---
title: Neuro Orion - NYC Taxi Traffic Time Series Anomaly Detection
emoji: 🐨
colorFrom: indigo
colorTo: yellow
sdk: streamlit
sdk_version: "1.41.1"
app_file: src/app.py
pinned: true
---

[![Sync to Hugging Face hub](https://github.com/gdtan02/NeuroOrion_Time_Series_Anomaly_Detection/actions/workflows/main.yml/badge.svg)](https://github.com/gdtan02/NeuroOrion_Time_Series_Anomaly_Detection/actions/workflows/main.yml)

# NYC Taxi Traffic - Time Series Anomaly Detection

## Project Overview

This project is developed for WID3011 Deep Learning Assignment.

SDG 8: Sustainable Cities & Communities:

This problem examines an anomaly detection challenge using the NYC Taxi Traffic dataset, available on Kaggle 
([https://www.kaggle.com/datasets/julienjta/nyc-taxi-traffic]) and provided by the NYC Taxi and Limousine 
Commission. The dataset presents a univariate time series of total taxi passenger counts from July 2014 to January 
2015, aggregated every 30 minutes. It includes five notable anomalies, occurring during the NYC Marathon, 
Thanksgiving, Christmas, New Year’s Day, and a snowstorm. 

The task involves implementing a complete anomaly detection pipeline: analyzing the NYC Taxi Traffic dataset, 
developing a Long Short Term Memory (LSTM) model to detect outliers and anomaly.

**Group Name:**
Neuro Orion

**Group Members:**
1. Poo Wei Chien
2. Tan Guo Dong
3. Tan Zhi Jian
4. Sanjivan A/L Balajawahar
5. Marvin Chin Yi Kai


---
## Acknowledgements

We acknowledge the contributors to the following resources:
- All the members of Neuro Orion for their contributions to the project.
- NYC Taxi Traffic dataset provided by NYC Taxi and Limousine Commission.
- Open-source tools and frameworks like TensorFlow, PyTorch, and Jupyter Notebook.

---

## Installation Guide

Follow these steps to set up the project locally:

### 1. Clone the repository to your local machine:

Run the following command in your terminal:

```bash
git clone https://github.com/gdtan02/NeuroOrion_Time_Series_Anomaly_Detection.git
cd nyc-taxi-anomaly-detection
```

### 2. Set up a Python Virtual Environment (Optional):

You can use `venv` or `conda` to create and activate a virtual environment to manage dependencies.

Using `venv`: 

For Windows user, run the following command:
```commandline
python -m venv venv
venv\Scripts\activate
```

For MacOS/Linux user, run the following command:
```commandline
python3 -m venv venv
source venv/bin/activate
```

Using `conda`:
```commandline
conda create --name nyc-taxi-env python=3.8 -y
conda activate nyc-taxi-env
```

### 3. Install dependencies:

Install all the required dependencies listed in `requirements.txt` file using `pip`: 
```commandline
pip install -r requirements.txt
```

### 4. Install Jupyter Notebook (Optional):

If Jupyter Notebook is not already installed, you can install it using `pip`:

```commandline
pip install notebook
```

Alternatively, if you are using `conda`, you can install Jupyter Notebook using the following command:

```commandline
conda install -c conda-forge notebook
```

### 5: Start Jupyter Notebook
Launch Jupyter Notebook to execute the project code:

```commandline
jupyter notebook
```
A browser window should open, displaying the Jupyter Notebook interface. 
If it does not open automatically, copy and paste the link shown in the terminal into your web browser.

You are now ready to run the project code in the Jupyter Notebook.

---

## Development Setup

### 1: Code Formatting
We use Black for code formatting. To set up:

1. Install black and pre-commit:
```bash
pip install black pre-commit
```

2. Run pre-commit hooks:
```bash
pre-commit install
```

3. Run Black manually:
```bash
black .
```

4. Configure VS Code (optional):
```json
{
    "python.formatting.provider": "black",
    "editor.formatOnSave": true
}
```

Refer to the [Black documentation](https://black.readthedocs.io/en/stable/) for more information. Reference from the article [here](https://dev.to/emmo00/how-to-setup-black-and-pre-commit-in-python-for-auto-text-formatting-on-commit-4kka)

---