Cybercrime-Regulation-Analysis

Data Of Year 2020 to 2024 From Imported From Kaggle Dataset

🌐 Global Cybercrime Regulation Progress Analysis (2020–2024)

📌 Project Overview

This project analyzes global changes in cybercrime regulation frameworks between 2020 and 2024 using structured data analysis techniques.
The goal is to extract policy-level insights that demonstrate real-world analytical thinking suitable for data analyst / cybersecurity internships.

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🎯 Objectives

• Measure country-level regulatory improvement
• Compare progress across regions
• Analyze performance by income level
• Identify convergence trends
• Detect extreme reform outliers

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📂 Dataset Description

1️⃣ cybercrime_regulation_change_20.csv

Country-level comparison between 2020 and 2024.

Key Columns

• score_2020, score_2024
• score_change
• percent_change
• region
• income_level

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2️⃣ global_cybercrime_regulation_in.csv

Year-wise regulation scores for longitudinal validation.

Key Columns

• year
• cybercrime_regulation_score
• region
• income_level

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🧹 Data Cleaning & Preparation

• Replaced infinite percentage values (inf) with valid numeric values
• Validated score change consistency
• Handled missing values
• Ensured dataset alignment between country and year-level data

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📊 Exploratory Analysis Performed

• Descriptive statistics
• Country-level ranking
• Regional aggregation
• Income-level comparison
• Distribution analysis
• Correlation analysis
• Convergence trend analysis

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🔍 Key Insights

Insight 1 — Income-Level Growth Pattern

Low-income countries show higher percentage growth but remain at lower absolute regulation scores, indicating early-stage regulatory development rather than maturity.

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Insight 2 — Regional Saturation Effect

Regions with historically strong governance frameworks (e.g., Europe) show slower growth, primarily due to already high baseline regulation scores.

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Insight 3 — Global Convergence Trend

A strong negative correlation between initial (2020) scores and improvement suggests that lower-scoring countries are improving faster, pointing toward global regulatory convergence.

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Insight 4 — Reform Acceleration Outliers

A small subset of countries demonstrates extreme regulation acceleration, making them potential policy case studies for rapid institutional reform.

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📈 Visualizations

All finalized visualizations are stored in the visuals/ directory:

• Regional improvement bar charts
• Income-level distribution boxplots
• Correlation heatmaps
• Convergence scatter plots

Each visualization is exported as a high-resolution PNG (300 DPI).

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🗂️ Repository Structure

Cybercrime-Regulation-Analysis/ │ ├── Data/ │ ├── cybercrime_regulation_change_20.csv │ ├── global_cybercrime_regulation_in.csv │ ├── Notebook/ │ └── analysis.ipynb │ ├── visuals/ │ ├── region_barplot.png │ ├── income_boxplot.png │ ├── correlation_heatmap.png │ ├── README.md

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🧠 Tools & Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn

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🚀 Future Scope

• Regression modeling for prediction
• Z-score based outlier detection
• Time-series trend extension
• Policy effectiveness benchmarking

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✅ Outcome

This project demonstrates:

• Structured analytical thinking
• Clean data handling
• Insight-driven storytelling
• Portfolio-ready visualization standards

Designed to reflect real data analyst workflows, not academic exercises.