Programming Languages Trend Analysis

📌 Overview

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.

🎯 Objectives

• 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.

🛠 Tech Stack

• Language: R
• Libraries:
  • dplyr, tidyr, readr → Data wrangling
  • ggplot2 → Visualization
  • stats → Statistical modeling & regression

📊 Key Findings

• Different languages show clear adoption cycles (growth, maturity, decline).
• Polynomial regression provides interpretable short-term forecasts.
• Visualizations highlight popularity shifts and emerging trends.