Accident Data Analysis

Project Description:

This project focuses on analyzing road accident data to identify high-risk locations, understand the causes, and observe trends. Instead of using Power BI, the entire analysis is done using Python in Google Colab.

🎯 Objective

1. Identify accident hotspots using location (latitude & longitude)

2. Analyze accident severity trends over time

3. Study patterns based on weather, vehicle type, road type, etc.

4. Provide safety improvement insights using data visualization

🛠 Tools & Skills Used

1. Google Colab

2. Python (Pandas, Seaborn, Plotly, Folium, Matplotlib)

3. Excel/CSV

• Skills: Data Cleaning, Mapping, Visualization, Analysis

📊 Key Insights Visualized

1. Heatmap showing accident-prone areas

2. Trend of accident severity over time

3. Accidents by hour of the day and day of the week

4. Accidents by weather conditions and vehicle type

5. Urban vs Rural accident comparison

📁 Dataset Info

1. The dataset includes details like:

2. Date, Time, Day of Week

3. Junction Details, Road Type, Speed Limit

4. Latitude, Longitude, Weather Conditions

5. Vehicle Type, Number of Vehicles, Severity, etc.

📌 How to Run

1. Open the .ipynb file in Google Colab

2. Upload the dataset when prompted

3. Run all cells to view analysis and charts

📈 Result

This project successfully replaces Power BI dashboards with a Python-based solution. It offers meaningful insights into accident data using visualization and mapping — helpful for road safety improvements.