{"id":24829570,"url":"https://github.com/mamtapanda088/uber-data-analysis","last_synced_at":"2026-04-15T18:31:28.908Z","repository":{"id":274904844,"uuid":"924449335","full_name":"mamtapanda088/uber-data-analysis","owner":"mamtapanda088","description":"Uber data analysis involves studying trip trends, peak hours, popular locations, fare patterns, and visualizing insights using Python.","archived":false,"fork":false,"pushed_at":"2025-01-30T02:53:00.000Z","size":113,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-26T02:11:20.981Z","etag":null,"topics":["jupyter-notebook","matplotlib","numpy","pandas","python3","seaborn"],"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/mamtapanda088.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":"2025-01-30T02:47:34.000Z","updated_at":"2025-01-30T03:01:38.000Z","dependencies_parsed_at":"2025-01-30T03:35:31.510Z","dependency_job_id":null,"html_url":"https://github.com/mamtapanda088/uber-data-analysis","commit_stats":null,"previous_names":["mamtapanda088/uber-data-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mamtapanda088/uber-data-analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mamtapanda088%2Fuber-data-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mamtapanda088%2Fuber-data-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mamtapanda088%2Fuber-data-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mamtapanda088%2Fuber-data-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mamtapanda088","download_url":"https://codeload.github.com/mamtapanda088/uber-data-analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mamtapanda088%2Fuber-data-analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31854646,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-15T15:24:51.572Z","status":"ssl_error","status_checked_at":"2026-04-15T15:24:39.138Z","response_time":63,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["jupyter-notebook","matplotlib","numpy","pandas","python3","seaborn"],"created_at":"2025-01-30T23:26:27.254Z","updated_at":"2026-04-15T18:31:28.848Z","avatar_url":"https://github.com/mamtapanda088.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Uber Data Analysis: Step-by-Step Guide\nObjective\nThe goal of this analysis is to explore Uber trip data to derive meaningful insights, identify trends, and make data-driven decisions.\n\nSteps and Procedure\n1. Data Collection\nObtain the dataset: Uber provides open-source trip data, or you can use a publicly available dataset from sources like Kaggle.\nExample of data fields:\nDate/Time: Date and time of the trip.\nLat, Lon: Latitude and longitude of pickup.\nBase: Uber base code.\n2. Data Preprocessing\nLoad the Dataset: Use Python libraries like pandas to load the data into a DataFrame.\nHandle Missing Values:\nCheck for null or missing values using df.isnull().sum().\nDrop or fill missing values depending on their impact on analysis.\nConvert Datatypes:\nConvert date columns into a datetime object for easier manipulation (pd.to_datetime).\nRemove Duplicates:\nEnsure there are no duplicate rows to maintain data integrity.\n3. Exploratory Data Analysis (EDA)\nBasic Statistics:\nUse df.describe() to get insights into numerical features.\nFeature Engineering:\nExtract new columns from Date/Time:\nHour, Day, Weekday, Month.\nExample:\npython\nCopy\nEdit\ndf['hour'] = df['Date/Time'].dt.hour\ndf['day'] = df['Date/Time'].dt.day\ndf['weekday'] = df['Date/Time'].dt.weekday\ndf['month'] = df['Date/Time'].dt.month\nData Visualization:\nPlot trip frequency across time (hours, days, weekdays) using matplotlib or seaborn.\nAnalyze pickup locations using geographic visualizations (folium or plotly).\n4. Data Visualization\nTime-based Trends:\nIdentify peak hours and days using bar plots or line charts.\nExample: Which hours have the most rides?\nGeospatial Analysis:\nVisualize pickup density using heatmaps.\nLibraries: folium, plotly.express.\nBase Code Analysis:\nCheck which Uber base (e.g., B02512) had the highest number of trips.\n5. Insights and Findings\nSummarize key observations from the analysis:\nPeak usage hours (e.g., morning rush hours).\nPopular pickup locations.\nSeasonal trends (e.g., increase in rides during holidays or weekends).\nExample Insights:\nMost rides occur during weekends and evenings.\nHigh demand is concentrated in central city areas.\n6. Conclusion and Recommendations\nUse insights to make recommendations:\nSuggest increasing the number of drivers during peak hours.\nHighlight areas for promotional offers based on high demand.\n7. Tools and Libraries Used\nProgramming Language: Python\nLibraries:\npandas: Data manipulation and analysis.\nmatplotlib, seaborn: Data visualization.\nfolium, plotly: Geospatial analysis.\nnumpy: Numerical computations.\n8. Future Scope\nIncorporate weather data to understand its effect on ride demand.\nPerform predictive modeling to forecast ride demand.\nAnalyze driver performance and customer satisfaction ratings.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmamtapanda088%2Fuber-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmamtapanda088%2Fuber-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmamtapanda088%2Fuber-data-analysis/lists"}