Road Accident Analysis PowerBI

The Road Accident Analysis Dashboard is a comprehensive visual tool designed to provide insights and facilitate informed decision-making regarding road safety measures. Developed using Power BI, this dashboard gathers data from a Kaggle Data Set to offer a holistic view of road accidents, their causes, and associated trends. The dashbaord utilizes a variety of key performance indicators (KPIs) to gauge the severity and ramifications of traffic accidents. These metrics encompass the total count of casualties categorized by the severity of the accidents, vehicle types involved, road classifications, and additional contributing factors.

Key Features:

1. Vehicle Type and Impact Analysis: This feature enables users to analyze the types of vehicles involved in accidents and their impact on severity. Insights into collision dynamics, such as vehicle size, speed, and angle of impact, aid in devising vehicle safety standards and infrastructure improvements.

2. Weather and Road Conditions Impact: By integrating weather and road condition data, the dashboard evaluates the influence of environmental factors on accident rates. Users can assess correlations between weather patterns, road conditions, and accident severity to enhance preparedness and response strategies.

3. Accident Trends: Through intuitive charts and graphs, the dashboard presents trends in accident occurrence over time. Users can identify patterns, seasonal variations, and emerging trends to allocate resources effectively and implement preventive measures.

Accomplished Activities:

1. Data Gathering from Kaggle UK Road Accident Data set (https://www.kaggle.com/datasets/devansodariya/road-accident-united-kingdom-uk-dataset).

2. Understanding The Business Case and its Requirements.

3. Data Cleaning: I removed duplicated records, checked the data types of each column, sorted the data, and grouped some data under one group to be used later in the data Visualization stage.

4. Data Processing: I extracted additional details from the cleaned dataset to enhance data analysis capabilities. This included the addition of monthly and yearly columns to facilitate the client's request for monthly trend analysis compared to current and previous years.

5. Data Analysis: I utilized statistical techniques and algorithms to derive insights from the processed dataset. This involved identifying patterns, trends, and correlations within the data to address the objectives outlined in the business case.

6. Data Visualization: I developed visually appealing and informative representations of the analyzed data using charts, graphs, and dashboards. This facilitated clear communication of findings and supported decision-making processes for stakeholders.