✈️ Global Air Freight Analysis: Trends, Leaders and Growth Patterns (2005–2019)

🔗 Quick Links

Resource	Link
🚀 Live Dashboard	your-streamlit-link-here
📓 Kaggle Notebook	your-kaggle-link-here
📊 Dataset	World Bank WDI

---

📌 Overview

This project analyzes 15 years of global air transport data across 230 countries from the World Bank World Development Indicators. It covers freight volume, passenger traffic, carrier departures, and logistics infrastructure quality scores.

---

🎯 Objectives

• Identify top countries by air freight volume
• Analyze global air freight trends over 15 years
• Explore relationship between infrastructure quality and freight performance
• Compare passenger traffic vs freight across countries
• Forecast freight demand using Machine Learning
• Cluster countries by air transport profile

---

📁 Project Structure

air-cargo-analysis/ │ ├── data.csv # Raw dataset (World Bank WDI) ├── dashboard.py # Streamlit dashboard ├── air-cargo-project.ipynb # Kaggle notebook ├── requirements.txt # Python dependencies └── README.md # Project documentation

---

📊 Dataset

Property	Detail
Source	World Bank World Development Indicators
Countries	230
Years	2005 – 2019
Metrics	4
Format	CSV

Metrics

Column	Description
departures	Registered carrier departures worldwide
freight_mton_km	Air freight in million ton-km
passengers	Air passengers carried
lpi_infra_score	Logistics infrastructure quality (1=low, 5=high)
freight_yoy_growth	Year-over-year freight growth rate
freight_efficiency	Freight per departure ratio
freight_lag1	Freight volume previous year
freight_lag2	Freight volume 2 years ago

---

🚀 Project Phases

✅ Phase 1 — Data Cleaning

✅ Phase 2 — Exploratory Data Analysis

✅ Phase 3 — Feature Engineering

✅ Phase 4 — Modelling

✅ Phase 5 - Dashboard

---

🔑 Key Findings

• USA, China and Germany dominate global air freight
• Global air freight showed consistent growth from 2005 to 2019
• Strong positive correlation exists between departures and freight volume
• Countries with higher LPI scores tend to handle more freight
• Random Forest outperforms Linear Regression for freight prediction
• K-Means clustering reveals 4 distinct country profiles by air transport activity

---

🛠️ Tools & Libraries

Tool	Purpose
Python	Core programming language
Pandas	Data manipulation
NumPy	Numerical operations
Matplotlib	Data visualization
Seaborn	Statistical visualization
Scikit-learn	Machine learning & clustering
Streamlit	Interactive dashboard

---

▶️ How to Run

Run Dashboard Locally

1. Clone the repository

git clone https://github.com/muhammadumermehmood/air-cargo-analysis.git
cd air-cargo-analysis

2. Install dependencies

pip install -r requirements.txt

3. Run the dashboard

streamlit run dashboard.py

---

👤 Author

Muhammad Umer Mehmood

• 💼 LinkedIn: Muhammad Umer Mehmood
• 🎯 Role: Aspiring Data Analyst

---