Machine Learning Examples

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Table of Contents

• Setup
• Models
  • MNIST Digit Classification
  • Logistic Regression
  • Linear Regression
  • Content-Based Filtering
  • Fraud Detection
  • Patient Clustering
  • Sentiment Analysis
• License

Project Structure

src/
├── clustering/          # Patient clustering analysis
├── fraud-detection/     # Fraud detection models
├── sentiment_analysis/  # Text sentiment analysis
├── mnist/              # MNIST digit classification
├── logistic-regression/ # Binary classification
├── linear-regression/   # Linear regression
├── content-based-filtering/ # Content-based filtering
└── requirements.txt     # Dependencies

Setup

1. Clone the repository:

git clone https://github.com/mineme0110/ml.git
cd ml

2. Create virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Models

Patient Clustering

Unsupervised learning for patient segmentation based on health metrics.

1. Install dependencies:

pip install -r src/clustering/requirements.txt

2. Run clustering analysis:

python src/clustering/train.py

Features:

• Multiple clustering algorithms:
  • K-means: Groups patients into k clusters
  • DBSCAN: Density-based clustering
  • Hierarchical: Creates cluster hierarchy
• Health metrics analyzed:
  • Age
  • BMI
  • Blood Pressure
  • Glucose Level
  • Cholesterol
  • Heart Rate
• Evaluation metrics:
  • Silhouette Score
  • Calinski-Harabasz Score
• Visualizations:
  • 2D cluster plots
  • Feature distributions

Example output:

Training KMEANS clustering...

Clustering Results:
Number of clusters: 3
Silhouette Score: 0.303
Calinski-Harabasz Score: 543.462

Cluster Characteristics:
Cluster 0 (Young, Healthy):
- Age: ~25 years
- BMI: ~22
- Blood Pressure: ~110

Cluster 1 (Middle-aged):
- Age: ~45 years
- BMI: ~28
- Blood Pressure: ~130

Cluster 2 (Elderly):
- Age: ~70 years
- BMI: ~26
- Blood Pressure: ~145

Sentiment Analysis

Text classification for sentiment analysis of movie reviews.

1. Install dependencies:

pip install -r src/sentiment_analysis/requirements.txt

2. Train the model:

python src/sentiment_analysis/train.py

3. Interactive testing:

python src/sentiment_analysis/interactive_test.py

MNIST Digit Classification

Neural network for MNIST digit classification using PyTorch.

1. Install dependencies:

pip install -r requirements.txt

2. Run the training script:

python src/mnist/train.py

Logistic Regression

Binary classification example using scikit-learn's Logistic Regression.

1. Install dependencies:

pip install -r src/logistic-regression/requirements.txt

2. Run the training script:

python src/logistic-regression/train.py

This will demonstrate logistic regression on a generated dataset with visualization of the decision boundary.

Content-Based Filtering

Movie recommendation system using content-based filtering.

1. Install dependencies:

pip install -r src/content-based-filtering/requirements.txt

2. Run the recommendation system:

python src/content-based-filtering/train.py

This will demonstrate movie recommendations based on content similarity using a sample movie dataset. The system considers movie genres, actors, and descriptions to make recommendations.

Example output:

Getting recommendations for: The Dark Knight
Recommended Movies:
------------------------------------------------------------
1. Iron Man
   Genres: Action, Adventure, Sci-Fi
   Similarity Score: 0.8245
------------------------------------------------------------
2. The Matrix
   Genres: Action, Sci-Fi
   Similarity Score: 0.7856

Fraud Detection

XGBoost-based fraud detection system for identifying fraudulent transactions.

Prerequisites for Mac users:

# Install OpenMP library (required for XGBoost)
brew install libomp

Simple Model

A basic fraud detection model with clear separation between normal and fraudulent transactions.

1. Install dependencies:

pip install -r src/fraud-detection/requirements.txt

2. Run the simple fraud detection model:

python src/fraud-detection/train_simple.py

Features:

• Clear separation between classes
• Basic XGBoost parameters
• Perfect for understanding basic fraud detection concepts
• Shows idealized probability distributions

Realistic Model

A more sophisticated model that better represents real-world fraud detection scenarios.

1. Install dependencies (if not already installed):

pip install -r src/fraud-detection/requirements.txt

2. Run the realistic fraud detection model:

python src/fraud-detection/train_realistic.py

Features:

• Handles imbalanced classes
• Uses realistic transaction patterns:
  • Transaction amounts
  • Time of day patterns
  • Geographic distances
  • Transaction frequency
• Early stopping and validation
• Feature importance analysis
• More representative probability distributions

Example output:

Realistic Model Performance:
ROC AUC Score: 0.9985
Top 5 Most Important Features:
transaction_amount: 0.4532
time_of_day: 0.2876
distance_from_last: 0.1543
transaction_frequency: 0.0892
feature_5: 0.0157

License

This project is distributed under the terms of the MIT license.

Project Structure