spydann/Road-Accidents-Analysis

Description: Analyzed road accident data from the Nigeria Bureau of Statistics for the four quarters of 2023 to identify patterns, trends, and contributing factors to road accidents. The goal was to develop targeted strategies for accident prevention and mitigation.

Merged and concatenated individual quarterly tables with additional data on accident causes, covering all 36 states in Nigeria.

Conducted extensive exploratory data analysis to uncover key insights, including the identification of the second quarter as having the highest number of accident cases.

Found that over 70% of accidents were caused by speed violations, signal light violations, wrongful overtaking, and tire bursts, with commercial vehicles being the most involved.

Developed regression models to analyze accident severity, revealing strong correlations between fatal accidents and factors such as speed violations and tire bursts.

Data Collection and Preparation: Integrated data from multiple sources, merging quarterly reports with additional cause-specific data to create a comprehensive dataset.

Exploratory Data Analysis (EDA): Identified critical patterns, such as the second quarter having the highest accident cases and 60% of accidents being fatal.

Accident Severity Analysis: Used regression models to analyze accident severity, finding significant contributors like fatigue, speed violations, and mechanical deficiencies.

Visualization: Created dynamic visualizations using Power BI to present key findings, aiding in strategic decision-making.

Recommendations: Proposed actionable strategies, including driver education programs, strengthened enforcement of traffic laws, infrastructure improvements, and public awareness campaigns.

Driver behavior was identified as a major contributor to road accidents, with significant impacts from speeding, fatigue, and mechanical failures.

Fatal accidents were strongly correlated with speed violations and tire bursts.

The analysis provided a foundation for targeted interventions to improve road safety.

Tools: Python, Power BI

Techniques: Data Cleaning, Regression Analysis, Data Visualization

Outcome: The analysis led to actionable insights and recommendations aimed at reducing road accident fatalities and improving public safety through targeted interventions and strategic decision-making.