Data analysis report and dashboard of the dataset using Power BI
Exploratory data analysis of the mentioned dataset is discussed in the article by Saba Firdous andmodel fitting to explore the relationship between the variables is discussed in the article by Hira Sadiq.
Exploratory Data Analysis(EDA) of France Accident using Power BI
Road accidents are a global scourge, causing loss of life, injuries, and significant economic burdens. Even in developed nations like France, with its extensive road network and diverse traffic conditions, this issue persists with grave consequences. In this article, we use Power BI’s data analytics capabilities to uncover insights into road accidents in France. Our goal is to reveal patterns, causes, and potential solutions to this pressing societal challenge.
Data Source:
The cornerstone of our study lies in a robust dataset obtained from Kaggle. This dataset spans multiple years and boasts a diverse array of variables, including year of Accident, road conditions, weather conditions, vehicle types,Lightning condition,human factors, and accident severity. This richness of data empowers us to conduct a comprehensive analysis capable of unveiling hidden patterns and insights that contribute to road accidents.
Objective
Our main objective is to leverage the capabilities of Power BI for in-depth data analysis. Through this approach, we aim to extract meaningful insights from the dataset, ensuring that our findings are easily understandable and actionable for improving road safety.
Data analysis report and dashboard of the dataset using Power BI
In this report, we have found that accidents are decreasing each year. We reached this conclusion by analyzing the data presented in the Power BI report, which we found to be easy to understand and interactive.The interactive power bi report is here.Report A stacked column chart displaying the count of accidents categorized by year and lighting condition. This chart provides insights into how accident counts vary across different years and under varying lighting conditions.Another pie chart showing the count of accidents by gender, allowing users to understand the gender-based distribution of accidents.A table displaying the data of per month count, helping users analyze the monthly variations in accident occurrences. Another table presenting the count of accidents based on surface conditions, offering insights into how road surface conditions affect accident rates.Additionally, slicers for “Month” and “Year” enable users to dynamically adjust the report to view data for specific time periods. When a particular year is selected, the entire report adjusts to display insights and visualizations for that specific year, allowing for deeper analysis.
Key Insights
The report reveals that accident rates tend to peak during certain months, suggesting a potential need for increased safety measures during those periods.Accidents have generally declined over the years, indicating some success in road safety initiatives.Collisions are the most common type of accident, followed by single-vehicle accidents.Understanding these patterns can inform targeted safety campaigns and infrastructure improvements.The pie chart illustrating accidents by gender highlights any gender-based disparities in accident involvement.The stacked column chart provides a detailed breakdown of accident counts by year and lighting condition.Tables for monthly accident counts and surface condition-based counts offer additional insights into accident patterns.
Conclusion
This report serves as a valuable tool for policymakers, traffic safety professionals, and the general public to gain a deeper understanding of road safety in France. By leveraging the insights provided, stakeholders can make informed decisions to improve road safety measures, reduce accidents, and save lives.
Recommendations
Based on the insights gained from this analysis, it is recommended that relevant authorities consider implementing targeted safety campaigns, enhancing infrastructure in high-accident areas, conducting further investigations into the causes of accidents under specific surface conditions, and addressing any seasonal variations in accident rates.By including tables for monthly accident counts and surface condition-based counts, you provide users with additional data-driven insights and the ability to explore accident trends further in your Power BI report. These tables enhance the granularity of your analysis and allow users to examine the data in more detail.