IBM Supervised Machine Learning Classification Course - Project Overview

By: Vadhna Samedy Hun

🤖 Course: IBM Supervised Machine Learning Classification (Coursera)
🛠 Skills Learned: Logistic Regression, KNN, SVM, Decision Trees, Random Forest, XGBoost, and more

📌 Project Description

This repository contains my work from the IBM Supervised Machine Learning Classification Course on Coursera. Throughout this course, I gained hands-on experience with various classification algorithms and evaluation techniques, including:

• Binary and multi-class classification methods
• Model selection and hyperparameter tuning
• Performance evaluation metrics
• Model interpretation techniques
• Handling unbalanced classes

Each module explores different classification algorithms with practical implementations and real-world datasets.

🛠 Classification Algorithms & Techniques Mastered

Algorithm/Technique	Application
Logistic Regression	Binary/multiclass classification with linear decision boundaries
K-Nearest Neighbors	Non-parametric, distance-based classification
Support Vector Machines	Classification with maximum margin hyperplanes
Decision Trees	Rule-based classification with interpretable results
Random Forest	Ensemble learning for improved accuracy and robustness
XGBoost	Gradient boosting for high-performance classification
ROC-AUC	Evaluating model performance across thresholds
Confusion Matrix	Detailed analysis of prediction errors
Cross-Validation	Robust model evaluation and hyperparameter tuning

📂 Repository Structure

├── module1_logistic_regression/    # Logistic regression implementations and exercises
├── module2_knearest_neighbors/     # K-nearest neighbors algorithm applications
├── module3_support_vector/         # Support vector machine classification
├── module4_decision_trees/         # Decision tree models and pruning techniques
├── module5_random_forest/          # Random forest ensemble methods
├── module6_model_interpretability/ # Understanding and interpreting model decisions
│   ├── model_interpretability/     # Techniques for model explanation
│   └── unbalanced_classes/         # Handling class imbalance problems
│
├── final_project/                  # Capstone classification project
├── .qodo/                          # Configuration files
├── .gitignore                      # Git ignore file
└── README.md                       # This file

🚀 How to Run the Code

1. Clone the repo

   git clone https://github.com/SamedyHUNX/ibm-supervised-machine-learning
   cd ibm-supervised-machine-learning

2. Install dependencies

   pip install -r requirements.txt  # If applicable

3. Run Jupyter notebooks

   jupyter notebook

🔍 Key Takeaways

✅ Building and evaluating classification models for different problem types
✅ Selecting appropriate algorithms based on data characteristics
✅ Tuning models for optimal performance
✅ Handling common challenges like class imbalance
✅ Interpreting model decisions for stakeholder communication
✅ Implementing cross-validation for robust model evaluation

📊 Performance Metrics Explored

• Accuracy, Precision, Recall, F1-Score
• ROC Curves and AUC
• Confusion Matrices
• Cross-Validation Scores
• Learning Curves

📜 License

This project is part of an IBM/Coursera course and is intended for educational purposes.

📬 Contact

✉️ Email: samedy.hunx@gmail.com

Happy classifying! 🤖📊

🔗 References

• IBM Supervised Machine Learning Classification Course (Coursera)
• Scikit-Learn Documentation
• XGBoost Documentation
• IBM Developer Machine Learning