A comprehensive collection of real-world machine learning projects focused on classification algorithms. This repository showcases diverse applications of classification models across healthcare, natural language processing, computer vision, and data analysis domains.
Classification is one of the most fundamental tasks in machine learning. This repository demonstrates practical implementations of classification algorithms on interesting and widely-used datasets, providing hands-on examples for learning and experimentation.
Each project includes:
- ✅ Data exploration and analysis
- ✅ Feature engineering and preprocessing
- ✅ Model training and evaluation
- ✅ Performance optimization
- ✅ Detailed documentation
Binary classification to predict whether a tumor is malignant or benign based on medical imaging features.
- Algorithm Focus: Logistic Regression, SVM, Random Forest, Neural Networks
- Use Case: Medical diagnosis assistance
- Location:
/Breast Cancer Detection
Multi-class classification to identify and distinguish between fake, satire, and reliable news articles.
- Algorithm Focus: NLP, Text Classification, Naive Bayes, SVM, Deep Learning
- Use Case: Misinformation detection
- Location:
/Fake News Predictions
Image classification for face detection and recognition in real-time video streams.
- Algorithm Focus: CNN, Deep Learning, Computer Vision
- Use Case: Security and authentication systems
- Location:
/Real-time face detection
Binary classification to identify spam emails from legitimate messages.
- Algorithm Focus: Text Classification, Naive Bayes, Logistic Regression
- Use Case: Email filtering and security
- Location:
/Spam Mail Detection
Classic binary classification predicting passenger survival on the Titanic.
- Algorithm Focus: Decision Trees, Random Forest, Gradient Boosting
- Use Case: Feature engineering best practices
- Location:
/Titanic Dataset Analysis
Multi-class classification identifying toxic behavior in online comments.
- Algorithm Focus: NLP, Deep Learning, Text Embeddings
- Use Case: Content moderation and online safety
- Location:
/Toxic Comment
Multi-class classification assessing water quality based on chemical and physical properties.
- Algorithm Focus: Ensemble Methods, Gradient Boosting
- Use Case: Environmental monitoring
- Location:
/Water Quality
Multi-class classification recognizing handwritten digits (0-9) using pixel intensity features from the MNIST dataset.
- Algorithm Focus: Logistic Regression, Random Forest, SVM, Computer Vision
- Use Case: Optical Character Recognition (OCR), postal mail sorting, check processing
- Location:
/Handwriting Recognition
- Python 3.x
- Data Processing: Pandas, NumPy
- Visualization: Matplotlib, Seaborn
- Machine Learning: Scikit-learn, TensorFlow, Keras
- NLP: NLTK, Scikit-learn (for text), Transformers
- Computer Vision: OpenCV, Pillow
- Model Evaluation: Scikit-learn metrics
This repository complements our previous work:
- SmartDataCleaner - A comprehensive data cleaning and preprocessing toolkit
Many techniques from SmartDataCleaner are applied during the preprocessing phases in these classification projects.
- ✨ Exploratory Data Analysis (EDA)
- ✨ Feature Scaling and Normalization
- ✨ Handling Imbalanced Datasets
- ✨ Cross-validation Techniques
- ✨ Hyperparameter Tuning
- ✨ Model Comparison and Selection
- ✨ Text Processing and NLP
- ✨ Computer Vision Basics
- ✨ Ensemble Methods
- ✨ Performance Metrics and Evaluation
Each project includes detailed evaluation metrics:
- Accuracy, Precision, Recall, F1-Score
- Confusion Matrix
- ROC-AUC Curves
- Feature Importance Analysis
Contributions are welcome! Feel free to:
- Report bugs
- Suggest improvements
- Add new classification projects
- Enhance documentation
Mohammad Majlesi - @mjmajlesi
If you find this repository helpful, please consider:
- ⭐ Starring the repository
- 📢 Sharing with others
- 💬 Providing feedback and suggestions
Happy Learning! 🚀 Explore diverse classification algorithms and improve your machine learning skills.