{"id":17633157,"url":"https://github.com/bhiogade/tlc-trip-analysis","last_synced_at":"2025-03-30T03:33:44.242Z","repository":{"id":245548311,"uuid":"818461467","full_name":"bhiogade/TLC-Trip-Analysis","owner":"bhiogade","description":"NYC Taxi and Limousine Commission (TLC) Trip Analysis","archived":false,"fork":false,"pushed_at":"2024-06-25T10:30:04.000Z","size":16507,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-05T05:45:04.402Z","etag":null,"topics":["data-analysis","data-cleaning","data-collection","data-visualization","pandas-python","tableau","tableau-desktop"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bhiogade.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-06-21T23:10:17.000Z","updated_at":"2024-06-25T10:30:08.000Z","dependencies_parsed_at":"2024-06-22T18:32:10.594Z","dependency_job_id":"5c6e82e5-408a-4d2b-b273-0240b364b8e7","html_url":"https://github.com/bhiogade/TLC-Trip-Analysis","commit_stats":null,"previous_names":["bhiogade/tlc-trip-analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bhiogade%2FTLC-Trip-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bhiogade%2FTLC-Trip-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bhiogade%2FTLC-Trip-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bhiogade%2FTLC-Trip-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bhiogade","download_url":"https://codeload.github.com/bhiogade/TLC-Trip-Analysis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246273536,"owners_count":20750904,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-cleaning","data-collection","data-visualization","pandas-python","tableau","tableau-desktop"],"created_at":"2024-10-23T01:47:34.223Z","updated_at":"2025-03-30T03:33:44.209Z","avatar_url":"https://github.com/bhiogade.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1\u003eProject Overview\u003c/h1\u003e\n\n**Introduction**\nThis 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.\n\n\n**Detailed Methodology**\n\nThe process of Uber data analytics is systematically divided into several key steps, which are illustrated in the accompanying methodology diagram:\n\nRaw Data Collection:\n\nWe begin by gathering raw data, specifically the TLC Trip Record Data, which includes detailed records of yellow and green taxi trips.\n\nProcessing Steps:\n\nData Cleaning and Formatting: The initial raw data is cleaned and formatted to ensure consistency and accuracy.\nMissing Value Imputation: Techniques are applied to handle any missing values within the dataset.\nHandling Outliers: Outliers are identified and treated to prevent them from skewing the analysis.\n\nAnalytical Processing:\n\nSQL Queries: Structured Query Language (SQL) is used to extract specific subsets of data and perform initial analyses.\nPandas and Numpy Operations: Advanced data manipulation and numerical operations are conducted using Pandas and Numpy libraries in Python.\n\nData Visualization:\n\n Tableau Dashboards: The processed data is then visualized using Tableau to create interactive and insightful dashboards that effectively communicate the findings.\n\n**Tools and Technologies**\n\nProgramming Language:\n\n Python: Utilized for data manipulation, cleaning, and advanced analytics.\n\nVisualization Tools:\n\n Tableau: Employed to create comprehensive dashboards for data visualization, enabling interactive exploration of the data.\n\nDiagramming Tools:\n\n Draw.io: Used for creating process flow diagrams that illustrate the methodology and analytical processes.\n\nDataset Information\nDataset Used:\n\nTLC 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.\nFor further details on the dataset, you can visit the following resources:\n\nNYC TLC Trip Record Data - https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page\n\n\n**Conclusion**\n\nThrough 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.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbhiogade%2Ftlc-trip-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbhiogade%2Ftlc-trip-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbhiogade%2Ftlc-trip-analysis/lists"}