Portfolio Project: Credit Card Fraud Detection for Vesta

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Section 1: Overview

  1. Course Overview
  2. How to Get Queries Resolved

Section 2: Setup Environment

  1. Python Setup Local Installation
  2. Python Setup Google Colab
  3. Download Resources

Section 3: Understanding the data

  1. Data Overview
  2. Optimize Memory Usage
  3. Basic Stats
  4. Course Review

Section 4: Pre-process Data and EDA

  1. Data Preprocessing for EDA
  2. Exploratory Data Analysis

Section 5: Statistical Significance Tests

  1. Chi Squared Test Theory and Maths
  2. Chi Square Test and Odds Ratio Demo
  3. ANOVA Concept- Intuition 4 ANOVA Cancept - Maths
  4. ANOVA Demo
  5. Course Review

Section 6: Exploring New Features and Pre-processing

  1. Feature Engineering
  2. Principal Components Analysis
  3. Feature Encoding Approaches
  4. Feature Encoding Demo
  5. Data Preprocessing for Model Building

Section 7: Building XGBoost Model

  1. Why XGBoost
  2. XGBoost Demo
  3. Course Review

Section 8: Model Evaluation Methods

  1. Confusion Matrix and Evaluation Metrics
  2. Concordance and Discordance
  3. ROC Curve
  4. Precision Recall Curve
  5. Evaluation Metrics Demo
  6. Capture Rates and Calibration Curve

Section 9: Using Complex ML Models for Better Performance

  1. Light GBM
  2. Light GBM Demo
  3. Decision Trees Algorithm
  4. Random Forest Classifier Algorithm
  5. Extra Trees Algorithm
  6. Random Forests Demo
  7. Course Review

Section 10: Using Model Improvement Strategies

  1. Random Oversampler
  2. Cost Sensitive Learning with Class Weights
  3. Probability Calibration
  4. Model Calibration Demo
  5. Model Tuning

Section 11: Interpreting and Explaining the Model

  1. Feature Importance
  2. Feature Importance Demo
  3. Partial Dependence and ICE Plots
  4. SHAP Interpretations
  5. SHAP Demo
  6. Course Review

Section 12: Summary

  1. Final Words
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