We have developed an application and solution approach (using this dataset) for automatically generating and suggesting short email responses to support queries in a university environment. Our proposed solution can be used as one tap or one click solution for responding to various types of queries raised by faculty members and students in a university. Office of Academic Affairs (OAA), Office of Student Life (OSL) and Information Technology Helpdesk (ITD) are support functions within a university which receives hundreds of email messages on the daily basis. Email communication is still the most frequently used mode of communication by these departments. A large percentage of emails received by these departments are frequent and commonly used queries or request for information. Responding to every query by manually typing is a tedious and time consuming task. Furthermore a large percentage of emails and their responses are consists of short messages. For example, an IT support department in our university receives several emails on Wi-Fi not working or someone needing help with a projector or requires an HDMI cable or remote slide changer. Another example is emails from students requesting the office of academic affairs to add and drop courses which they cannot do it directly. The dataset consists of emails messages which are generally received by ITD, OAA and OSL in Ashoka University. The dataset also contains intermediate results while conducting machine learning experiments.

We use the Enron email dataset to build a network of email addresses. It contains 614586 emails sent over the period from 6 January 1998 until 4 February 2004. During the pre-processing, we remove the periods of low activity and keep the emails from 1 January 1999 until 31 July 2002 which is 1448 days of email records in total. Also, we remove email addresses that sent less than three emails over that period. In total, the Enron email network contains 6 600 nodes and 50 897 edges.

To build a graph G = (V, E), we use email addresses as nodes V. Every node v_i has an attribute which is a time-varying signal that corresponds to the number of emails sent from this address during a day. We draw an edge e_ij between two nodes i and j if there is at least one email exchange between the corresponding addresses.

Column 'Count' in 'edges.csv' file is the number of 'From'->'To' email exchanges between the two addresses. This column can be used as an edge weight.

The file 'nodes.csv' contains a dictionary that is a compressed representation of time-series. The format of the dictionary is Day->The Number Of Emails Sent By the Address During That Day. The total number of days is 1448.

'id-email.csv' is a file containing the actual email addresses.

DomainIQ is a comprehensive global Domain Name dataset for organizations that want to build cyber security, data cleaning and email marketing applications. The dataset consists of the DNS records for over 267 million domains, updated daily, representing more than 90% of all public domains in the world.

The data is enriched by over thirty unique data points, including identifying the mailbox provider for each domain and using AI based predictive analytics to identify elevated risk domains from both a cyber security and email sending reputation perspective.

DomainIQ from Datazag offers layered intelligence through a highly flexible API and as a dataset, available for both cloud and on-premises applications. Standard formats include CSV, JSON, Parquet, and DuckDB.

Custom options are available for any other file or database format. With daily updates and constant research from Datazag, organizations can develop their own market leading cyber security, data cleaning and email validation applications supported by comprehensive and accurate data from Datazag. Data updates available on a daily, weekly and monthly basis. API data is updated on a daily basis.

Aeslc (Email Subject Generation Task)

A collection of email messages of employees in the Enron Corporation.

By Huggingface Hub [source]

About this dataset

The AESLC (Automatic Extraction of Semantically-Linked Corporate Communications) dataset provides a unique and captivating glimpse into the lives of Enron employees - from the perspective of communications sent via emails during a period between 1999 to 2004. These anonymous emails not only provide fascinating insight into the daily professional activities, interactions, and relationships within Enron employees, but also offer an educational opportunity for those interested in further exploring corporate communication. Containing such features as email body and subject lines, researchers can tap into this invaluable resource to research topics surrounding linguistics, sentiment analysis, and data mining. Unlock their secrets by discovering what messages were shared amongst these before the breach of scandal that caused their company’s downfall!

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How to use the dataset

This comprehensive dataset includes anonymized emails sent by then Enron employees in the period of 1999 and 2004. By delving into this unique dataset, you can gain a deeper insight into the lives of former Enron employees as well as their professional activities and relationships.

In this guide, we'll provide a walkthrough on how to use this dataset and make meaningful discoveries from it. Let's get started!

