Life Expectancy Analysis

Source folder: Life-Expendancy-Analysis/

Summary

Project focused on WHO life expectancy data, combining data cleaning, exploratory analysis, clustering, and dimensionality reduction.

Analysis Used

  • Initial and full cleaning steps, including country-name standardization.
  • Country ISO mapping for geographic visualization support.
  • Missingness audits with nullity inspection and imputation workflows.
  • Feature preparation and scaling with StandardScaler.
  • Clustering with KMeans, with silhouette_score checks.
  • World-map choropleth visualization of cluster assignments.
  • Covariance/correlation exploration and PCA for reduced-dimensional structure.
  • Additional classification checks with confusion matrix/report outputs.

Technologies and Methods

  • Python, Jupyter Notebook
  • pandas, numpy
  • missingno, pycountry
  • scikit-learn (StandardScaler, KMeans, PCA, RandomForestClassifier, model metrics)
  • plotly (graph objects + express), matplotlib