{"id":26234800,"url":"https://github.com/sayed-ashfaq/aviation-accidents-eda","last_synced_at":"2026-04-22T12:08:43.145Z","repository":{"id":281470138,"uuid":"945372475","full_name":"sayed-ashfaq/Aviation-Accidents-EDA","owner":"sayed-ashfaq","description":"This project analyzes aviation accident data to identify trends, contributing factors, and patterns that impact flight safety. 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The goal is to derive insights that can help improve aviation safety and reduce the likelihood of accidents.  \n\n## **Dataset**  \nThe dataset is sourced from the **NTSB aviation accident database** and contains accident records from **1962 to 2023**. It includes details such as:  \n- **Aircraft Type \u0026 Model**  \n- **Accident Date \u0026 Location**  \n- **Weather Conditions**  \n- **Phase of Flight at Accident**  \n- **Injury Severity \u0026 Fatalities**  \n\n### **Dataset Information:**  \n- **Total Records:** 88,889  \n- **Total Features:** 31  \n\n## **Objectives**  \n- **Trend Analysis:** Identify patterns in accident frequency over time.  \n- **Geographical Analysis:** Determine accident-prone regions.  \n- **Aircraft Safety Evaluation:** Analyze which aircraft types and engine types are more prone to accidents.  \n- **Weather \u0026 Flight Phase Impact:** Understand how weather conditions and flight phases affect accident rates.  \n\n## **Exploratory Data Analysis (EDA)**  \n### **Key Insights from EDA:**  \n1. **Accident Trends:**  \n   - Accidents increased significantly after 1980 due to mass production of aircraft.  \n   - Over time, accident rates have declined, but incidents still occur frequently.  \n\n2. **Monthly Trends:**  \n   - Most accidents occur in **June, July, and August**, aligning with heavy travel and monsoon seasons.  \n   - Suggests extra safety measures should be implemented during these months.  \n\n3. **Accident Distribution by Aircraft Type:**  \n   - **Airplanes** have the highest number of accidents, followed by **helicopters and gliders**.  \n\n4. **Impact of Weather Conditions:**  \n   - Accidents occur more frequently in **Visual Meteorological Conditions (VMC)** than **Instrument Meteorological Conditions (IMC)**.  \n   - Suggests reliance on **instrumentation** can enhance safety.  \n\n5. **Flight Phases Prone to Accidents:**  \n   - **Landing and takeoff** phases see the most accidents.  \n   - Indicates that advanced **automated landing systems** could reduce risks.  \n\n## **Machine Learning Scope**  \nWe plan to use **predictive modeling** to estimate accident probability based on historical patterns. Potential models include:  \n- **Logistic Regression**  \n- **Random Forest Classifier**  \n- **Gradient Boosting**  \n\n## **Files in Repository**  \n- `AviationAccidents-EDA.ipynb` → Jupyter Notebook with full analysis  \n- `AviationData.csv` → Raw dataset  \n- `USState_Codes.csv` → Mapping file for state-level analysis  \n\n## **Technologies Used**  \n- **Python** (Pandas, NumPy, Matplotlib, Seaborn, Scikit-Learn)  \n- **Jupyter Notebook**  \n- **Git \u0026 GitHub**  \n\n## **How to Run the Project**  \n1. Clone the repository:  \n   ```bash\n   git clone https://github.com/sayed-ashfaq/Aviation-Accident-EDA.git\n   cd Aviation-Accident-EDA\n   ```\n2. Install required libraries:  \n   ```bash\n   pip install pandas numpy matplotlib seaborn scikit-learn\n   ```\n3. Open the Jupyter Notebook:  \n   ```bash\n   jupyter notebook\n   ```\n4. Run `AviationAccidents-EDA.ipynb` to explore the analysis.  \n\n## **Conclusion \u0026 Recommendations**  \n- **Weather plays a significant role in accidents.** Airlines should enforce stricter rules for flights during high-risk months.  \n- **Automated Instrumentation Systems (IMC) reduce accidents.** Encouraging the use of advanced avionics in small aircraft can improve safety.  \n- **Takeoff and landing remain critical.** Investing in better pilot training and automated landing assistance is crucial.  \n- **Personal aircraft accidents are high.** Stricter maintenance and licensing regulations for private aircraft can mitigate risks.  \n\n## **Disclaimer / Note**  \nThis project is a result of extensive **independent analysis and research**. The **initial observations, trend analysis, and insights were derived through detailed EDA performed by me**. To ensure clarity and a structured presentation, **ChatGPT was used for fact-checking, formatting, and refining the observations into a well-organized report**. This approach allowed me to focus on deep data exploration while leveraging AI to improve readability and structure.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsayed-ashfaq%2Faviation-accidents-eda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsayed-ashfaq%2Faviation-accidents-eda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsayed-ashfaq%2Faviation-accidents-eda/lists"}