Research Ideas

• Analyzing the connections between Enron employees by tracking their email communications over time to uncover trends and correlations.
• Examining the emails for keywords or topics as a way to classify each email in order to gain better understanding of what Enron employees were discussing and what activities they were engaging in.
• Using sentiment analysis techniques on the emails in order to gain insight into the emotional state of Enron employees at different points in time or during particular events or incidents such as when allegations against Enron emerged

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: validation.csv | Column name | Description | |:-----------------|:--------------------------------------------------------------| | email_body | The body of the email sent by Enron employees. (Text) | | subject_line | The subject line of the email sent by Enron employees. (Text) |

File: train.csv | Column name | Description | |:-----------------|:--------------------------------------------------------------| | email_body | The body of the email sent by Enron employees. (Text) | | subject_line | The subject line of the email sent by Enron employees. (Text) |

File: test.csv | Column name | Description | |:-----------------|:--------------------------------------------------------------| | email_body | The body of the email sent by Enron employees. (Text) | | subject_line | The subject line of the email sent by Enron employees. (Text) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Huggingface Hub.

The Reminder extension for CKAN enhances data management by providing automated email notifications based on dataset expiry dates and update subscriptions. Designed to work with CKAN versions 2.2 and up, but tested on 2.5.2, this extension offers a straightforward mechanism for keeping users informed about dataset updates and expirations, promoting better data governance and engagement. The extension leverages a daily cron job to check expiry dates and trigger emails. Key Features: Data Expiry Notifications: Sends email notifications when datasets reach their specified expiry date. A daily cronjob process determines when to send these emails. Note that failure of the cronjob will prevent email delivery for that day. Dataset Update Subscriptions: Allows users to subscribe to specific datasets to receive notifications upon updates via a subscription form snippet that can be included in dataset templates. Unsubscribe Functionality: Includes an unsubscribe link in each notification email, enabling users to easily manage their subscriptions. Configuration Settings: Supports at least one recipient for reminder emails via configuration settings in the CKAN config file. Bootstrap Styling: Intended for use with Bootstrap 3+ for styling, but may still work with Bootstrap 2 with potential style inconsistencies. Technical Integration: The Reminder extension integrates into CKAN via plugins, necessitating the addition of reminder to the ckan.plugins setting in the CKAN configuration file. The extension requires database initialization using paster commands to support the subscription functionality. Setting up a daily cronjob is necessary for the automated sending of reminder and notification emails. Benefits & Impact: By implementing the Reminder extension, CKAN installations can improve data management and user engagement. Automated notifications ensure that stakeholders are aware of dataset expirations and updates, leading to better data governance, and more active user involvement in data ecosystems. This extension provides an easy-to-implement solution for managing data lifecycles and keeping users informed.

DomainIQ is a comprehensive global Domain Name dataset for organizations that want to build cyber security, data cleaning and email marketing applications. The dataset consists of the DNS records for over 267 million domains, updated daily, representing more than 90% of all public domains in the world.

The data is enriched by over thirty unique data points, including identifying the mailbox provider for each domain and using AI based predictive analytics to identify elevated risk domains from both a cyber security and email sending reputation perspective.

DomainIQ from Datazag offers layered intelligence through a highly flexible API and as a dataset, available for both cloud and on-premises applications. Standard formats include CSV, JSON, Parquet, and DuckDB.

Custom options are available for any other file or database format. With daily updates and constant research from Datazag, organizations can develop their own market leading cyber security, data cleaning and email marketing applications supported by comprehensive and accurate data from Datazag. Data updates available on a daily, weekly and monthly basis. API data is updated on a daily basis.

Overview This Extended Golf Play Dataset is a rich and detailed collection designed to extend the classic golf dataset. It includes a variety of features to cover many aspects of data science. This dataset is especially useful for teaching because it offers many small datasets within it, each one created for a different learning purpose.

Core Features: Outlook: Type of weather (sunny, cloudy, rainy, snowy). Temperature: How hot or cold it is, in Celsius. Humidity: How much moisture is in the air, as a percent. Windy: If it is windy or not (True or False). Play: If golf was played or not (Yes or No). Extra Features: ID: Each player's unique number. Date: The day the data was recorded. Weekday: What day of the week it is. Holiday: If the day is a special holiday (Yes or No). Season: Time of the year (spring, summer, autumn, winter). Crowded-ness: How crowded the golf course is. PlayTime-Hour: How long people played golf, in hours. Text Features: Review: What players said about their day at golf. EmailCampaign: Emails the golf place sent every day. MaintenanceTasks: Work done to take care of the golf course. Mini Datasets Collection This dataset includes a special set of mini datasets:

Each mini dataset focuses on a specific teaching point, like how to clean data or how to combine datasets. They're perfect for beginners to practice with real examples. Along with these datasets, you'll find notebooks with step-by-step guides that show you how to use the data. Learning With This Dataset Students can use this dataset to learn many skills:

