palmer-penguins

Demonstrating F# applications in Data Science — functional data analysis, cleaning, and visualization using Deedle & Plotly.NET. Explore how F#'s type safety, pipelines, and pattern matching create elegant data science workflows.

An interactive Streamlit web application that uses a Random Forest classifier to predict penguin species based on real-time user inputs.

A reproducible exploratory data analysis (EDA) of the Palmer Penguins dataset using R. Features automated plot generation, data cleaning, and species differentiation insights.

Exploratory data analysis and statistical tests on the 1973 New York AirQuality dataset and the Palmer Penguins dataset — visualizations, hypothesis testing, and insights.

🐧 Comprehensive EDA & Executive AI/Analytics Dashboard on the Palmer Penguins dataset. Features Simpson's Paradox visual audit, bias-aware cleaning, bilingual UI (EN/ES), and LLM evaluation rubrics.

Analisi statistica e predittiva end-to-end sul dataset Palmer Penguins: data cleaning, inferenza e regressione lineare (OLS) in Python. Progetto finale del corso "Elementi di Data Analytics" — Immaginazione e Lavoro.

Data cleaning, preprocessing, and class balancing of the Palmer Penguins dataset using Python (pandas, seaborn, scikit-learn, imbalanced-learn). Includes handling missing values, outliers, encoding, visualization, and SMOTE.

PPDE (Palmer Penguins Dashboard Explorer) — an interactive R Shiny dashboard for exploring the Palmer Penguins dataset through a CRISP-DM-inspired workflow

Dockerized machine learning classification pipeline using scikit-learn and the Palmer Penguins dataset.

Seaborn-powered exploratory data analysis on the Palmer Penguins dataset — uncovering Simpson's Paradox, sexual dimorphism, and a two-feature species classifier hidden in the correlations.

Predict Palmer Penguins species with TabFM, Google's tabular foundation model — repeated random splits with accuracy and confusion-matrix reporting

用 Python 对 Palmer Penguins 数据集进行从清洗到可视化的完整分析。

Manual ML experiment tracking and Git-based MLOps workflow using Decision Tree experiments on the Palmer Penguins dataset.

A Penguin Prediction Model

python automation project using Palmer Penguins data