Machine Learning Algorithms Comparison
A machine learning project focused on the implementation, evaluation, and comparison of multiple supervised and unsupervised learning algorithms.
The objective was to understand the strengths, weaknesses, and performance of different machine learning techniques on real datasets.
Algorithms Implemented
K-Nearest Neighbors (KNN)
- Classification algorithm
- Multiple values of K tested
- Accuracy comparison performed
- Best performance identified through experimentation
K-Means Clustering
- Unsupervised learning
- Cluster segmentation analysis
- Comparison between different cluster counts
- Data grouping and pattern discovery
Random Forest
- Ensemble learning method
- Multiple tree configurations tested
- Feature importance analysis
- Confusion matrix evaluation
Support Vector Machine (SVM)
- Linear Kernel
- Polynomial Kernel
- RBF Kernel
- Sigmoid Kernel
Performance comparison performed across kernels.
Model Evaluation
Evaluation techniques include:
- Accuracy Score
- Confusion Matrix
- Feature Importance
- Hyperparameter Tuning
- Model Comparison
Technologies Used
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-Learn
- Jupyter Notebook
- Google Colab
Repository Structure
machine-learning-algorithms/ │ ├── README.md │ ├── notebooks/ │ └── ml_algorithms.ipynb │ └── presentation/ └── machine_learning_presentation.pdf
Learning Outcomes
- Classification algorithms
- Clustering techniques
- Ensemble learning
- Support Vector Machines
- Model evaluation
- Hyperparameter tuning
- Feature importance analysis
Author
Adem Jlassi
BDAD – Big Data & Data Analytics
ISAMM Tunis