Seeing Data: Learn how to make graphs and see patterns. Sorting Data: Find out which data helps to predict if golf will be played. Finding Odd Data: Spot data that doesn't look right. Understanding Data Over Time: Look at how things change day by day or month by month. Grouping Data: Learn how to put similar days together. Learning From Text: Use players' reviews to get more insights. Making Recommendations: Suggest the best time to play golf based on past data. Who Can Use This Dataset This dataset is for everyone:

New Learners: It's easy to understand and has guides to help you learn. Teachers: Great for classes on how to see and understand data. Researchers: Good for testing new ways to analyze data.

Original Data Source: ⛳️ Golf Play Dataset Extended

Context

The Astronomer's Telegram (ATel) is an internet-based short notice publication service for quickly disseminating information on new astronomical observations. Telegrams as available instantly on the service's website and distributed to subscribers via email digest within 24 hours.– The Astronomer's Telegram was launched on 17 December 1997 by Robert E. Rutledge with the goal of rapidly (<1 s) sharing information of interest to astronomers. Telegrams are sent out daily by email, but especially time-sensitive events can be transmitted instantly.

Content

The dataset contains two files: 1. Original Text file [Not processed] 2. CSV file with one ATEL in each row [partially processed]

Acknowledgements

My project partner : Adeem

References

The official Astronomer's Telegram website

Version 1.0 - This version is the final revised one.

This is the LamaH-CE dataset accompanying the paper: Klingler et al., LamaH-CE | LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, published at Earth System Science Data (ESSD), 2021 (https://doi.org/10.5194/essd-13-4529-2021).

LamaH-CE contains a collection of runoff and meteorological time series as well as various (catchment) attributes for 859 gauged basins. The hydrometeorological time series are provided with daily and hourly time resolution including quality flags. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with high temporal resolution. LamaH is in its basics quite sililar to the well-known CAMELS datasets for the contiguous United States (https://doi.org/10.5194/hess-21-5293-2017), Chile (https://doi.org/10.5194/hess-22-5817-2018), Brazil (https://doi.org/10.5194/essd-12-2075-2020), Great Britain (https://doi.org/10.5194/essd-12-2459-2020) and Australia (https://doi.org/10.5194/essd-13-3847-2021), but new features like additional basin delineations (intermediate catchments) and attributes allow to consider the hydrological network and river topology in further applications.

We provide two different files to download: 1) Hydrometeorological time series with daily and hourly resolution, which requires decompressed about 70 GB of free disk space. 2) Hydrometeorological time series only with daily resolution, which requires 5 GB. Beyond the temporal resolution of the time series, there are no differences.

Note: It is recommended to read the supplementary info file before using the dataset. For example, it clarifies the time conventions and that NAs are indicated by the number -999 in the runoff time series.

Disclaimer: We have created LamaH with care and checked the outputs for plausibility. By downloading the dataset, you agree that we nor the provider of the used source datasets (e.g. runoff time series) cannot be liable for the data provided. The runoff time series of the German federal states Bavaria and Baden-Württemberg are retrospective checked and updated by the hydrographic services. Therefore, it might be appropriate to obtain more up-to-date runoff data from Bavaria (https://www.gkd.bayern.de/en/rivers/discharge/tables) and Baden-Württemberg (https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer). Runoff data from the Czech Republic may not be used to set up operational warning systems (https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf).

License: This work is licensed with CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ESSD paper, version of dataset and all sources which are declared in the folder "Info"), indicate if and what changes were made and distribute your work under the same public license as the original.

Additional references: We ask kindly for compliance in citing the following references when using LamaH, as an agreement to cite was usually a condition of sharing the data: BAFU (2020), CHMI (2020), GKD (2020), HZB (2020), LUBW (2020), BMLFUW (2013), Broxton et al. (2014), CORINE (2012), EEA (2019), ESDB (2004), Farr et al. (2007), Friedl and Sulla-Menashe (2019), Gleeson et al. (2014), HAO (2007), Hartmann and Moosdorf (2012), Hiederer (2013a, b), Linke et al. (2019), Muñoz Sabater et al. (2021), Muñoz Sabater (2019a), Myneni et al. (2015), Pelletier et al. (2016), Toth et al. (2017), Trabucco and Zomer (2019), and Vermote (2015). These references are listed in detail in the accompanying paper.

