BTR Analysis is a project aimed at highlighting the criticality of precautionary measures for road accidents. Leveraging Data Analysis, Machine Learning, and Web Development, this project integrates these aspects into a cohesive platform that displays and analyzes crucial data related to road safety.
- Introduction
- Contributors
- Project Structure
- Features
- Installation
- Usage
- Technologies Used
- Acknowledgments
The primary objective of BTR Analysis is to emphasize the significance of proactive measures in mitigating road accidents. Through the utilization of various technologies such as Data Analysis, Machine Learning algorithms, and Web Development tools, this project amalgamates datasets and presents insights into road safety statistics.
The project consists of several components:
- HTML Pages: index.html, column.html, bar3.html, map.html, model.html
- Stylesheets: CSS files for styling
- JavaScript: Script files for functionality and interactivity
- Machine Learning Model: Included in model.html, offering predictive analysis
- Data Visualization: Utilizes Chart.js for state-wise and year-wise analysis.
- Geographical Analysis: Provides insights into accident-prone areas through an embedded map.
- Machine Learning Integration: Includes a machine learning model to predict accidents based on historical trends, which is an API built in Flask
- Informative Interface: Offers information on primary causes of accidents and precautionary measures.
- Clone the repository:
git clone https://github.com/sanj16/btranalysis - Open the HTML files in a web browser.
- Navigate through the different HTML pages to explore various analyses and information presented.
- Use the machine learning model interface to predict accidents based on historical data.
- HTML/CSS/JavaScript
- Chart.js
- Bootstrap
- Axios
- Machine Learning (Polynomial Regression)
- AWS Sagemaker for hosting the dataset and model
- Contributors for their dedicated efforts in building this project
- Render for site hosting / deployment