https://github.com/rohitinu6/traffic-flow-prediction
This project analyzes and predicts traffic flow patterns using historical data.
https://github.com/rohitinu6/traffic-flow-prediction
data-science eda machine-learning machine-learning-algorithms python traffic-analysis traffic-light traffic-prediction visualization
Last synced: 27 days ago
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This project analyzes and predicts traffic flow patterns using historical data.
- Host: GitHub
- URL: https://github.com/rohitinu6/traffic-flow-prediction
- Owner: rohitinu6
- Created: 2025-02-02T14:14:24.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-06T02:59:25.000Z (8 months ago)
- Last Synced: 2025-02-22T17:37:54.867Z (8 months ago)
- Topics: data-science, eda, machine-learning, machine-learning-algorithms, python, traffic-analysis, traffic-light, traffic-prediction, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 5.04 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Traffic Flow Prediction
## 📌 Project Overview
This project analyzes and predicts traffic flow patterns using historical data. The goal is to provide insights into traffic congestion, peak hours, and future traffic conditions to aid in transportation planning.
## 🚀 Features
- Data preprocessing and exploratory data analysis (EDA)
- Traffic pattern visualization and trend analysis
- Machine learning model implementation for traffic prediction
- Model evaluation and optimization
## 🛠 Tech Stack
- Python
- Pandas, NumPy
- Scikit-learn
- Matplotlib, Seaborn
- Jupyter Notebook
## 💂️ Dataset
The dataset includes:
- **Timestamped Traffic Data**
- **Vehicle Count**
- **Road Conditions**
- **Weather Influence**
## 💊 Machine Learning Models Used
- Linear Regression
- Random Forest Regressor
- Neural Networks (Deep Learning)
## 🔥 Results
The models are evaluated based on mean absolute error (MAE), root mean square error (RMSE), and R-squared metrics to assess predictive performance.
## 🗂️ Repository Structure
```
📂 Traffic-Flow-Prediction
👉📂 data (Dataset & processed data)
👉📂 notebooks (Jupyter Notebooks)
👉📂 models (Trained models)
👉📂 images (Code and Results Screenshots)
👉📄 README.md (Project documentation)
```
## 🖼 Code and Results
Include images of code and results in the `images` folder. Example:
## 📝 How to Run the Project
1. Clone the repository:
```bash
git clone https://github.com/rohitinu6/Traffic-Flow-Prediction.git
```
2. Navigate to the project folder:
```bash
cd Traffic-Flow-Prediction
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Run the Jupyter Notebook or Python scripts to train and test models.
## 🔗 Links
- **GitHub Repository:** [Traffic Flow Prediction](https://github.com/rohitinu6/Traffic-Flow-Prediction.git)
- **Portfolio:** [Rohit Dubey](https://tinyurl.com/dubeyrohit)
- **GitHub Profile:** [rohitinu6](https://github.com/rohitinu6)
- **LinkedIn:** [Rohit Dubey](https://www.linkedin.com/in/rohit-dubey-d/)
- **Twitter/X:** [@rohitdubey003](https://x.com/rohitdubey003)
## 🛣️ Tags
`Machine Learning` `Traffic Prediction` `Data Science` `Python` `EDA`
## 📝 License
This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).
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💡 **For any queries or collaboration opportunities, feel free to connect!** 🚀