Supplements: We have created additional files after publication (therefore non peer-reviewed): 1) Shapefiles for reservoirs (points) and cross-basin water transfers (lines) including several attributes as well as tables with information about the accumulated storage volume and effective catchment area (considerung artificial in- and outflows) for every runoff gauge. 2) Water quality data (e.g. dissolved oxygen, water temperature, conductivity, NO3-N), which are suitable to the gauges. The data for water quality may not be used for commercial purposes. If you are interessted, just send us an email with your name, affiliation and the intended purpose for the requested files to the address listed below. If you find any errors in the dataset, feel free to send us an email to: christoph.klingler@boku.ac.at

Access high-fidelity consumer data powered by our proprietary modeling technology that provides the most comprehensive consumer intelligence, accurate targeting, first-party data enrichment, and personalization at scale. Our deterministic dataset, anchored in the purchasing habits of over 140 million U.S. consumers, delivers superior targeting performance with proven 70% increase in ROAS.

Core Data Assets Transactional Data Foundation: Real purchasing behavior from over 140 million U.S. consumers with 8.5 billion behavioral signals across 250 million adults. Seven years of daily credit card and debit card purchase data aggregated from all major credit cards sourced from more than 300 national banks, capturing $2+ trillion in annual discretionary spending.

Consumer Demographics & Lifestyle: Comprehensive profiles including age, income, household composition, geographic distribution, education, employment, and lifestyle indicators. Our proprietary taxonomy organizes consumer spending across 8,000+ brands and 2,500+ merchants, from major retailers to emerging direct-to-consumer brands.

Behavioral Segmentation: 150+ custom consumer communities including demographic groups (Gen Z, Millennials, Gen X), lifestyle segments (Health & Fitness Enthusiasts, Tech Early Adopters, Luxury Shoppers), and behavioral categories (Deal Seekers, Brand Loyalists, Premium Service Users, Streaming Subscribers). Purchase Intelligence: Deep insights into consumer spending patterns across entertainment, fitness, fashion, technology, travel, dining, and retail categories. Our models identify cross-category purchasing behaviors, seasonal trends, and brand switching patterns to optimize targeting strategies. Advanced Modeling Technology

Our proprietary consumer intelligence engine combines deterministic transaction-based data with Smart Audience Engineering that transforms first-party signals from anonymized website traffic, behavioral indicators, and CRM enrichment into precision-modeled segments. Unlike traditional data providers who sell static lists, our AI-powered predictive modeling continuously learns and optimizes for unprecedented precision and superior conversion outcomes.

Performance Advantages: Audiences built on user-level transactional data deliver 70% increase in ROAS compared to traditional targeting methods. Weekly-optimized audiences with performance narratives eliminate wasted ad spend by 20-30%, while our deterministic AI models analyze hundreds of attributes and conversion-validated signals to identify prospects with genuine purchase intent, not just lookalike behaviors.

Dataset of Elon musk tweets updated and recorded automatically every day starting from September 2, 2021 (due to a limit of Twitter API)

Content tweets only (if the tweet is not a reply of any other tweet)

License

CC0

Original Data Source: Elon Musk Tweets (Updated Daily Automatically)

Analysis of ‘Medallion Drivers - Active’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/1c75e7ca-9626-4f3b-b18e-39c1efbc7f11 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

PLEASE NOTE: This dataset, which includes all TLC Licensed Drivers who are in good standing and able to drive, is updated every day in the evening between 4-7pm. Please check the 'Last Update Date' field to make sure the list has updated successfully. 'Last Update Date' should show either today or yesterday's date, depending on the time of day. If the list is outdated, please download the most recent list from the link below. http://www1.nyc.gov/assets/tlc/downloads/datasets/tlc_medallion_drivers_active.csv

This is a list of drivers with a current TLC Driver License, which authorizes drivers to operate NYC TLC licensed yellow and green taxicabs and for-hire vehicles (FHVs). This list is accurate as of the date and time shown in the Last Date Updated and Last Time Updated fields. Questions about the contents of this dataset can be sent by email to: licensinginquiries@tlc.nyc.gov.

