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https://github.com/bhiogade/tlc-trip-analysis

NYC Taxi and Limousine Commission (TLC) Trip Analysis
https://github.com/bhiogade/tlc-trip-analysis

data-analysis data-cleaning data-collection data-visualization pandas-python tableau tableau-desktop

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NYC Taxi and Limousine Commission (TLC) Trip Analysis

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README 

        
Project Overview



**Introduction**

This project focuses on performing comprehensive data analytics on trip data. The objective is to utilize a variety of tools and technologies such as Python for programming, Pandas for data manipulation, draw.io for diagramming, and Tableau for data visualization. Through these tools, we aim to derive meaningful insights and trends from the NYC Taxi and Limousine Commission (TLC) dataset.



**Detailed Methodology**



The process of Uber data analytics is systematically divided into several key steps, which are illustrated in the accompanying methodology diagram:



Raw Data Collection:



We begin by gathering raw data, specifically the TLC Trip Record Data, which includes detailed records of yellow and green taxi trips.



Processing Steps:



Data Cleaning and Formatting: The initial raw data is cleaned and formatted to ensure consistency and accuracy.

Missing Value Imputation: Techniques are applied to handle any missing values within the dataset.

Handling Outliers: Outliers are identified and treated to prevent them from skewing the analysis.



Analytical Processing:



SQL Queries: Structured Query Language (SQL) is used to extract specific subsets of data and perform initial analyses.

Pandas and Numpy Operations: Advanced data manipulation and numerical operations are conducted using Pandas and Numpy libraries in Python.



Data Visualization:



 Tableau Dashboards: The processed data is then visualized using Tableau to create interactive and insightful dashboards that effectively communicate the findings.



**Tools and Technologies**



Programming Language:



 Python: Utilized for data manipulation, cleaning, and advanced analytics.



Visualization Tools:



 Tableau: Employed to create comprehensive dashboards for data visualization, enabling interactive exploration of the data.



Diagramming Tools:



 Draw.io: Used for creating process flow diagrams that illustrate the methodology and analytical processes.



Dataset Information

Dataset Used:



TLC Trip Record Data: This extensive dataset includes records of yellow and green taxi trips, capturing detailed information such as pick-up and drop-off dates and times, locations, trip distances, itemized fares, rate types, payment types, and driver-reported passenger counts.

For further details on the dataset, you can visit the following resources:



NYC TLC Trip Record Data - https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page



**Conclusion**



Through the implementation of this project, we have successfully demonstrated the power of data analytics in deriving meaningful insights from NYC Taxi and Limousine Commission (TLC) trip data. By leveraging Python for data manipulation, SQL for data extraction, and Tableau for visualization, we were able to clean, analyze, and visualize complex datasets efficiently. The resulting dashboards provide a comprehensive view of trip patterns, fare structures, and passenger behaviors, which can be utilized for improving operational efficiency, enhancing customer satisfaction, and informing strategic decisions. This project underscores the value of integrating various analytical tools and methodologies to unlock the full potential of big data in the transportation sector.