correlation-heatmap

A data analysis project exploring consumer behavior and sales trends through EDA using Python. Includes visualizations and insights derived from retail shopping data.

This project analyzes a heart disease dataset to identify key risk factors and patterns using data visualization. It reads patient health data from a CSV file and generates 4 professional Seaborn graphs including Age distribution, Cholesterol vs Blood Pressure analysis, Disease count by Sex, and a Correlation Heatmap

Bitcoin vs marchés traditionnels : corrélations dynamiques et visualisations interactives. M2 — MSc DAB (KEDGE), 2025-2026

This project analyzes tumor cell data from 550 patients using Python. It involves data cleaning, exploratory analysis, feature engineering, and machine learning to classify tumors as malignant or benign. Techniques include PCA, logistic regression, and k-fold cross-validation to ensure model accuracy and reliability.

This repository, Composite Visualization of Microsoft Stock Performance (2010-2022) contains visualization of Microsoft Stock performance from 2010 to 2022. Line graph, correlation heatmap, scatter plot, and histogram were produced in this visualization project.

Bioinformatics tasks: DNA-protein translation, Hamming distance, and six visualizations for gene expression & breast cancer data using Pandas, Seaborn, Matplotlib.

Pipeline modular de limpieza e ingenieria de datos (pandas + pydantic + rapidfuzz) para tickets TI/SaaS: outliers, casi-duplicados, filtrado, RandomForest y MLP (ReLU/Tanh) con matrices de confusion

Research-grade statistical visualization on Seaborn: a typed package, an eleven-notebook curriculum and a reproducible figure pipeline adding bootstrap CIs, effect sizes, corrected pairwise tests with annotations, colour-blind palette checks, journal-width figure sizing and provenance-tagged exports.

End-to-end ML workflow on the UCI Cleveland Heart Disease dataset: missing-value imputation (median/mode), Min-Max scaling, EDA with describe() and a correlation heatmap, training Logistic Regression and Random Forest, evaluating with accuracy, and selecting the better model. All work is in Jupyter notebooks + a concise 1-page report.

📊 Analyze and forecast retail sales with historical data using ML techniques, featuring a Streamlit dashboard for insights and visualization.