--- Original source retains full ownership of the source dataset ---

Historical FiresLast updated on 06/17/2022OverviewThe national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2021 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer:Alaska fire history USDA FS Regional Fire History Data BLM Fire Planning and Fuels National Park Service - Includes Prescribed Burns Fish and Wildlife ServiceBureau of Indian AffairsCalFire FRAS - Includes Prescribed BurnsWFIGS - BLM & BIA and other S&LData LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.AttributesThis dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer. (This unique identifier may NOT replace the GeometryID core attribute)INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name.FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT).AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin.SOURCE - System/agency source of record from which the perimeter came.DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy.MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; OtherGIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456.UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMPCOMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or UnknownGEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID).Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4580 Records thru 2021), other federal sources for AK data removed. CA: GEOID = OBJECT ID of downloaded file geodatabase (12776 Records, federal fires removed, includes RX)FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2052 Records). Handful of WFIGS (11) fires added that were not in FWS record.BIA: GEOID = "FireID" 2017/2018 data (416 records) provided or WFDSS PID (415 records). An additional 917 fires from WFIGS were added, GEOID=GLOBALID in source.NPS: GEOID = EVENT ID (IRWINID or FRM_ID from FOD), 29,943 records includes RX.BLM: GEOID = GUID from BLM FPER and GLOBALID from WFIGS. Date Current = best available modify_date, create_date, fire_cntrl_dt or fire_dscvr_dt to reduce the number of 9999 entries in FireYear. Source FPER (25,389 features), WFIGS (5357 features)USFS: GEOID=GLOBALID in source, 46,574 features. Also fixed Date Current to best available date from perimeterdatetime, revdate, discoverydatetime, dbsourcedate to reduce number of 1899 entries in FireYear.Relevant Websites and ReferencesAlaska Fire Service: https://afs.ak.blm.gov/CALFIRE: https://frap.fire.ca.gov/mapping/gis-dataBIA - data prior to 2017 from WFDSS, 2017-2018 Agency Provided, 2019 and after WFIGSBLM: https://gis.blm.gov/arcgis/rest/services/fire/BLM_Natl_FirePerimeter/MapServerNPS: New data set provided from NPS Fire & Aviation GIS. cross checked against WFIGS for any missing perimeters in 2021.https://nifc.maps.arcgis.com/home/item.html?id=098ebc8e561143389ca3d42be3707caaFWS -https://services.arcgis.com/QVENGdaPbd4LUkLV/arcgis/rest/services/USFWS_Wildfire_History_gdb/FeatureServerUSFS - https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_FireOccurrenceAndPerimeter_01/MapServerAgency Fire GIS ContactsRD&A Data ManagerVACANTSusan McClendonWFM RD&A GIS Specialist208-258-4244send emailJill KuenziUSFS-NIFC208.387.5283send email Joseph KafkaBIA-NIFC208.387.5572send emailCameron TongierUSFWS-NIFC208.387.5712send emailSkip EdelNPS-NIFC303.969.2947send emailJulie OsterkampBLM-NIFC208.258.0083send email Jennifer L. Jenkins Alaska Fire Service 907.356.5587 send email

PLEASE NOTE: This dataset, which includes all TLC Licensed Drivers who are in good standing and able to drive, is updated every day in the evening between 4-7pm. Please check the 'Last Update Date' field to make sure the list has updated successfully. 'Last Update Date' should show either today or yesterday's date, depending on the time of day. If the list is outdated, please download the most recent list from the link below. http://www1.nyc.gov/assets/tlc/downloads/datasets/tlc_medallion_drivers_active.csv This is a list of drivers with a current TLC Driver License, which authorizes drivers to operate NYC TLC licensed yellow and green taxicabs and for-hire vehicles (FHVs). This list is accurate as of the date and time shown in the Last Date Updated and Last Time Updated fields. Questions about the contents of this dataset can be sent by email to: licensinginquiries@tlc.nyc.gov.

The dataset is a collection of aggregated clinical parameters for the participants (such as clinical scores), parameters extracted from the utilized devices (such as average heart rate per day, average gait speed etc.), and coupled events about them (such as falls, loss of orientation etc.). It contains information which was collected during the clinical evaluation of the older people from medical experts.This information represents the clinical status of the older person across different domains, e.g. physical, psychological, cognitive etc.

The dataset contains several medical features which are used by clinicians to assess the overall state of the older people.

The purpose of the Virtual Patient Model is to assess the overall state of the older people based on their medical parameters, and to find associations between these parameters and frailty status.

