Internship ML Case Studies
A portfolio of supervised learning workflows focused on baseline consistency, diagnostic evaluation, and data storytelling.
Scikit-learn
Regression
Classification
Data Visualization
Classification Boundary Analysis
Iris classification via Logistic Regression
Sales Prediction Weights
Multiple Linear Regression Feature Importance
Executive Summary
This internship portfolio combines practical projects covering classification, regression, and trend analysis. The objective was to build strong end-to-end habits: data cleaning, hypothesis-driven exploration, baseline modeling, evaluation, and stakeholder-friendly storytelling.
Methodology Framework
- Data Prep: Profile distributions, handle missing values, and align target features.
- Exploration: Visual checks to identify strong predictive patterns.
- Modeling: Train baseline algorithms (Logistic Regression, Multiple Linear).
- Evaluation: Strict Train/Validation splits and metric behavior analysis.
Key Takeaways & Impact
Moving past academic accuracy metrics, I learned how to frame models around business value.
Interpretability
Explaining feature impact and model behavior beyond just output scores.
Decision Utility
Connecting analytical results to practical recommendations for planning action.
Baseline Reliability
Establishing dependable baselines with clean split strategies before adding complexity.