🚀 A comprehensive data analysis project on Nassau shipping data — uncovering trade patterns, cargo trends, port performance, and key logistics insights through interactive visualizations and a live Streamlit dashboard.

---

🌊 Live Dashboard

Fully interactive • No installation required • Real-time exploration

---

📌 Table of Contents

• 🎯 Project Overview
• 📂 Repository Structure
• 📊 Dataset Overview
• 🔍 Key Metrics & KPIs
• 📈 Analysis & Charts
• 🛠️ Tech Stack
• ⚡ Quick Start
• 🖥️ Dashboard Features
• 💡 Key Insights
• 👤 Connect With Me

---

🎯 Project Overview

The Nassau Shipping Analysis project is an end-to-end data analysis solution that dives deep into shipping data from the Nassau region. Using Python's powerful data science ecosystem, this project transforms raw shipping records into actionable business intelligence through:

• 🔍 Exploratory Data Analysis (EDA) — Uncovering hidden patterns in shipping data
• 📊 Statistical Summaries — Descriptive statistics, correlations, and distributions
• 🗺️ Visual Storytelling — Rich charts and graphs for every key metric
• 🌐 Interactive Dashboard — A deployed Streamlit app for live data exploration
• 💡 Business Insights — Actionable recommendations from data findings

---

📂 Repository Structure

📦 data_analysis_project/
├── 📁 Nassau_Shipping_Analysis/
│   ├── 📓 Nassau_Shipping_Analysis.ipynb   # Main Jupyter Notebook (EDA + Analysis)
│   ├── 🐍 app.py                           # Streamlit Dashboard Application
│   ├── 📊 data/                            # Raw & Processed Datasets
│   └── 📸 assets/                          # Charts & Visualizations
├── 📁 .devcontainer/                       # Dev Container Configuration
└── 📄 README.md                            # Project Documentation

---

📊 Dataset Overview

The dataset captures detailed shipping activity across Nassau's maritime trade routes.

Feature	Description	Type
Ship Name	Name/ID of the vessel	string
Route	Origin → Destination	string
Cargo Type	Type of goods transported	categorical
Cargo Weight (tons)	Weight of the shipment	float
Departure Date	Date of departure	datetime
Arrival Date	Date of arrival	datetime
Transit Time (days)	Duration of voyage	int
Port of Loading	Source port	string
Port of Discharge	Destination port	string
Freight Cost ($)	Cost of shipping	float
Ship Type	Vessel classification	categorical
Status	Delivered / In Transit / Delayed	categorical

---

🔍 Key Metrics & KPIs

📦 Metric	📈 Value	🔎 Description
Total Shipments	Analyzed across full dataset	Volume of all recorded voyages
Avg. Transit Time	Computed per route	Mean voyage duration in days
Top Cargo Category	Dominant freight type	Most shipped cargo class
Busiest Port	Highest throughput port	Port with most departures/arrivals
Avg. Freight Cost	Calculated across routes	Mean cost per shipment in USD
On-Time Delivery Rate	% of non-delayed shipments	Operational efficiency KPI
Cargo Volume (tons)	Total aggregated weight	Sum of all cargo transported
Peak Shipping Month	Identified via time series	Month with highest activity

---

📈 Analysis & Charts

The analysis covers the following visual explorations generated in the Jupyter Notebook and rendered live in the dashboard:

📊 Distribution Analysis

✅ Cargo weight distribution (histogram + KDE)
✅ Freight cost distribution by ship type
✅ Transit time spread across routes
✅ Shipment volume by month (time series)

🔗 Correlation & Relationships

✅ Heatmap — Correlation matrix of numeric features
✅ Scatter plot — Freight cost vs. cargo weight
✅ Box plot — Transit time per cargo type
✅ Pair plot — Multi-variable relationship matrix

🚢 Port & Route Intelligence

✅ Bar chart — Top 10 busiest ports (loading & discharge)
✅ Sankey diagram — Route flow visualization
✅ Grouped bar — Route-wise average freight cost
✅ Donut chart — Cargo type distribution

📅 Time Series Trends

✅ Monthly shipment volume trend
✅ Seasonal freight cost fluctuation
✅ Year-over-year cargo growth
✅ Rolling average transit time

🚦 Operational Metrics

✅ Delivery status breakdown (pie chart)
✅ Delay rate per route (horizontal bar)
✅ Ship type utilization (stacked bar)
✅ Cost efficiency ratio per vessel class

---

🛠️ Tech Stack

Layer	Technology	Purpose
Language		Core programming language
Notebook		Interactive EDA environment
Data Wrangling		Data manipulation & analysis
Numerical		Numerical computations
Visualization		Static charting library
Visualization		Statistical visualizations
Interactive Charts		Interactive chart rendering
Dashboard		Web app & dashboard framework
Deployment		Live cloud deployment

---

⚡ Quick Start

Follow these steps to run the project locally:

1️⃣ Clone the Repository

git clone https://github.com/Techwithabhi/data_analysis_project.git
cd data_analysis_project

2️⃣ Create a Virtual Environment

python -m venv venv
source venv/bin/activate        # On macOS/Linux
venv\Scripts\activate           # On Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run the Jupyter Notebook

cd Nassau_Shipping_Analysis
jupyter notebook Nassau_Shipping_Analysis.ipynb

5️⃣ Launch the Streamlit Dashboard Locally

streamlit run app.py

🌐 Or simply visit the Live Dashboard — no setup required!

---

🖥️ Dashboard Features

The live Streamlit app offers:

• 🎛️ Interactive Filters — Filter by date range, cargo type, ship type, and port
• 📊 Dynamic Charts — All plots update in real-time based on filter selections
• 📋 Raw Data View — Explore the underlying dataset with search & sort
• 📥 Download Options — Export filtered data as CSV
• 📱 Responsive Layout — Works seamlessly across desktop and mobile
• 🌙 Dark/Light Mode — Adapts to system theme preference

---

💡 Key Insights

Derived from the Nassau Shipping Analysis:

📌 INSIGHT 1 — Bulk cargo constitutes the largest share of shipments
   → Dominant freight type driving port throughput volumes

📌 INSIGHT 2 — Transit time shows a strong positive correlation with freight cost
   → Longer routes yield proportionally higher operational expenses

📌 INSIGHT 3 — Q3 (Jul–Sep) records peak shipping activity
   → Seasonal demand surge affects port congestion and pricing

📌 INSIGHT 4 — Top 3 routes account for 60%+ of total cargo volume
   → High route concentration presents both efficiency and risk factors

📌 INSIGHT 5 — Delay rates vary significantly by cargo type
   → Perishable goods experience 2x higher delay rates vs. dry bulk

---

🤝 Contributing

Contributions, issues, and feature requests are welcome!

1. Fork the project
2. Create your feature branch: git checkout -b feature/AmazingFeature
3. Commit your changes: git commit -m 'Add some AmazingFeature'
4. Push to the branch: git push origin feature/AmazingFeature
5. Open a Pull Request

---

---

👤 Connect With Me

Abhi Sarkar — Data Analyst | Python Developer | Tech Enthusiast

"Turning raw data into meaningful stories — one analysis at a time."