Machine Learning Projects in Python

A comprehensive collection of machine learning projects covering classification, regression, clustering, dimensionality reduction, and model optimization techniques. Each project includes complete implementations with detailed notebooks demonstrating end-to-end machine learning workflows.

📚 Table of Contents

• Classification Projects
• Regression Projects
• Clustering Projects
• Dimensionality Reduction
• Model Optimization
• Data Analysis and Preprocessing
• Text Classification
• Blog Tutorials

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Classification Projects

Support Vector Machines (SVM)

Cancer Cell Prediction | Classification/cancer-prediction/

• Binary classification of cell samples as benign or malignant
• Implements SVM with RBF and linear kernels
• Demonstrates handling non-numerical data and label encoding
• Evaluation using confusion matrix, F1-score, and Jaccard index
• Dataset: Cell samples with morphological features

Breast Cancer Prediction | Classification/evaluation-techniques/

• Comprehensive evaluation techniques for classification models
• Uses scikit-learn's built-in breast cancer dataset
• Compares multiple evaluation metrics and visualization techniques

Logistic Regression

Customer Churn Prediction | Classification/customer-churn-prediction/

• Predicts customer churn in telecommunications
• Feature scaling and regularization techniques
• Coefficient interpretation for business insights
• Handles class imbalance with appropriate metrics
• Dataset: ChurnData.csv

Softmax (Multinomial Logistic) Regression | Classification/Softmax(Multinomial Logistic) Regression/

• Multi-class classification using softmax regression
• Compares One-vs-Rest and One-vs-One strategies
• Decision boundary visualization
• Dataset: Iris dataset

Decision Trees

Patient Drug Prediction | Classification/drug-prediction/

• Predicts appropriate medication based on patient characteristics
• Label encoding for categorical features
• Decision tree visualization and interpretation
• Achieves 98.33% accuracy
• Dataset: drug200.csv

K-Nearest Neighbors (KNN)

Customer Service Tier Prediction | Classification/service-tier-prediction/

• Classifies customers into service tier categories
• Demonstrates distance metrics and K value selection
• Dataset: teleCust1000t.csv

Ensemble Methods

Random Forest Evaluation | Classification/california-housing/

• House price prediction using Random Forest regression
• Feature importance analysis
• Residual analysis and model diagnostics
• Comprehensive evaluation metrics (MAE, MSE, RMSE, R²)
• Dataset: California Housing dataset

Credit Card Fraud Detection | Classification/fraud-detection/

• Handles severely imbalanced datasets (99.8% vs 0.2%)
• Implements Decision Trees and SVM
• Sample weighting and stratified splitting
• ROC-AUC evaluation for imbalanced data
• Dataset: creditcard.csv (284,807 transactions)

Multi-Class Classification

Obesity Risk Prediction | Classification/obesity-risk-prediction/

• Multi-class classification for obesity risk levels
• Feature engineering and preprocessing
• Dataset: Obesity_level_prediction_dataset.csv

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Regression Projects

Linear Regression

CO2 Emission Prediction | Simple and Mutiple Linear Regression/co2-emission/

• Simple and multiple linear regression comparison
• Predicts vehicle CO2 emissions from engine characteristics
• Feature relationship analysis and coefficient interpretation
• Demonstrates improvement from single to multiple features
• Dataset: FuelConsumptionCo2.csv

Housing Price Prediction | Simple and Mutiple Linear Regression/housing_prediction/

• California housing price prediction
• Multiple feature regression analysis
• Model evaluation and residual analysis
• Dataset: housing.csv

Real Estate Price Prediction | Simple and Mutiple Linear Regression/real-estate-price-prediction/

• Real estate market analysis and price prediction
• Feature engineering for property characteristics
• Dataset: real_estate_data.csv

Taxi Tip Prediction | Simple and Mutiple Linear Regression/taxi-tip-prediction/

• Predicts taxi tip amounts from trip characteristics
• Large-scale dataset analysis
• Dataset: yellow_tripdata_2019-06.csv

Regularization

Regularization Techniques | Simple and Mutiple Linear Regression/regularization/

• Ridge, Lasso, and Elastic Net regularization
• Prevents overfitting in linear models
• Demonstrates bias-variance tradeoff

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Clustering Projects

K-Means Clustering

Customer Segmentation | Clustering/customer-segmentation/

• Unsupervised customer grouping for marketing
• K-Means++ initialization strategy
• Feature normalization and cluster interpretation
• Business applications and insights
• Dataset: Cust_Segmentation.csv

Evaluating K-Means | Clustering/evaluating-kmeans/

• Optimal cluster number selection
• Elbow method, Silhouette score, and Davies-Bouldin index
• Comprehensive cluster quality assessment

Density-Based Clustering

DBSCAN vs HDBSCAN | Clustering/DBSCAN-HDBSCAN/

• Compares density-based clustering algorithms
• Geospatial clustering of museum locations across Canada
• Handles clusters of arbitrary shapes and identifies outliers
• Coordinate system transformations and basemap overlays
• Dataset: ODCAF_v1.0.csv (Canadian cultural facilities)

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Dimensionality Reduction

Principal Component Analysis (PCA)

PCA Implementation | Dimensionalty Reduction Algorithms/PCA/

• Reduces high-dimensional data while preserving variance
• Explained variance analysis and component interpretation
• Visualizing Iris dataset in 2D
• Both conceptual understanding and practical application

Non-Linear Methods

t-SNE and UMAP | Dimensionalty Reduction Algorithms/t-SNE and UMAP/

• Advanced visualization techniques for high-dimensional data
• Compares t-SNE and UMAP performance
• Parameter tuning (perplexity, min_dist)
• Understanding local vs global structure preservation

