🤖 AlgoML-Collection: Machine Learning Algorithms Collection

A comprehensive implementation of fundamental machine learning algorithms using Python and scikit-learn. This repository demonstrates practical applications of supervised and unsupervised learning techniques across various real-world datasets.

📊 Algorithms Implemented

🎯 Supervised Learning

1. Support Vector Machine (SVM)

• Dataset: Breast cancer cell classification
• Implementation: Multiple kernel comparison (RBF, Polynomial, Sigmoid)
• Evaluation: Confusion matrix, F1-score, Jaccard index, Log loss
• Use Case: Binary classification for medical diagnosis

2. Logistic Regression

• Dataset: Customer churn prediction
• Features: Customer demographics and usage patterns
• Evaluation: Precision, recall, accuracy, classification report
• Use Case: Business analytics and customer retention

3. K-Nearest Neighbors (KNN)

• Dataset: Telecommunications customer segmentation
• Implementation: Optimal K selection with cross-validation
• Visualization: Accuracy vs. K-value plots with confidence intervals
• Use Case: Customer categorization and targeted marketing

4. Decision Trees

• Dataset: Drug prescription classification
• Features: Patient demographics and medical indicators
• Implementation: Entropy-based splitting with pruning
• Use Case: Medical decision support systems

🔍 Unsupervised Learning

5. K-Means Clustering

• Implementation: Synthetic and real-world data clustering
• Features: Centroid visualization and cluster optimization
• Applications: Customer segmentation and pattern recognition
• Datasets: Synthetic blobs and customer segmentation data

6. Hierarchical Clustering

• Methods: Agglomerative clustering with multiple linkage criteria
• Visualization: Dendrograms and cluster trees
• Comparison: Different distance metrics and standardization effects
• Use Case: Taxonomy creation and data organization

7. DBSCAN (Density-Based Clustering)

• Implementation: Density-based spatial clustering with noise detection
• Comparison: Performance analysis against K-Means
• Parameters: Epsilon and minimum samples optimization
• Use Case: Anomaly detection and irregular cluster shapes

🛠️ Technical Stack

• Python 3.x
• scikit-learn - Machine learning algorithms
• pandas - Data manipulation and analysis
• numpy - Numerical computing
• matplotlib - Data visualization
• scipy - Scientific computing

📁 Repository Structure

ML-Assignments-master/
├── SVM/                    # Support Vector Machine
│   ├── SVM.py
│   └── cell_samples.csv
├── LogisticReg/           # Logistic Regression
│   ├── Logistic_Reg.py
│   └── ChurnData.csv
├── K-NearestNeigh/        # K-Nearest Neighbors
│   ├── K-NearestNeigh.py
│   └── teleCust1000t.csv
├── DecisionTrees/         # Decision Trees
│   ├── decisionTrees.py
│   ├── drug200.csv
│   └── newData.csv
├── K-Means/               # K-Means Clustering
│   ├── K-Means-1.py
│   ├── K-Means-2.py
│   └── Cust_Segmentation.csv
├── Hierarchical/          # Hierarchical Clustering
│   ├── Hierarchical-1.py
│   ├── Hierarchical-2.py
│   └── cars_clus.csv
└── DBSCAN/               # DBSCAN Clustering
    ├── DBSCAN.py
    ├── Weather_station_clustring.py
    └── weather-stations20140101-20141231.csv

🚀 Getting Started

Prerequisites

pip install numpy pandas scikit-learn matplotlib scipy

Running the Examples

Each algorithm is self-contained and can be run independently:

# Navigate to specific algorithm directory
cd SVM/
python SVM.py

# Or run from root directory
python SVM/SVM.py

📈 Key Features

• Comprehensive Evaluation: Each implementation includes multiple evaluation metrics
• Data Preprocessing: Proper feature scaling and encoding techniques
• Visualization: Clear plots for model performance and data distribution
• Real-world Datasets: Practical applications across healthcare, business, and telecommunications
• Comparative Analysis: Multiple algorithms for similar problems
• Parameter Optimization: Grid search and cross-validation techniques

🎯 Learning Outcomes

This repository demonstrates:

• Algorithm Selection: Choosing appropriate algorithms for different problem types
• Data Preprocessing: Handling categorical variables, scaling, and cleaning
• Model Evaluation: Using appropriate metrics for classification and clustering
• Hyperparameter Tuning: Optimizing model parameters for better performance
• Visualization: Creating meaningful plots to understand data and results

📊 Performance Highlights

• SVM: Achieves high accuracy in cancer cell classification with RBF kernel
• Logistic Regression: Effective customer churn prediction with regularization
• KNN: Optimal performance with K=4 for customer segmentation
• Decision Trees: Clear decision boundaries for drug prescription
• Clustering: Successful pattern identification in customer and geographical data

🤝 Contributing

Feel free to fork this repository and submit pull requests for:

• Algorithm improvements
• Additional evaluation metrics
• New datasets
• Documentation enhancements

📝 License

This project is open source and available under the MIT License.

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