🚗 Accident Survival Data Analysis

📌 Overview

This project performs an in-depth Exploratory Data Analysis (EDA) on an accident survival dataset to identify key factors that influence survival outcomes in road accidents.

The main objective is to understand how variables like speed of impact, safety measures (helmet/seatbelt), age, and gender affect the likelihood of survival.

This analysis helps uncover data-driven insights that can support road safety awareness and decision-making.

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📂 Dataset Information

• File: accident.csv
• Rows: 200
• Columns: 6

Key Features:

• Age
• Gender
• Speed_of_Impact
• Helmet_Used
• Seatbelt_Used
• Survived (Target Variable: 1 = Yes, 0 = No)

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🛠️ Technologies Used

• Python 🐍
• Pandas
• Matplotlib
• Seaborn
• Jupyter Notebook

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🔍 Project Workflow

1. Data Loading

• Loaded dataset using Pandas
• Checked dataset shape, structure, and data types

2. Data Cleaning

• Identified missing values in:
  • Gender
  • Speed_of_Impact
• Handled missing values:
  • Filled Gender with "Unknown"
  • Imputed Speed_of_Impact using mean values

3. Feature Engineering

• Converted Survived from numeric to categorical:

  Survived → Yes / No

🔍 4. Exploratory Data Analysis (EDA)

Performed analysis using:

• Value counts for categorical variables

• Pie charts for:

  • Survival distribution
  • Seatbelt usage among survivors
  • Helmet usage among survivors

• Bar plots for:

  • Survival count

• Comparative analysis:

  • Survivors vs Non-survivors
  • Safety measures impact

• Statistical summaries:

  • Average age
  • Average speed of impact

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📊 Key Insights

• 🚗 Speed Matters:
  Higher speed of impact is associated with lower survival rates.

• 🪖 Safety Measures Save Lives:
  Helmet and seatbelt usage significantly improve survival chances.

• 👥 Demographic Patterns:
  Age and gender show noticeable differences in survival outcomes.

• 📈 Survival Distribution:
  The dataset shows a clear distinction between survivors and non-survivors.

• 📊 Average Trends:
  Survivors tend to have lower average impact speeds compared to non-survivors.

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🧪 Hypothesis Testing

The project explores hypotheses such as:

• Whether speed of impact significantly affects survival
• The impact of helmet and seatbelt usage on survival rates
• Differences in survival across demographic groups

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📌 Visualizations

The notebook includes:

• Pie charts 📊
• Bar graphs 📈
• Count distributions 📉

These visualizations help in understanding relationships between safety measures and survival outcomes.

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🚀 How to Run

1. Clone the repository

git clone https://github.com/Dev1822/Road-Accident-EDA
cd Road-Accident-EDA

2. Install dependencies

pip install pandas matplotlib seaborn

3. Run the notebook

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Made By : https://github.com/Dev1822