https://github.com/vikkiezdev/ai-global-index-analysis

This project analyzes the AI readiness of 62 countries using key indicators like government strategy, commercial activity, research, development, and infrastructure. Through data cleaning, EDA, and visualization, it identifies key drivers of AI adoption and competitiveness.
https://github.com/vikkiezdev/ai-global-index-analysis

cleaning-data correlation-analysis eda matplotlib numpy pandas python3 seaborn statistical-analysis

Last synced: 7 months ago
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This project analyzes the AI readiness of 62 countries using key indicators like government strategy, commercial activity, research, development, and infrastructure. Through data cleaning, EDA, and visualization, it identifies key drivers of AI adoption and competitiveness.

• Host: GitHub
• URL: https://github.com/vikkiezdev/ai-global-index-analysis
• Owner: VikkiezDev
• Created: 2025-03-16T18:05:17.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2025-03-16T18:59:14.000Z (7 months ago)
• Last Synced: 2025-03-16T19:28:48.029Z (7 months ago)
• Topics: cleaning-data, correlation-analysis, eda, matplotlib, numpy, pandas, python3, seaborn, statistical-analysis
• Language: Python
• Homepage:
• Size: 1.05 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# AI-Global-Index-Analysis

- Data Source: https://www.kaggle.com/datasets/katerynameleshenko/ai-index

## **1. Introduction**

Artificial Intelligence (AI) is a key driver of economic and technological growth. This report analyzes the AI Global Index dataset, which benchmarks 62 countries based on AI-related factors like Talent, Infrastructure, Research, and Investment. The goal is to uncover key insights and propose actionable recommendations to bridge the AI development gap between emerging and developed economies.

## **2. Data Cleaning & Preparation**

- No missing values were found in the dataset.

- No duplicate records were detected.

- Data types were verified and corrected where necessary.

- Countries were categorized into **"Developed"** and **"Emerging"** economies based on their income groups.

## **3. Exploratory Data Analysis (EDA)**

### **3.1 Key Trends & Correlations**

- Strong correlation between **Talent, Research, and Development**, showing that skilled professionals drive AI innovation.

- **Government Strategy and Commercial AI activity** show a positive correlation, indicating that public policies significantly impact AI business initiatives.

- **Infrastructure** has weaker correlation with Government Strategy, meaning AI policy alone does not ensure strong infrastructure.

### **3.2 AI Score Distribution & Global Trends**

- **AI Score is right-skewed**, meaning a few countries dominate AI progress while most nations lag.

- **High-income countries outperform lower-income ones**, reflecting economic disparities in AI capabilities.

- **Liberal democracies have higher AI scores**, suggesting that political openness fosters AI innovation.

- **Europe and the Americas lead in AI**, while Africa struggles with lower scores.

## **4. Scenario Analysis: Bridging the AI Divide**

### **4.1 AI Performance: Developed vs. Emerging Economies**

- **Developed nations consistently outperform emerging ones** in all AI indicators.

- **Biggest gaps:** Research, Development, and Talent availability.

- **Smaller but crucial gaps:** Government Strategy and Infrastructure.

- Emerging economies need to **prioritize AI research funding, enhance education, and improve infrastructure** to catch up.

### **4.2 AI Readiness Gaps: Radar Analysis**

A comparative radar chart highlighted that:

- **Developed nations excel in Research, Talent, and Development**.

- **Emerging economies struggle across the board**, particularly in AI innovation.

- **Targeted policies** are necessary to close these gaps.

## **5. Actionable Recommendations**

### **5.1 Talent Development**

- **Establish AI-focused university programs** with industry collaboration.

- **Offer AI scholarships and coding bootcamps** to upskill professionals.

- **Encourage AI research exchanges** with global leaders.

### **5.2 Infrastructure & Investment**

- **Governments should invest in AI-ready cloud infrastructure.**

- **Provide tax benefits to AI startups and foreign investors.**

- **Improve access to high-speed internet and electricity stability.**

### **5.3 Research & Innovation**

- **Increase AI research funding in universities and startups.**

- **Encourage AI collaborations with leading tech hubs.**

- **Develop AI sandboxes for businesses to test and innovate safely.**

## **6. Additional Plots**

- AI Research vs. AI Development - Highlights the correlation between research efforts and AI development.

- Infrastructure vs. AI Score - Examines how infrastructure impacts a country's overall AI readiness.

## **7. Conclusion**

This analysis highlights the stark differences in AI development between developed and emerging economies. By prioritizing **talent development, infrastructure improvements, and AI research investments**, emerging countries can bridge the AI gap and drive sustainable innovation.