A comprehensive collection of Machine Learning projects demonstrating expertise in Classification, Regression, Data Preprocessing, and Model Evaluation using scikit-learn and Python's data science ecosystem.
| # | Project | Algorithm | Notebook | Application |
|---|---|---|---|---|
| 1 | Breast Cancer Classification | Logistic Regression, SVM | 01_breast_cancer_classification.ipynb |
Medical Diagnosis |
| 2 | K-Nearest Neighbors | KNN Classifier | 02_knn_classifier.ipynb |
Pattern Recognition |
| 3 | Kernel Ridge Regression | KRR | 03_kernel_ridge_regression.ipynb |
Nonlinear Regression |
| 4 | Data Preprocessing Pipeline | Feature Engineering | 04_data_preprocessing.ipynb |
Data Cleaning & Transformation |
| 5 | ML Algorithms Lab | Multiple Algorithms | 05_ml_algorithms_lab.ipynb |
Comparative Analysis |
| 6 | Comprehensive ML Project | End-to-End Pipeline | 06_comprehensive_ml_project.ipynb |
Production-Ready Workflow |
- scikit-learn - Machine learning algorithms
- Pandas - Data manipulation and analysis
- NumPy - Numerical computing
- Matplotlib & Seaborn - Data visualization
- Supervised Learning - Classification & Regression
- Feature Engineering - Scaling, encoding, selection
- Model Evaluation - Cross-validation, metrics
- Hyperparameter Tuning - Grid search, optimization
- Python 3.8 or higher
-
Clone the repository
git clone https://github.com/uzi-gpu/machine-learning-projects.git cd machine-learning-projects -
Create a virtual environment
python -m venv venv source venv/bin/activate # On Windows: venv\\Scripts\\activate
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Install dependencies
pip install -r requirements.txt
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Launch Jupyter Notebook
jupyter notebook
File: 01_breast_cancer_classification.ipynb
Objective: Build a binary classifier to diagnose breast cancer (benign vs malignant)
Dataset: Wisconsin Breast Cancer Dataset
- 569 samples
- 30 features (cell nucleus characteristics)
- Binary classification
Algorithms Implemented:
- Logistic Regression
- Support Vector Machines (SVM)
- Decision Trees
- Random Forest
Key Features:
- ✅ Exploratory Data Analysis (EDA)
- ✅ Feature correlation analysis
- ✅ Model comparison and evaluation
- ✅ Confusion matrix visualization
- ✅ ROC curve and AUC scores
- ✅ Feature importance analysis
Medical Application: Early cancer detection support system
File: 02_knn_classifier.ipynb
Objective: Implement and optimize KNN for pattern recognition tasks
KNN Concepts Covered:
- Distance metrics (Euclidean, Manhattan, Minkowski)
- K-value optimization
- Decision boundary visualization
- Curse of dimensionality
Implementation:
- ✅ Custom KNN from scratch
- ✅ scikit-learn KNN comparison
- ✅ Parameter tuning (n_neighbors, weights, metric)
- ✅ Performance evaluation
- ✅ Visualization of decision regions
Use Cases:
- Classification tasks
- Recommendation systems
- Anomaly detection
File: 03_kernel_ridge_regression.ipynb
Objective: Perform nonlinear regression using kernel methods
Kernels Implemented:
- Linear kernel
- Polynomial kernel
- RBF (Radial Basis Function) kernel
Key Concepts:
- ✅ Ridge regression basics
- ✅ Kernel trick for nonlinearity
- ✅ Regularization parameter tuning
- ✅ Overfitting prevention
- ✅ Model complexity vs performance trade-off
Applications:
- Nonlinear relationship modeling
- Time series prediction
- Function approximation
File: 04_data_preprocessing.ipynb
Objective: Master essential data preprocessing techniques
Techniques Covered:
1. Data Cleaning:
- Handling missing values (imputation strategies)
- Outlier detection and treatment
- Duplicate removal
2. Feature Scaling:
- StandardScaler (z-score normalization)
- MinMaxScaler (0-1 normalization)
- RobustScaler (outlier-resistant)
3. Feature Encoding:
- One-Hot Encoding (categorical variables)
- Label Encoding
- Ordinal Encoding
4. Feature Engineering:
- Polynomial features
- Feature interaction
- Dimensionality reduction (PCA)
5. Data Splitting:
- Train/validation/test splits
- Stratified sampling
- Cross-validation setup
Best Practices:
- ✅ Pipeline creation with scikit-learn
- ✅ Preventing data leakage
- ✅ Reproducibility with random seeds
- ✅ Scalable preprocessing workflows
File: 05_ml_algorithms_lab.ipynb
Objective: Hands-on exploration of various ML algorithms
Algorithms Compared:
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Gradient Boosting
- Naive Bayes
- SVM
Analysis:
- ✅ Algorithm strengths and weaknesses
- ✅ Performance benchmarking
- ✅ Computational complexity
- ✅ Interpretability vs accuracy trade-offs
- ✅ When to use which algorithm
File: 06_comprehensive_ml_project.ipynb
Objective: End-to-end machine learning workflow from data to deployment-ready model
Complete Pipeline:
-
Problem Definition
- Business understanding
- Success metrics
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Data Collection & EDA
- Data loading and inspection
- Statistical analysis
- Visualization
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Data Preprocessing
- Cleaning and transformation
- Feature engineering
- Train/test split
-
Model Selection
- Algorithm comparison
- Baseline model establishment
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Model Training
- Hyperparameter tuning
- Cross-validation
- Model optimization
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Model Evaluation
- Performance metrics
- Error analysis
- Model interpretation
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Model Deployment Preparation
- Model serialization (pickle/joblib)
- Performance documentation
- Inference pipeline
Real-World Skills:
- ✅ Production-ready code structure
- ✅ Logging and monitoring
- ✅ Model versioning
- ✅ Documentation best practices
- Supervised vs Unsupervised Learning
- Bias-Variance Tradeoff
- Overfitting and Underfitting
- Training, Validation, and Test Sets
- Classification Metrics: Accuracy, Precision, Recall, F1-Score, AUC-ROC
- Regression Metrics: MSE, RMSE, MAE, R²
- Cross-Validation: K-Fold, Stratified K-Fold
- Confusion Matrix analysis
- Data preprocessing pipelines
- Feature scaling and normalization
- Handling imbalanced datasets
- Model selection and comparison
- Hyperparameter optimization
- Code reproducibility
- Accuracy: >95% on test set
- Precision/Recall: Balanced for medical diagnosis
- Best Model: Random Forest with optimized hyperparameters
- Optimal K: Determined through cross-validation
- Performance: High accuracy on structured data
- Insights: Distance metric selection impact
- End-to-End Pipeline: Successfully implemented
- Model Ready: Serialized for deployment
- Documentation: Production-ready code quality
Uzair Mubasher - BSAI Graduate
This project is licensed under the MIT License - see the LICENSE file for details.
- scikit-learn documentation and community
- UCI Machine Learning Repository
- Course instructors and mentors
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