UPI Fraud Detection Using Machine Learning - Part 3: Model Building & Optimization #machinelearning

Hi There,

Welcome to AnalyticalAnuj, In this uncut video, we’re diving into Part 3 of the UPI Fraud Detection Project using machine learning. This time, we’ll focus on model building, optimization, and performance evaluation to create a robust fraud detection system.

What you’ll learn in this video:
✅ How to preprocess real-world data for machine learning.
✅ Choosing the best classification models (Decision Tree, Random Forest, Gradient Boosting, XGBoost).
✅ Handling class imbalance with SMOTE to improve model fairness.
✅ Evaluating model performance with critical metrics like Accuracy, Precision, Recall, and ROC-AUC.
✅ Optimizing model hyper parameters using GridSearchCV for better results.

By the end of this video, you’ll have a powerful, optimized machine learning model ready for fraud detection in financial systems.

Timestamps:
0:00 Introduction
00:57 Agenda for the video
04:37 Dropping unnecessary columns
05:43 Data preprocessing: Encoding & Scaling
10:45 Model selection & why these models are used
15:45 Performance metrics overview
24:58 Balancing data with SMOTE
26:55 Model Outputs
27:21 Model optimization with GridSearchCV
28:46 Retraining models and comparing results
30:10 Visualizing model performance
31:09 Saving the final model
31:54 Project Conclusion
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Видео UPI Fraud Detection Using Machine Learning - Part 3: Model Building & Optimization #machinelearning канала Anuj Rastogi