Data Science: Credit Card Fraud Detection - Model Building

🎓 Course Headline: Master Data Science with a Hands-On Project on Credit Card Fraud Detection 🚀

Data Science: Credit Card Fraud Detection - Model Building

Course Overview:

In this comprehensive course, you'll learn to develop a highly accurate Credit Card Fraud Detection model using advanced Machine Learning techniques. This is not just a theoretical course; it's a practical, hands-on project that will guide you through every step of creating and evaluating a machine learning model for real-world data.

🚀 What You Will Cover:

1. Installing Packages.

• Setting up your environment for success.

2. Importing Libraries.

• Essential libraries for Data Science and Machine Learning.

3. Loading the data from source.

• Handling data sources efficiently.

4. Understanding the data

• Initial exploration of the dataset.

5. Checking the class distribution of the target variable

• Analyzing the balance between classes.

6. Finding correlation and plotting Heat Map

• Identifying relationships in the data.

7. Performing Feature engineering.

• Crafting features that matter.

8. Train Test Split

• Dividing the data for model development and validation.

9. Plotting the distribution of a variable

• Visualizing data distributions.

10. About Confusion Matrix, Classification Report, AUC-ROC - Understanding performance metrics.

11. Created a common function to plot confusion matrix - Tools for interpreting model predictions.

12. Logistic Regression, KNN, Tree, Random Forest, XGBoost, SVM Models - Exploring different machine learning algorithms.

13-18. Common functions for model fitting and prediction - Automating the predictive process for efficiency.

19. About RepeatedKFold and StratifiedKFold. - Understanding cross-validation techniques.

20-21. Cross validation with RepeatedKFold and StratifiedKFold - Evaluating model performance with cross-validation.

22-24. Proceeding with the best model, Oversampling with Random Oversampler & SMOTE - Techniques for improving model accuracy on imbalanced datasets.

25-26. Hyperparameter Tuning and Feature Importance Extraction - Refining your model to perform at its best.

27. Final Inference - Drawing conclusions from your analysis.

🎫 Learning Resources:

• Certificate of completion: Earn a certificate from AutomationGig upon course completion.
• Datasets: Access all datasets used throughout the course in the resources section.
• Jupyter Notebook & Project Files: Get hands-on with the provided Jupyter notebook and project files at the end of the course.

📅 Ready to Start?

• Coffee Mug at the ready!
• Enroll Now: Click the button and jump into one of the most demanded skills of the 21st century.
• Join the Learning Community: Let's embark on this journey together!

🎓 Happy Learning!

Remember, this course and its content are for educational purposes only. So, grab your chance to learn Data Science through a real-world application with our Credit Card Fraud Detection project. We can't wait to see you inside the course! 🌟

[Music: bensound] 🎶

Happy Learning and here's to your future success in Data Science! 🚀💡