explained 14:31 Logistic Regression Model Formulation 14:58 Weighted Binary Cross Entropy Loss 15:21 Class Weight Calculation Explained 17:18 Evaluation Metrics for Fraud Detection 17:25 Importing Python Libraries 17:41 Loading Credit Card Fraud Dataset 17:57 Splitting Features and Target Variable 18:44 Dataset Shape and Fraud Count 19:14 Checking Missing Values 19:31 Dataset Information and Data Types 20:06 Fraud vs Non Fraud Class Distribution 20:19 Correlation Matrix for Feature Analysis 21:48 Fraud Transaction Statistics 23:07 Train Validation Test Split 24:25 Standardization Without Data Leakage 25:21 Defining Sigmoid Function 25:28 Binary Cross Entropy Loss Function 25:52 Clipping Probabilities for Numerical Stability 26:42 Computing Class Weights 27:14 Training Logistic Regression Function 28:01 Forward Pass in Logistic Regression 28:36 Error Calculation and Weighted Loss 29:22 Applying Class Weights to Errors 31:54 Gradient Calculation for Weights and Bias 32:32 Updating Model Parameters 32:45 Training and Validation Loss Tracking 34:23 Training Model with Weighted Loss 35:43 Predicting on Test Data 36:10 Threshold Selection for Fraud Detection 36:55 Confusion Matrix and Model Metrics 37:45 Effect of Lower Threshold on Fraud Detection 38:45 Training Without Weighted Loss 40:02 Why Class Imbalance Handling is Important 41:34 ROC Curve and Threshold Analysis 42:30 ROC AUC Score Interpretation 43:00 Project Practice and Final Summary 43:13 End of Lecture 🔗 Connect with Me: ---------------------------------- Instagram (YouTube) →   / sonuyadav_iitdelhi   Instagram (Personal) →   / sonuyadav5504   👉 Join WhatsApp Channel: https://whatsapp.com/channel/0029Vb7b... WhatsApp Group → https://chat.whatsapp.com/HjGuZZr07Uu... ---------------------------------- #ArtificialIntelligence #AIML #MachineLearning #DeepLearning #NLP #ComputerVision #GenerativeAI #LLM #AIforBeginners #TechEducation #FreeCourse #SonuYadav