Curriculum
| Section 1: Introduction to Fraud Analytics | |||
|---|---|---|---|
| Lecture 1 |
Welcome note
Preview | 02:13 | |
| Lecture 2 |
Introduction and Problem Definition
Preview | 06:28 | |
| Section 2: Data Exploration | |||
| Lecture 3 |
Understanding the Data
| 10:06 | |
| Lecture 4 |
Treating Categorical Variables
| 02:44 | |
| Section 3: Data Preparation, Model Creation and Validation | |||
| Lecture 5 |
Data Preparation- Sampling and Missing Value Treatment
| 08:46 | |
| Lecture 6 |
Data Preparation- Outlier Treatment
| 08:37 | |
| Lecture 7 |
Data Partitioning
| 02:54 | |
| Lecture 8 |
Variable Reduction
| 02:34 | |
| Lecture 9 |
Creating Deciles
| 07:07 | |
| Lecture 10 |
Variable Transformation- Part 1
| 01:59 | |
| Lecture 11 |
Variable Transformation- Part 2
| 03:30 | |
| Lecture 12 |
Model Creation
| 08:39 | |
| Lecture 13 |
Gains Chart
| 09:35 | |
| Lecture 14 |
Validation
| 02:47 | |
| Lecture 15 |
Extra
| 01:03 | |
Instructor Biography
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