🔬 Data Science Projects Portfolio

A comprehensive collection of data science and machine learning projects, tutorials, and real-world applications. This repository contains 29 Jupyter notebooks covering fundamental concepts, advanced algorithms, model evaluation techniques, and practical data analysis projects.

📋 Table of Contents

• Overview
• Repository Structure
• Getting Started
• Project Categories
  • Machine Learning - Regression
  • Machine Learning - Classification
  • Machine Learning - Clustering
  • Ensemble Methods & Neural Networks
  • Model Evaluation & Validation
  • Data Fundamentals
  • Real-World Projects
• Technologies Used
• Prerequisites
• Installation
• Usage
• Key Highlights
• Contributing
• License

🎯 Overview

This repository serves as both a learning resource and a portfolio showcase, demonstrating proficiency in:

• Machine Learning Algorithms: Classification, clustering, and ensemble methods
• Deep Learning: Neural network implementations
• Data Analysis: Exploratory data analysis and visualization
• Model Evaluation: Cross-validation, hyperparameter tuning, and metrics
• Real-World Applications: Environmental studies, real estate analysis, healthcare predictions

📁 Repository Structure

The repository contains standalone Jupyter notebooks organized by topic, making it easy to explore specific areas of interest without dependencies on other files.

🚀 Getting Started

Prerequisites

• Python 3.7+
• Jupyter Notebook or JupyterLab
• Required Python libraries (see Installation)

Installation

1. Clone the repository:

git clone https://github.com/DataDarling/DATA-SCIENCE-projects.git
cd DATA-SCIENCE-projects

2. Create a virtual environment (recommended):

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

3. Install required dependencies:

pip install jupyter numpy pandas matplotlib seaborn scikit-learn scipy

Usage

Launch Jupyter Notebook:

jupyter notebook

Navigate to any notebook and run the cells to see the demonstrations and analyses.

📚 Project Categories

📈 Machine Learning - Regression

Notebook	Description	Dataset	Key Concepts
linear_regression.ipynb	Simple and multiple linear regression from first principles	Synthetic data (1D & 2D)	Matrix-based regression, coefficient calculation, prediction visualization

🎓 Machine Learning - Classification

Fundamental Algorithms:

Notebook	Description	Dataset	Key Concepts
logistic_regression.ipynb	Binary and multiclass logistic regression implementations	Synthetic data	Classification, decision boundaries, sigmoid function
naive_Bayes.ipynb	Implementation of Multinomial, Bernoulli, and Gaussian Naive Bayes	Synthetic text & binary data	Probabilistic classification, Bayes theorem
decision_tree.ipynb	Decision tree classifier with visualization	Iris Dataset	Tree-based learning, feature importance, tree visualization
knn_knearest neighbors.ipynb	k-Nearest Neighbors algorithm	Synthetic data	Distance metrics, k-value selection, instance-based learning
suport_vector_machine_svm.ipynb	SVM with multiple kernel types (linear, polynomial, RBF)	Iris Dataset	Kernel methods, hyperplane optimization, C and gamma parameters

Advanced Classification Applications:

Notebook	Description	Dataset	Key Achievements
Heart Disease Prediction Model using Logistic Regression.ipynb	Medical diagnosis prediction model	Heart Disease Dataset	Healthcare ML application, logistic regression
Breast Cancer Dataset - Model Evaluation and Hyperparameter Tuning.ipynb	Comprehensive model evaluation and optimization	Breast Cancer Dataset	Hyperparameter tuning, model selection, medical ML
Multi-Class Classification and Model Tuning.ipynb	Multi-class problem solving with model comparison	Synthetic data	Model comparison, hyperparameter optimization

🔍 Machine Learning - Clustering

Unsupervised Learning:

Notebook	Description	Dataset	Key Concepts
k_means_clustering.ipynb	K-Means clustering fundamentals	Synthetic blobs	Centroid-based clustering, elbow method
Iris Dataset - k_means clustering.ipynb	K-Means applied to Iris dataset	Iris Dataset	Cluster visualization, centroid analysis
clustering.ipynb	Advanced clustering techniques	Synthetic blobs (300 samples)	GMM, hierarchical clustering (Ward, Complete, Average, Single linkage), dendrograms

