Road-accident-data-analytics-using-Excel-and-Power-BI

Table of Contents

1. Overview
2. Project Structure
3. Features
4. How to Use
5. Visualizations and Insights
   • Accident Overview
   • Monthly Trends
   • Weather Conditions
   • Police Force Distribution
   • Urban/Rural Areas
   • Vehicle Types
   • Junction Types
   • Day of the Week
   • Road Type and Conditions
6. Screenshots
   • Dashboard Snapshot
7. Kaggle Dataset
8. Contributing
9. Acknowledgments

1. Overview

This repository contains a Power BI project focused on analyzing road accidents in Kensington and Chelsea during January 2021. The project includes visualizations and insights derived from the provided dataset.

2. Project Structure

• data: Contains the dataset used in the project.
• reports: Holds the Power BI file (.pbix) and any supporting Excel files.
• images: A folder for images used in this README or within the Power BI report.

3. Features

• Interactive visualizations showcasing accident trends.
• Slicers for filtering data by year and accident severity.
• In-depth analysis of factors such as weather conditions, day of the week, and road type.

4. How to Use

Follow these steps to effectively use and explore the project:

1. Clone the Repository:

   • Clone this repository to your local machine using the following command:

     git clone https://github.com/hiteshchinu/Road-accident-data-analytics-using-Excel-and-Power-BI.git

2. Open Power BI Project:

   • Navigate to the reports folder.
   • Open the Power BI project file named Road Accident Dashboard.pbix using Power BI Desktop.

3. Explore Visualizations:

   • Once the project is open, you'll find interactive visualizations and dashboards.
   • Utilize the year slicer and accident severity slicer for dynamic data filtering.

4. Interact with Slicers:

   • Use the year slicer to focus on specific time periods.
   • Utilize the accident severity slicer to filter data based on the severity of accidents.

5. Gain Insights:

   • Explore various visualizations, such as monthly trends, weather conditions, vehicle types, and more.
   • Hover over data points and interact with the visuals to uncover valuable insights.

5. Visualizations and Insights

5.1. Accident Overview

Majority of accidents were categorized as 'Slight,' highlighting a positive trend for overall road safety.

5.2. Monthly Trends

The graphs revealed a consistent pattern, with a slight increase in accidents during the winter months. This insight can guide targeted safety measures during specific times of the year.

5.3. Weather Conditions

Surprisingly, most accidents occurred in 'Daylight,' emphasizing the need for awareness and caution even in optimal conditions.

5.4. Police Force Distribution

Varied distribution indicates collaborative efforts among different police forces, contributing to effective accident management.

5.5. Urban/Rural Areas

Understanding where accidents are more prevalent helps tailor safety measures to specific environments.

5.6. Vehicle Types

Insights into the types of vehicles involved can inform policies and awareness campaigns.

5.7. Junction Types

Notable data on junction types aids in understanding accident-prone areas, guiding urban planning for safer intersections.

5.8. Day of the Week

More accidents on Fridays reveal a potential need for heightened vigilance heading into the weekend.

5.9. Road Type and Conditions

Stacked bar graphs provide a detailed view, assisting in the identification of high-risk road conditions.

6. Screenshots

View a snapshot of the entire Power BI dashboard to get a visual preview of the comprehensive analysis.

6.1 Dashboard Snapshot

7. Kaggle Dataset

Access the original dataset used in this project on Kaggle. Follow this link for additional details and resources related to the dataset.

8. Contributing

If you have suggestions, find issues, or want to contribute, feel free to open an issue or submit a pull request.

9. Acknowledgments

Special thanks to Kaggle for providing the dataset used in this analysis.