A list of the recorded clinical parameters and their description is shown below:

- part_id: The user ID, which should be a 4-digit number

- q_date: The recording timestamp, which follows the “YYYY-MM-DDTHH:mm:ss.fffZ” format (eg. 14 September 2017 12:23:34.567, is formatted as 2019-09-14T12:23:34.567Z)

- clinical_visit: As several clinical evaluations were performed to each older adult, this number shows for which clinical evaluation these measurements refer to

- fried: Ordinal categorization of frailty level according to Fried operational definition of frailty

- hospitalization_one_year: Number of nonscheduled hospitalizations in the last year

- hospitalization_three_years: Number of nonscheduled hospitalizations in the last three years

- ortho_hypotension: Presence of orthostatic hypotension

- vision: Visual difficulty (qualitative ordinal evaluation)

- audition: Hearing difficulty (qualitative ordinal evaluation)

- weight_loss: Unintentional weight loss >4.5 kg in the past year (categorical answer)

- exhaustion_score: Self-reported exhaustion (categorical answer)

- raise_chair_time: Time in seconds to perform a lower limb strength clinical test

- balance_single: Single foot station (Balance) (categorical answer)

- gait_get_up: Time in seconds to perform the 3meters’ Timed Get Up And Go Test

- gait_speed_4m: Speed for 4 meters’ straight walk

- gait_optional_binary: Gait optional evaluation (qualitative evaluation by the investigator)

- gait_speed_slower: Slowed walking speed (categorical answer)

- grip_strength_abnormal: Grip strength outside the norms (categorical answer)

- low_physical_activity: Low physical activity (categorical answer)

- falls_one_year: Number of falls in the last year

- fractures_three_years: Number of fractures during the last 3 years

- fried_clinician: Fried’s categorization according to clinician’s estimation (when missing data for answering the Fried’s operational frailty definition questionnaire)

- bmi_score: Body Mass Index (in Kg/m²)

- bmi_body_fat: Body Fat (%)

- waist: Waist circumference (in cm)

- lean_body_mass: Lean Body Mass (%)

- screening_score: Mini Nutritional Assessment (MNA) screening score

- cognitive_total_score: Montreal Cognitive Assessment (MoCA) test score

- memory_complain: Memory complain (categorical answer)

- mmse_total_score: Folstein Mini-Mental State Exam score

- sleep: Reported sleeping problems (qualitative ordinal evaluation)

- depression_total_score: 15-item Geriatric Depression Scale (GDS-15)

- anxiety_perception: Anxiety auto-evaluation (visual analogue scale 0-10)

- living_alone: Living Conditions (categorical answer)

- leisure_out: Leisure activities (number of leisure activities per week)

- leisure_club: Membership of a club (categorical answer)

- social_visits: Number of visits and social interactions per week

- social_calls: Number of telephone calls exchanged per week

- social_phone: Approximate time spent on phone per week

- social_skype: Approximate time spent on videoconference per week

- social_text: Number of written messages (SMS and emails) sent by the participant per week

- house_suitable_participant: Subjective suitability of the housing environment according to participant’s evaluation (categorical answer)

- house_suitable_professional: Subjective suitability of the housing environment according to investigator’s evaluation (categorical answer)

- stairs_number: Number of steps to access house (without possibility to use elevator)

- life_quality: Quality of life self-rating (visual analogue scale 0-10)

- health_rate: Self-rated health status (qualitative ordinal evaluation)

- health_rate_comparison: Self-assessed change since last year (qualitative ordinal evaluation)

- pain_perception: Self-rated pain (visual analogue scale 0-10)

- activity_regular: Regular physical activity (ordinal answer)

- smoking: Smoking (categorical answer)

- alcohol_units: Alcohol Use (average alcohol units consumption per week)

- katz_index: Katz Index of ADL score

- iadl_grade: Instrumental Activities of Daily Living score

- comorbidities_count: Number of comorbidities

- comorbidities_significant_count: Number of comorbidities which affect significantly the person’s functional status

- medication_count: Number of active substances taken on a regular basis

Networks from SNAP (Stanford Network Analysis Platform) Network Data Sets, Jure Leskovec http://snap.stanford.edu/data/index.html email jure at cs.stanford.edu

Citation for the SNAP collection:

@misc{snapnets, author = {Jure Leskovec and Andrej Krevl}, title = {{SNAP Datasets}: {Stanford} Large Network Dataset Collection}, howpublished = {\url{http://snap.stanford.edu/data}}, month = jun, year = 2014 }

The following matrices/graphs were added to the collection in June 2010 by Tim Davis (problem id and name):

