nyc 311 data analysis

download the GitHub extension for Visual Studio, My project Customer Service Requests Analysis.ipynb, https://github.com/Simplilearn-Edu/Data-Science-with-Python-Project-2-. Park Facility Name (Plain text) - Unspecified With that, I also generated additional features from the timestamps to be able to filter by month, week of the year, day of the week, day of the year, and hour. For the geographic data, I used Leaflet with additional add-ons for custom tiles and layers. Garage Lot Name (Plain text) - Empty Field, Ferry Direction (Plain text) - Empty Field Learn more. School State (Plain text) - Unspecified Sorry, this file is invalid so it cannot be displayed. Learn more. How I Started The app leverages the complaints in this data set to understand what life is truly like in New York City, as told, albeit, indirectly from the people themselves. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. (Hint: Explore the package/module datetime) City of New York. The dashboard has also been published to shinyapps.io where you can interact with the data yourself here! For my Data Visualization project, I took inspiration from Ben Wellington's TED talk on storytelling through data and sought to create an interactive dashboard to unearth any notable insights on 311 complaints for the year of 2015. A data science project which predicts in how much time a service request will be solved. Citywide Data Sharing. City of New York. School Zip (Plain text) - Unspecified Each day, NYC311 receives thousands of requests related to several hundred types of non-emergency services, including noise complaints, plumbing issues, and illegally parked cars. In addition to the heatmap, one can also zoom in and toggle the "clusters" and "top_50" displays to view a more granular distribution of the incidents, upon clicking on the house icon, additional data will appear in a popup: Here I've zoomed in on a particular address in Northern Manhattan that appeared in the top 50 given my filtering criteria. School Number (Plain text) - Unspecified We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. they're used to log you in. These requests are received by NYC311 and forwarded to the relevant agencies such as the police, buildings, or transportation. Complaint Type (Plain text) - Type of the complaint The Official Website of the City of New York. Exploratory Data Analysis(EDA) | Data Analysis on 311, NewYork Service Request Calls Import the required modules and load the data in panda dataframe. NYC is a trademark and service mark of the City of New York. Perform a service request data analysis of New York City 311 calls. Common browsers are included in this page; mention of a specific browser does not imply endorsement or recommendation. Descriptor (Plain text) - Complaint type label (Heating - Heat, Traffic Signal Condition - Controller) they're used to log you in. 2017 All Rights Reserved. If nothing happens, download the GitHub extension for Visual Studio and try again. If nothing happens, download Xcode and try again. Domain: Customer Service, (Perform a service request data analysis of New York City 311 calls). Learn more. The agency responds to the request, addresses it, and then closes it. In this project, I analyzed the year 2014 NYC 311 incident call data that's available on NYC Open data platform. School Name (Plain text) - Unspecified If nothing happens, download GitHub Desktop and try again. You will focus on the data wrangling techniques to understand the pattern in the data and also visualize the major complaint types. Location Type (Plain text) - Type of the location (Residential, Restaurant, Bakery, etc) School or Citywide Complaint (Plain text) - Empty Field Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Park Borough (Plain text) - Categorical (Unspecified, Queens, Brooklyn etc) We chose 311 data for our Final Project for STAT 5702, an Exploratory Data Analysis class at Columbia Spring 2018. Cannot retrieve contributors at this time. You signed in with another tab or window. Moreover, the timescale and complaint types can be filtered on a more granular level to uncover further trends: From this chart filtering by weekday, one can see that complaint volume is considerably lower on the weekends for environmental incidents such as vermin sightings, fallen trees, or sidewalk repairs. The app leverages the complaints in this data set to understand what life is truly like in New York City, as told, albeit, indirectly from the people themselves. Are the type of complaint or service requested and location related? You can always update your selection by clicking Cookie Preferences at the bottom of the page. You will focus on the data wrangling techniques to understand the pattern in the data and also visualize the major complaint types. The number provides access to non-emergency municipal services. The data sets are available in a variety of machine-readable formats and are refreshed when new data becomes available. City (Plain text) - City for the location Closed Date (Date and Time) - The date and time on which the complaint is closed I found developing this Shiny App to be a deeply rewarding experience since I was not only working with a real-world, messy dataset but could also produce tangible results in a dashboard that can be easily extended to other types of analyses. This data is very interesting to explore having attributes such as zip code, borough, latitude and longitude in addition to details about the requests. Unique Key (Plain text) - Unique identifier for the complaints With that, this visualization tool could be used to further investigate a given address when considering a relocation or event in an area and determine whether it has historically received a large number of complaints.

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