Data Mining Full Course Part 5 (3 Hours): Market Basket Analysis, Outliers & Fraud Detection

Find what customers buy together with Apriori and FP-Growth, mine sequential patterns, and catch the records that don't fit. Learn statistical, distance- and density-based outlier detection, then build a credit card fraud detector on 284,807 real transactions. Part 5 of 6 of the complete Data Mining Full Course in Python (49 lessons, about 20 hours). What you'll be able to do: ✅ Run market basket analysis with Apriori and FP-Growth ✅ Mine sequential patterns in customer behaviour ✅ Detect outliers with statistical, distance and density methods ✅ Build a credit card fraud detection model on real data 👤 Who it's for: analysts in retail, banking, cybersecurity and risk. ▶️ All 6 parts: https://www.youtube.com/playlist?list=PLKBh1EsfMSjQ ⏮️ Previous: Part 4: https://youtu.be/8-1nXp2Dx0I ▶️ Next: Part 6: https://youtu.be/CEqyIaj-r9g 📓 Free notebooks, datasets and cheat sheets for every lesson (no sign-up): https://www.mathewkanalytics.com/ 🔔 Subscribe: new free lessons every day. Chapters 0:00 Lesson 33 - Association Rule Mining with Apriori Algorithm 0:23:18 Lesson 34 - FP-Growth and Market Basket Analysis 0:47:48 Lesson 35 - Sequential Pattern Mining 1:12:07 Lesson 36 - Introduction to Anomaly and Outlier Detection 1:36:25 Lesson 37 - Statistical Methods for Outlier Detection 2:00:42 Lesson 38 - Distance and Density-Based Outlier Detection 2:23:58 Lesson 39 - Applications in Fraud Detection and Cybersecurity 2:46:46 Lesson 40 - Hands-on Project- Credit Card Fraud Detection #datamining #python #datascience #machinelearning #dataanalysis