Applied Machine Learning Portfolio

Welcome to my Applied Machine Learning Portfolio. This repository showcases a comprehensive collection of end-to-end Machine Learning pipelines, ranging from robust Exploratory Data Analysis (EDA) to advanced algorithmic implementation, evaluation, and MLOps deployment.

All projects in this repository are written in clean, modular Python and are designed to solve real-world classification and regression problems using industry-standard libraries like scikit-learn, pandas, numpy, and matplotlib.

---

📂 Project Directory

Project	Description	Key Technologies & Skills
01_Titanic_Survival_Analysis	An end-to-end binary classification pipeline predicting passenger survival. Highlights extensive EDA, handling missing data, and baseline Logistic Regression modeling.	EDA, Data Imputation, Logistic Regression
02_Telecom_Churn_Prediction	Features two distinct pipelines: A Logistic Regression classifier utilizing PCA for dimensionality reduction, and a Ridge Regression model exploring feature scaling.	PCA, Ridge Regression, Feature Scaling
03_Heart_Disease_Prediction	A highly structured predictive pipeline engineered utilizing scikit-learn's Pipeline and ColumnTransformer to enforce strict data transformations and prevent data leakage.	Pipelines, ColumnTransformer, GridSearchCV
04_Pulsar_Star_Classification	Focuses on rigorous model evaluation on highly imbalanced astronomical data. Uses Stratified K-Fold Cross Validation, Learning Curves, and statistical T-Tests.	Stratified K-Fold, Learning Curves, T-Tests
05_Decision_Tree_From_Scratch	A custom, pure-Python implementation of the ID3 Decision Tree Algorithm built from mathematical scratch utilizing Shannon Entropy and Information Gain.	NumPy, Information Theory, Algorithm Design
06_Breast_Cancer_Classification	Compares the diagnostic capabilities of an advanced Ensemble model (Random Forest) against a baseline linear model. Programmatically extracts and plots feature importances.	Ensemble Models, Random Forest, Diagnostics
07_Cardiovascular_Disease_Prediction	Analyzes 70,000 patient records. Involves deep data cleaning (removing physiological outliers), calculating BMI, and exhaustively tuning Gradient Boosting models.	Data Cleaning, Gradient Boosting, GridSearchCV
08_Breast_Cancer_Deployment_Pipeline	An MLOps focused project. Implements SMOTE for severe class imbalances, evaluates 6 architectures (including SVMs and Neural Networks), and serializes the final models (.pkl) via joblib for web deployment.	SMOTE, MLOps, Model Serialization (joblib)

---

🛠️ Technology Stack

• Core Data Science: Python, NumPy, Pandas
• Machine Learning Algorithms: Scikit-Learn (Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, SVM, KNN, Gaussian Naive Bayes, MLP Neural Networks)
• Data Engineering & Imbalance: Imbalanced-Learn (SMOTE), ColumnTransformer, Pipeline, StandardScaler
• Model Tuning & Evaluation: GridSearchCV, Stratified K-Fold CV, ROC-AUC, Precision/Recall optimization.
• Data Visualization: Matplotlib, Seaborn

🚀 How to Use

Each project directory is fully self-contained. Navigate to any project folder and run its respective .py script to automatically preprocess the datasets, train the models, and generate the diagnostic visualizations.

cd 08_Breast_Cancer_Deployment_Pipeline
python breast_cancer_deployment_pipeline.py

📬 Contact

Feel free to explore the code, and if you have any questions or opportunities, you can reach out to me via my GitHub profile.