license:apache-2.0 size_categories:100K<n<1M format:csv modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
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license: apache-2.0
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# Predictive Analysis of Road Accidents in Western Australia

## Table of Contents

1. [Project Description](#project-description)
2. [Features](#features)
3. [Data Sources](#data-sources)
4. [Installation](#installation)
5. [Usage](#usage)
6. [Modeling Approach](#modeling-approach)
7. [Results](#results)
8. [Contributing](#contributing)
9. [License](#license)
10. [Contact Information](#contact-information)

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### Project Description

This project focuses on predicting road accidents in Western Australia using various datasets, including historical accident data, traffic patterns, weather conditions, and other relevant factors. The analysis aims to identify key predictors of accidents and develop a predictive model to enhance road safety initiatives.

### Features

- Data collection from multiple sources (e.g., accident reports, traffic data).
- Data preprocessing and cleaning.
- Exploratory data analysis (EDA) to identify trends and patterns.
- Predictive modeling using machine learning algorithms.
- Visualization of results and predictions.
- Insights and recommendations for road safety improvements.

### Data Sources

- **Road Accident Data:** [DATA WA](https://catalogue.data.wa.gov.au/)
- **Traffic Volume Data:** [Main Roads Western Australia](https://www.mainroads.wa.gov.au)
- **Weather Data:** [Australian Bureau of Meteorology](http://www.bom.gov.au)
- **Demographic Data:** [Australian Bureau of Statistics](https://www.abs.gov.au)

### Installation

#### Prerequisites

- Python 3.8 or higher
- Required packages (listed in `requirements.txt`)

#### Steps

```bash
# Clone the repository
git clone https://github.com/username/repo.git

# Navigate into the project directory
cd project-directory

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
# For Windows
venv\Scripts\activate
# For macOS/Linux
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt
