UK Road Accident Analysis

Project Overview

This project analyzes road traffic accident data from Great Britain for the year 2020. The objective is to identify patterns and propose strategies to enhance road safety. The analysis includes data exploration, cleaning, visualization, statistical analysis, and predictive modeling.

Motivation

Understanding the patterns and causes of road traffic accidents is critical for improving road safety. This project aims to leverage data analysis and machine learning techniques to uncover insights that can inform better safety measures and policies.

Data Collection

The data includes records from four tables: accidents, vehicles, casualties, and LSOA for the year 2020. The dataset was sourced from the UK government’s open data portal.

Code and Resources Used

• Python Version: 3.8
• Packages: pandas, matplotlib, seaborn, scikit-learn
• Requirements: pip install -r requirements.txt

Data Exploration

import pandas as pd

# Load the accident data
accidents = pd.read_csv('data/accident_data.csv')

Data Cleaning

from src.data_preprocessing import clean_data

# Clean the accident data
cleaned_accidents = clean_data(accidents)

Data Visualization

Plot the distribution of accidents

plot_accidents(cleaned_accidents)
Statistical Analysis

from src.statistical_analysis import analyze_data

Perform statistical analysis

analysis_results = analyze_data(cleaned_accidents)
Predictive Modeling
from src.predictive_modeling import build_model

Build and evaluate a predictive model

model, evaluation = build_model(cleaned_accidents)