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Model Optimization

Hyperparameter Tuning

GridSearchCV | GridSearchCV/

• Systematic hyperparameter optimization
• Cross-validation for robust evaluation
• Parallelization for efficiency
• SVM hyperparameter tuning example (C, gamma, kernel)
• Dataset: Iris dataset

Machine Learning Pipelines

Pipeline with GridSearchCV | Machine Learning Pipeline/

• Prevents data leakage with proper workflows
• Combines preprocessing and modeling
• Stratified K-Fold cross-validation
• End-to-end workflow automation
• Dataset: Iris dataset

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Data Analysis and Preprocessing

Exploratory Data Analysis

Adult Census Income Analysis | Preprocessing and EDA/

• Comprehensive EDA workflow demonstration
• Missing value detection and handling strategies
• Statistical analysis and visualization techniques
• Cross-tabulation and relationship analysis
• Feature encoding (one-hot encoding)
• Normalization and standardization
• Correlation analysis and feature selection
• Dataset: adult-modified-09-13-2025.csv (32,561 samples)

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Text Classification

Document Classification

Newsgroup Classification with KNN and Rocchio | Document Classification/

• Custom KNN implementation from scratch
• Euclidean vs Cosine distance for text data
• TF-IDF weighting for improved accuracy
• Rocchio (nearest centroid) method
• Comparison of custom vs scikit-learn implementations
• Multiple classifier comparison (KNN, Decision Trees, Naive Bayes, LDA)
• Dataset: Newsgroups (800 training, 200 test documents, 5,500 features)

Census Data Classification | Part of Document Classification/

• Predictive modeling on census data
• Feature normalization and scaling
• Hyperparameter tuning for KNN
• Overfitting analysis
• Comparison of multiple algorithms

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Blog Tutorials

Detailed tutorial blog posts are available in the blogs:

1. Cancer Prediction with SVM - Understanding kernel functions and medical ML
2. Customer Churn with Logistic Regression - Regularization and business applications
3. CO2 Prediction with Linear Regression - Simple to multiple regression
4. Customer Segmentation with K-Means - Unsupervised learning for business
5. Drug Prediction with Decision Trees - Interpretable healthcare ML
6. Credit Card Fraud Detection - Handling imbalanced datasets
7. DBSCAN vs HDBSCAN - Density-based clustering for geospatial data
8. PCA for Dimensionality Reduction - Understanding variance and components
9. Hyperparameter Tuning with GridSearchCV - Systematic optimization
10. Machine Learning Pipelines - Professional workflow automation
11. EDA and Preprocessing - Comprehensive data preparation guide
12. Document Classification - Text classification with KNN and Rocchio
13. Softmax Regression - Multi-class classification strategies
14. t-SNE and UMAP - Visualizing high-dimensional data
15. Random Forest Evaluation - Regression model diagnostics

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Technologies Used

• Python 3.x
• scikit-learn - Core ML library
• pandas - Data manipulation and analysis
• numpy - Numerical computing
• matplotlib - Data visualization
• seaborn - Statistical visualization
• plotly - Interactive visualizations
• UMAP - Dimensionality reduction
• HDBSCAN - Hierarchical density-based clustering
• geopandas - Geospatial data analysis

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Project Structure

machine-learning-python/
├── Classification/           # Classification projects
├── Clustering/              # Clustering projects
├── Simple and Multiple Linear Regression/  # Regression projects
├── Dimensionalty Reduction Algorithms/     # PCA, t-SNE, UMAP
├── Document Classification/  # Text classification
├── GridSearchCV/            # Hyperparameter tuning
├── Machine Learning Pipeline/  # Pipeline implementations
├── Preprocessing and EDA/   # Data analysis workflows
├── blogs/                   # Tutorial blog posts
└── Cheetsheet/             # Reference materials

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Key Learning Outcomes

Classification

• Binary and multi-class classification
• Kernel methods (SVM)
• Ensemble methods (Random Forest)
• Handling imbalanced datasets
• Model evaluation metrics

Regression

• Simple and multiple linear regression
• Regularization techniques
• Feature engineering
• Residual analysis
• Model diagnostics

Clustering

• Partitioning methods (K-Means)
• Density-based methods (DBSCAN, HDBSCAN)
• Cluster evaluation
• Unsupervised learning applications

Dimensionality Reduction

• Linear methods (PCA)
• Non-linear methods (t-SNE, UMAP)
• Visualization techniques
• Feature extraction

Best Practices

• Data preprocessing and cleaning
• Train/test splitting strategies
• Cross-validation techniques
• Preventing data leakage
• Hyperparameter optimization
• Model interpretation

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Getting Started

Each project folder contains:

• Jupyter notebooks with complete implementations
• Datasets (or links to datasets)
• Detailed comments and explanations
• Visualizations and results

To run any project:

# Clone the repository
git clone https://github.com/yourusername/machine-learning-python.git

# Navigate to a project folder
cd machine-learning-python/Classification/cancer-prediction

# Open the notebook
jupyter notebook cancer_cell_prediction.ipynb

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Contributing

These projects represent hands-on learning from various machine learning concepts. Each notebook includes:

• Step-by-step implementation
• Explanation of concepts and techniques
• Insights from actual experimentation
• Best practices and common pitfalls

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License

This project is licensed under the terms included in the LICENSE file.

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Acknowledgments

Projects developed through practical machine learning experience, combining academic learning with real-world problem-solving. Special focus on understanding not just how algorithms work, but when and why to use them.