Portfolio Project: Credit Card Fraud Detection for Vesta
Go to main | Course Page
Section 1: Overview
- Course Overview
- How to Get Queries Resolved
Section 2: Setup Environment
- Python Setup Local Installation
- Python Setup Google Colab
- Download Resources
Section 3: Understanding the data
- Data Overview
- Optimize Memory Usage
- Basic Stats
- Course Review
Section 4: Pre-process Data and EDA
- Data Preprocessing for EDA
- Exploratory Data Analysis
Section 5: Statistical Significance Tests
- Chi Squared Test Theory and Maths
- Chi Square Test and Odds Ratio Demo
- ANOVA Concept- Intuition
4 ANOVA Cancept - Maths
- ANOVA Demo
- Course Review
Section 6: Exploring New Features and Pre-processing
- Feature Engineering
- Principal Components Analysis
- Feature Encoding Approaches
- Feature Encoding Demo
- Data Preprocessing for Model Building
Section 7: Building XGBoost Model
- Why XGBoost
- XGBoost Demo
- Course Review
Section 8: Model Evaluation Methods
- Confusion Matrix and Evaluation Metrics
- Concordance and Discordance
- ROC Curve
- Precision Recall Curve
- Evaluation Metrics Demo
- Capture Rates and Calibration Curve
- Light GBM
- Light GBM Demo
- Decision Trees Algorithm
- Random Forest Classifier Algorithm
- Extra Trees Algorithm
- Random Forests Demo
- Course Review
Section 10: Using Model Improvement Strategies
- Random Oversampler
- Cost Sensitive Learning with Class Weights
- Probability Calibration
- Model Calibration Demo
- Model Tuning
Section 11: Interpreting and Explaining the Model
- Feature Importance
- Feature Importance Demo
- Partial Dependence and ICE Plots
- SHAP Interpretations
- SHAP Demo
- Course Review
Section 12: Summary
- Final Words