pdesgrip/final_project

final IronHack boot camp project

New York taxis - examining the data to reduce costs and identifying improvments for service.

Description: This project exmaines New York taxi trip data over the course of several years. We will provide useful insights for the taxi companies.

Areas of investigation:

1. Can we predict the total amount for a given taxi trip based on features like Trip_distance, PULocationID, DOLocationID, tpep_pickup_datetime, tpep_dropoff_datetime, RateCodeID?

2. Can we predict the tip amount a driver will receive based on the total fare, time of day, pickup and dropoff locations, and payment type?

3. Businesses often use clustering to understand their customer base by grouping customers into segments based on purchasing behavior, demographics, etc. Evaluation metrics help determine the quality of these segments, ensuring that marketing strategies can be tailored to each distinct group.

4. Anomaly Detection: Can we identify anomalous trips that might suggest fraudulent activity, like unusually high fares for short distances, or trips with no charge but high tip amounts?

Source of data:

https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page

https://data.cityofnewyork.us/Transportation/NYC-Taxi-Zones/d3c5-ddgc

Findings:

New York yellow taxis are big business with 400M trips in the last 7 years, not bad taking cvoid into account. Interesting shoter trips yeild the highest value tips per distance travelled.

Conclusions : New York Taxis operate efficiently and are extremely busy. The area covered alone by yellow taxis is significant.

Futures ideas/implementations: With more time I would add interactive maps, weather data, and carbon footprint data.