[Code Addition Request]: Traffic Accident Prediction Model using Deep Learning #568
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## Pull Request for Traffic Accident Prediction Model 🛣️ ### Requesting to submit a pull request to the Traffic Accident Prediction Model repository. --- #### Issue Title **Please enter the title of the issue related to your pull request.** *Traffic Accident Prediction Model using Deep Learning* - [x] I have provided the issue title. --- #### Info about the Related Issue **What's the goal of the project?** The aim of the project is to predict the likelihood of traffic accidents using historical data such as accident records, weather conditions, traffic volume, and road characteristics, helping local authorities implement targeted safety measures and improve traffic management strategies. - [x] I have described the aim of the project. --- #### Name **Please mention your name.** *Alolika Bhowmik* - [x] I have provided my name. --- #### GitHub ID **Please mention your GitHub ID.** *alo7lika* - [x] I have provided my GitHub ID. --- #### Email ID **Please mention your email ID for further communication.** *[email protected]* - [x] I have provided my email ID. --- #### Identify Yourself **Mention in which program you are contributing (e.g., WoB, GSSOC, SSOC, SWOC).** *Enter your participant role here.*GSSOC ext 24 - [x] I have mentioned my participant role. --- #### Closes **Enter the issue number that will be closed through this PR.** *Closes: #568 * - [x] I have provided the issue number. --- #### Describe the Add-ons or Changes You've Made **Give a clear description of what you have added or modified.** *I have added an improved evaluation method for the model by implementing new accuracy metrics and visualizations. The changes include updating the model's evaluation function, adding precision-recall curves, and modifying the code to output these visualizations during testing.* - [x] I have described my changes. --- #### Type of Change **Select the type of change:** - [ ] Bug fix (non-breaking change which fixes an issue) - [x] New feature (non-breaking change which adds functionality) - [ ] Code style update (formatting, local variables) - [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected) - [ ] This change requires a documentation update --- #### How Has This Been Tested? **Describe how your changes have been tested.** *The changes have been tested by running the model with the updated evaluation method on the validation dataset. Unit tests were created to check for precision, recall, and F1-score calculations. Additionally, visualizations were generated to verify that the output matches expected behavior.* - [x] I have described my testing process. --- #### Checklist **Please confirm the following:** - [x] My code follows the guidelines of this project. - [x] I have performed a self-review of my own code. - [x] I have commented my code, particularly wherever it was hard to understand. - [x] I have made corresponding changes to the documentation. - [x] My changes generate no new warnings. - [x] I have added tests that prove my fix is effective or that my feature works. - [x] Any dependent changes have been merged and published in downstream modules.
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Project Description
Aim: To develop a deep learning model that predicts the likelihood of traffic accidents based on historical data and various contributing factors, ultimately enhancing road safety.
Dataset: Historical traffic accident data, including features like weather conditions, traffic volume, time of day, and geographical location.
Approach: Implement a Recurrent Neural Network (RNN) model, specifically using Long Short-Term Memory (LSTM) networks. The model will focus on time-based patterns in the traffic accident data to predict future events based on historical data. The project will include exploratory data analysis (EDA) to understand the dataset's characteristics and distributions, ensuring that EDA includes visualizations of the relationships between features and the target variable.
Full Name
Alolika bhowmik
Participant Role
Contributor GSSOC EXT 24
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