This repository is a comprehensive collection of machine learning and data science projects I completed as part of my coursework and independent practice. It covers a wide range of techniques – from regression and classification to clustering, dimensionality reduction, and even an interactive Dash app. The repo combines multiple batches of work into one unified portfolio, demonstrating my ability to explore datasets, apply statistical and machine learning models, and present results effectively.
- Machine Learning: Regression, classification, clustering, PCA, time series forecasting
- Deep Learning: PyTorch, TensorFlow
- Data Visualization: Matplotlib, Seaborn, Plotly
- Dashboarding: Dash (Plotly) for interactive apps
- Python Libraries: pandas, numpy, scikit-learn, statsmodels, scipy
- Data Handling: SQL basics, CSV/Excel parsing, JSON
- Model Evaluation: Cross-validation, confusion matrices, ROC/AUC, R², adjusted R², etc.
- Statistical Analysis: Hypothesis testing, correlation analysis, ANOVA
- Version Control & Collaboration: Git, GitHub
- Notebooks & Workflow: Jupyter Notebook, reproducible pipelines