🎯 Ensemble Methods & Neural Networks

Notebook	Description	Algorithms	Performance
ensemble_models.ipynb	Boosting methods for classification and regression	AdaBoost, Gradient Boosting	100% accuracy on Iris, comprehensive regression metrics
neural_nets.ipynb	Multi-Layer Perceptron implementation	Neural Network (2-10-1 architecture)	Decision boundaries, ReLU activation, Sigmoid output

📊 Model Evaluation & Validation

Comprehensive Evaluation Techniques:

Notebook	Description	Focus	Key Techniques
evaluation_metrics.ipynb	Complete classification metrics guide	Performance measurement	Confusion matrix, Accuracy, Precision, Recall, F1-score, ROC curves, AUC
cross_validation.ipynb	Cross-validation strategies	Model validation	KFold, LeaveOneOut, LeavePOut, ShuffleSplit, StratifiedKFold
feature_selection.ipynb	Feature importance and selection	Wine dataset analysis	Univariate selection, feature ranking
feature_selection_advanced.ipynb	Advanced feature engineering	Synthetic data	Missing value analysis, tree-based feature importance, Random Forest

🔧 Data Fundamentals

Core Skills & Tools:

Notebook	Description	Focus
numpy_demo.ipynb	NumPy fundamentals	Array operations, formatting, mathematical operations
pandas_demo.ipynb	Pandas library essentials	DataFrame manipulation, data operations
data_visualization.ipynb	Data visualization techniques	Matplotlib, Seaborn plotting
data_preparation.ipynb	Data preprocessing workflow	Data cleaning, transformation
data_quality_report.ipynb	Data quality assessment	Missing values, quality metrics
iris_data_exploration.ipynb	Comprehensive EDA	Statistical summaries, distributions

🌍 Real-World Projects

Applied Data Science:

Project	Description	Domain	Key Insights
weather data analysis.ipynb	Weather pattern analysis	Meteorology	Trend analysis, seasonal patterns
New York Homes and Hotel Listings Data Exploration.ipynb	NYC real estate and hospitality analysis	Real Estate	Price trends, market insights, feature analysis
2020-2025 Southern States Fungi Report.ipynb	Multi-year fungi observation study	Ecology	Species distribution, temporal patterns, regional analysis
georgia fungi observation report sept-oct 24.ipynb	Georgia fungi seasonal report	Environmental Science	Monthly patterns, species identification
V2 Hikes Of Georgia Monthly Fungi Reports.ipynb	Hiking and fungi tracking in Georgia	Outdoor Recreation/Ecology	Geographic analysis, species by location

🛠️ Technologies Used

• Programming Language: Python 3.x
• Data Manipulation: NumPy, Pandas
• Visualization: Matplotlib, Seaborn
• Machine Learning: Scikit-learn
• Deep Learning: Neural network implementations
• Development Environment: Jupyter Notebook
• Statistical Analysis: SciPy

✨ Key Highlights

🏆 Algorithm Coverage

• Regression: Linear regression with matrix-based solutions
• 9 Classification algorithms including SVM, Naive Bayes, Logistic Regression, Decision Trees, KNN, and ensemble methods
• 3 Clustering approaches with various linkage methods and algorithms
• Neural Networks with custom architecture
• Comprehensive evaluation with 4 notebooks dedicated to model validation

📈 Real-World Impact

• Healthcare applications: Heart disease and breast cancer prediction models
• Environmental research: Multi-year fungi observation studies
• Market analysis: Real estate and hospitality data exploration

🎓 Educational Value

• Beginner-friendly: Starts with NumPy and Pandas basics
• Progressive complexity: Moves from simple algorithms to advanced ensemble methods
• Best practices: Demonstrates proper cross-validation, feature selection, and model evaluation
• Well-documented: Each notebook contains explanations and visualizations

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

How to Contribute

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is available for educational and reference purposes. Please check individual datasets for their respective licenses.

📧 Contact

For questions or collaborations, please open an issue in the repository.

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Note: Some notebooks may require specific datasets. Check individual notebooks for dataset sources and requirements.

Last Updated: February 2026