Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/mohini1403/road_accident_data_analytics
This project aims to analyze road accident data to gain insights into the factors contributing to accidents, identify patterns, and propose data-driven recommendations for improving road safety. The dataset used in this project contains information about various aspects of road accidents, such as location, time, weather conditions, and severity.
https://github.com/mohini1403/road_accident_data_analytics
analytics data-visualization pandas powerbi
Last synced: 7 days ago
JSON representation
This project aims to analyze road accident data to gain insights into the factors contributing to accidents, identify patterns, and propose data-driven recommendations for improving road safety. The dataset used in this project contains information about various aspects of road accidents, such as location, time, weather conditions, and severity.
- Host: GitHub
- URL: https://github.com/mohini1403/road_accident_data_analytics
- Owner: MOHINI1403
- Created: 2023-12-24T10:22:09.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2023-12-24T16:18:03.000Z (11 months ago)
- Last Synced: 2023-12-25T11:26:20.137Z (11 months ago)
- Topics: analytics, data-visualization, pandas, powerbi
- Homepage:
- Size: 672 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Road Accident Data Analytics Project
## Overview
This project aims to analyze road accident data to gain insights into the factors contributing to accidents, identify patterns, and propose data-driven recommendations for improving road safety. The dataset used in this project contains information about various aspects of road accidents, such as location, time, weather conditions, and severity.
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Data Sources](#data-sources)
- [Data Cleaning](#data-cleaning)
- [Exploratory Data Analysis (EDA)](#exploratory-data-analysis-eda)
- [Data Visualization](#data-visualization)
- [Statistical Analysis](#statistical-analysis)
- [Machine Learning Models](#machine-learning-models)
- [Results and Insights](#results-and-insights)
- [Future Enhancements](#future-enhancements)
- [Contributing](#contributing)
- [License](#license)
## Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/road-accident-data-analytics.git
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Navigate to the project directory:
```bash
cd road-accident-data-analytics
```
2. Run the main analysis script:
```bash
python analyze_accident_data.py
```
3. View the generated visualizations and insights in the output folder.
## Data Sources
The project utilizes the road accident dataset sourced from [provide data source information]. The dataset includes information on accident location, date, time, weather conditions, road type, and severity.
## Data Cleaning
The raw dataset undergoes a thorough cleaning process to handle missing values, outliers, and inconsistencies. The cleaned data is used for subsequent analysis.
## Exploratory Data Analysis (EDA)
EDA is performed to understand the distribution of variables, identify correlations, and explore patterns within the data. Descriptive statistics and visualizations are used to derive initial insights.
## Data Visualization
The project employs various data visualization techniques to present key findings effectively. Matplotlib, Seaborn, and Plotly are used to create informative charts, graphs, and maps.
## Statistical Analysis
Statistical tests are conducted to validate hypotheses and identify significant factors contributing to road accidents. This includes regression analysis, hypothesis testing, and correlation studies.
## Machine Learning Models
Machine learning models, such as decision trees or logistic regression, are employed to predict accident severity based on relevant features. The models are trained and evaluated using appropriate metrics.
## Results and Insights
The project concludes with a summary of findings, actionable insights, and recommendations for improving road safety. Visualizations and statistical evidence support the key takeaways.
## Future Enhancements
Future enhancements may include:
- Real-time data integration for more timely analysis.
- Integration with geographical information systems (GIS) for advanced spatial analysis.
- Continuous model improvement using updated data.
## Contributing
Contributions are welcome! If you would like to contribute to this project, please follow our [Contribution Guidelines](CONTRIBUTING.md).
## License
This project is licensed under the [MIT License](LICENSE). Feel free to use, modify, and distribute the code for your own projects.