NYC Taxi Data Analysis 🚖📊

# NYC Taxi Data Analysis 🚖📊

This repository contains a Jupyter Notebook that analyzes NYC taxi trip data using NumPy. The dataset (`nyc_taxis.csv`) contains information about taxi rides, including trip distance, fare amount, and payment methods.

## 📌 Features & Analysis
- **Load Data:** Import and preprocess NYC taxi trip data.
- **Speed Calculation:** Compute average speed of taxi rides.
- **Ride Count in February:** Filter and count rides that happened in February.
- **Expensive Rides:** Count rides with fare amounts greater than $50.
- **Credit Card Transactions:** Count rides paid via credit cards.

## 📂 Files
- `analysis.ipynb` - Jupyter Notebook with all the analysis.
- `nyc_taxis.csv` - The dataset containing taxi ride details.

## 🛠️ Installation & Usage
1. Clone the repository:
   ```sh
   git clone https://github.com/your-username/nyc-taxi-analysis.git

2. Navigate to the project directory:

   cd nyc-taxi-analysis

3. Install dependencies:

   pip install numpy jupyter

4. Run the Jupyter Notebook:

   jupyter notebook

🖥️ Code Overview

Load Data

import numpy as np
taxi = np.genfromtxt('nyc_taxis.csv', delimiter=',', skip_header=True)

Calculate Average Speed

speed = taxi[:, 7] / (taxi[:, 8] / 3600)
mean_speed = speed.mean()
print(mean_speed)

Filter Rides in February

rides_feb = taxi[taxi[:, 1] == 2, 1]
print(rides_feb.shape[0])

Count Expensive Rides

expensive_rides = taxi[taxi[:, -3] > 50, -3].shape[0]
print(expensive_rides)

Count Credit Card Payments

credit_card_rides = taxi[taxi[:, 6] == 2, 6].shape[0]
print(credit_card_rides)

📊 Data Insights

• The average taxi speed is approximately 32.24 mph.
• There were 13,333 rides in February.
• 16 rides had fares exceeding $50.
• 11,832 rides were paid using credit cards.

🤝 Contributing

Feel free to fork this repo and contribute by improving analysis or adding visualizations!

📜 License

This project is open-source and available under the MIT License.

This README provides:
✅ Clear description 
✅ Code snippets  
✅ Setup guide  
✅ Insights from analysis