Portfolio Project: Credit Card Fraud Detection for Vesta

What you'll be able to do

• Frame Data Science Problem Statement
• Extensive Exploratory Data Analysis
• Feature Engineering Strategies
• Build Multiple Classification Models
• Model Evaluation and Improvement
• Advanced Machine Learning Algorithms
• Real data processing techniques
• Derive high business impact insights

1. 01Overview2 lessons · 4 min

   1. Course OverviewPreview3:26
   2. How to Get Queries Resolved1:00
2. 02Setup Environment3 lessons · 16 min

   1. Python Setup Local Installation10:35
   2. Python Setup Google Colab4:33
   3. Download Resources1:00
3. 03Understanding the data4 lessons · 17 min

   1. Data OverviewPreview5:05
   2. Optimize Memory Usage4:40
   3. Basic StatsPreview6:00
   4. Course Review1:00
4. 04Pre-process Data and EDA2 lessons · 16 min

   1. Data Preprocessing for EDA4:29
   2. Exploratory Data Analysis11:54
5. 05Statistical Significance Tests6 lessons · 57 min

   1. Chi Squared Test Theory and Maths17:47
   2. Chi Square Test and Odds Ratio Demo9:10
   3. ANOVA Concept - Intuition10:07
   4. ANOVA Cancept - Maths12:40
   5. ANOVA Demo6:31
   6. Course Review1:00
6. 06Exploring New Features and Pre-processing5 lessons · 28 min

   1. Feature Engineering5:16
   2. Principal Components Analysis8:22
   3. Feature Encoding Approaches8:02
   4. Feature Encoding Demo4:10
   5. Data Preprocessing for Model Building2:38
7. 07Building XGBoost Model3 lessons · 8 min

   1. Why XGBoost4:25
   2. XGBoost Demo2:07
   3. Course Review1:00
8. 08Model Evaluation Methods6 lessons · 48 min

   1. Confusion Matrix and Evaluation Metrics9:21
   2. Concordance and Discordance9:18
   3. ROC Curve9:22
   4. Precision Recall Curve2:54
   5. Evaluation Metrics Demo6:36
   6. Capture Rates and Calibration Curve10:46
9. 09Using Complex ML Models for Better Performance8 lessons · 48 min

   1. Light GBM7:39
   2. Light GBM Demo3:37
   3. Random Forest Introduction0:32
   4. Decision Trees Algorithm13:21
   5. Random Forest Classifier Algorithm6:14
   6. Extra Trees Algorithm5:55
   7. Random Forests Demo9:20
   8. Course Review1:00
10. 10Using Model Improvement Strategies5 lessons · 33 min

    1. Random Oversampler9:48
    2. Cost Sensitive Learning with Class Weights6:43
    3. Probability Calibration8:09
    4. Model Calibration Demo1:49
    5. Model Tuning6:50
11. 11Interpreting and Explaining the Model6 lessons · 33 min

    1. Feature Importance11:11
    2. Feature Importance Demo3:07
    3. Partial Dependence and ICE Plots5:35
    4. SHAP Interpretations7:11
    5. SHAP Demo5:12
    6. Course Review1:00
12. 12Summary1 lesson · 2 min

    1. Final Words2:04

What you need

• Basics of Python
• Foundational knowledge of Data Science
• High school maths

Who this course is for

• Professionals in the field of data science
• Professionals looking for a robust, structured Python learning program
• Software or data engineers interested in quantitative analysis
• Professionals working with large datasets
• Data analysts, economists, researchers

Your instructor

Prabhakaran S

Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this course!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I've deployed 20+ global products. I'm also the Founder and Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I've put up the series of courses and projects with detailed explanations, just like on-the-job experience. Hope you love it!

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