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NYC Taxi Data Analysis

Overview

This project performs an in-depth analysis of NYC yellow taxi trip data to uncover patterns and insights related to trip durations, fare amounts, peak hours, and popular pickup and drop-off locations. The analysis was conducted using PySpark on Databricks to efficiently process and analyze large datasets.

Dataset

  • Source: NYC Yellow Taxi Trip Data (January 2015) on Kaggle
  • Description: This dataset includes various details about yellow taxi trips, such as pickup and drop-off times, trip distances, fare amounts, passenger counts, and pickup and drop-off locations.
  • Key Columns:
    • tpep_pickup_datetime & tpep_dropoff_datetime: Timestamps of when the trip started and ended
    • trip_distance: Distance of the trip in miles
    • fare_amount: Fare charged for the trip
    • passenger_count: Number of passengers on the trip
    • pickup_longitude & pickup_latitude: GPS coordinates of pickup location
    • dropoff_longitude & dropoff_latitude: GPS coordinates of drop-off location

Key Analyses and Insights

  1. Trip Duration Analysis: Examined trip duration trends by hour and day to identify peak times.
  2. Fare Amount Analysis: Analyzed how fare amounts vary based on pickup locations and passenger count.
  3. Hotspot Identification: Mapped the most frequent pickup and drop-off locations, revealing high-demand areas.
  4. Hourly Demand Analysis: Investigated demand patterns throughout the day to understand peak taxi usage hours.
  5. Correlation Analysis: Explored relationships between trip duration, trip distance, and fare amount.

How to Run

Prerequisites

  1. Databricks: Set up a Databricks environment to run PySpark code. Alternatively, use a local PySpark setup.
  2. Required Libraries:
    • PySpark
    • Pandas
    • Matplotlib
    • Seaborn (for visualizations)

Steps

  1. Clone this repository: 
    git clone https://github.com/TrueCodee/NYC-Taxi-Data-Analysis.git
  2. Upload nyc-taxi-data-analysis.ipynb to Databricks or open it in your local Jupyter environment.
  3. Run each code cell in sequence to perform the analysis.

Project Structure

  • NYC-Taxi-Data-Analysis.ipynb: Jupyter Notebook with the complete analysis and visualizations.
  • NYC-Taxi-Data-Analysis - Databricks.pdf: PDF version of the notebook for quick reference.
  • README.md: Project overview and instructions.

Summary of Findings

  • Average Fare by Hour: Fares tend to be higher in the early morning hours (4-6 AM), with the lowest fares around 7 PM, likely due to competition during peak demand times.
  • Trip Duration Patterns: Peak demand occurs between 6 PM and 10 PM, aligning with the most frequent pickup hours.
  • Popular Locations: Certain GPS coordinates show high pickup and drop-off frequencies, identifying hotspots that could inform targeted marketing or service allocation.
  • Fare and Distance Relationship: A moderate correlation exists between trip fare and distance, as expected, while trip duration and fare show a weaker correlation due to variability in traffic and route efficiency.

License

This project is licensed under the MIT License - see the LICENSE file for details.