This project analyzes worldwide usage trends of 9 programming languages (2004–2022) using statistical methods in R. It applies polynomial regression to forecast future adoption and visualizes results with clear plots.
- Collect and clean historical data of programming language usage (2004–2022).
- Apply descriptive statistics and trend analysis.
- Use polynomial regression to model and forecast adoption trends.
- Visualize trends for comparison across languages.
- Language: R
- Libraries:
dplyr,tidyr,readr→ Data wranglingggplot2→ Visualizationstats→ Statistical modeling & regression
- Different languages show clear adoption cycles (growth, maturity, decline).
- Polynomial regression provides interpretable short-term forecasts.
- Visualizations highlight popularity shifts and emerging trends.