2284 SNAP/soc-Epinions1 who-trusts-whom network of Epinions.com 2285 SNAP/soc-LiveJournal1 LiveJournal social network 2286 SNAP/soc-Slashdot0811 Slashdot social network, Nov 2008 2287 SNAP/soc-Slashdot0902 Slashdot social network, Feb 2009 2288 SNAP/wiki-Vote Wikipedia who-votes-on-whom network 2289 SNAP/email-EuAll Email network from a EU research institution 2290 SNAP/email-Enron Email communication network from Enron 2291 SNAP/wiki-Talk Wikipedia talk (communication) network 2292 SNAP/cit-HepPh Arxiv High Energy Physics paper citation network 2293 SNAP/cit-HepTh Arxiv High Energy Physics paper citation network 2294 SNAP/cit-Patents Citation network among US Patents 2295 SNAP/ca-AstroPh Collaboration network of Arxiv Astro Physics 2296 SNAP/ca-CondMat Collaboration network of Arxiv Condensed Matter 2297 SNAP/ca-GrQc Collaboration network of Arxiv General Relativity 2298 SNAP/ca-HepPh Collaboration network of Arxiv High Energy Physics 2299 SNAP/ca-HepTh Collaboration network of Arxiv High Energy Physics Theory 2300 SNAP/web-BerkStan Web graph of Berkeley and Stanford 2301 SNAP/web-Google Web graph from Google 2302 SNAP/web-NotreDame Web graph of Notre Dame 2303 SNAP/web-Stanford Web graph of Stanford.edu 2304 SNAP/amazon0302 Amazon product co-purchasing network from March 2 2003 2305 SNAP/amazon0312 Amazon product co-purchasing network from March 12 2003 2306 SNAP/amazon0505 Amazon product co-purchasing network from May 5 2003 2307 SNAP/amazon0601 Amazon product co-purchasing network from June 1 2003 2308 SNAP/p2p-Gnutella04 Gnutella peer to peer network from August 4 2002 2309 SNAP/p2p-Gnutella05 Gnutella peer to peer network from August 5 2002 2310 SNAP/p2p-Gnutella06 Gnutella peer to peer network from August 6 2002 2311 SNAP/p2p-Gnutella08 Gnutella peer to peer network from August 8 2002 2312 SNAP/p2p-Gnutella09 Gnutella peer to peer network from August 9 2002 2313 SNAP/p2p-Gnutella24 Gnutella peer to peer network from August 24 2002 2314 SNAP/p2p-Gnutella25 Gnutella peer to peer network from August 25 2002 2315 SNAP/p2p-Gnutella30 Gnutella peer to peer network from August 30 2002 2316 SNAP/p2p-Gnutella31 Gnutella peer to peer network from August 31 2002 2317 SNAP/roadNet-CA Road network of California 2318 SNAP/roadNet-PA Road network of Pennsylvania 2319 SNAP/roadNet-TX Road network of Texas 2320 SNAP/as-735 733 daily instances(graphs) from November 8 1997 to January 2 2000 2321 SNAP/as-Skitter Internet topology graph, from traceroutes run daily in 2005 2322 SNAP/as-caida The CAIDA AS Relationships Datasets, from January 2004 to November 2007 2323 SNAP/Oregon-1 AS peering information inferred from Oregon route-views between March 31 and May 26 2001 2324 SNAP/Oregon-2 AS peering information inferred from Oregon route-views between March 31 and May 26 2001 2325 SNAP/soc-sign-epinions Epinions signed social network 2326 SNAP/soc-sign-Slashdot081106 Slashdot Zoo signed social network from November 6 2008 2327 SNAP/soc-sign-Slashdot090216 Slashdot Zoo signed social network from February 16 2009 2328 SNAP/soc-sign-Slashdot090221 Slashdot Zoo signed social network from February 21 2009

Then the following problems were added in July 2018. All data and metadata from the SNAP data set was imported into the SuiteSparse Matrix Collection.

2777 SNAP/CollegeMsg Messages on a Facebook-like platform at UC-Irvine 2778 SNAP/com-Amazon Amazon product network 2779 SNAP/com-DBLP DBLP collaboration network 2780 SNAP/com-Friendster Friendster online social network 2781 SNAP/com-LiveJournal LiveJournal online social network 2782 SNAP/com-Orkut Orkut online social network 2783 SNAP/com-Youtube Youtube online social network 2784 SNAP/email-Eu-core E-mail network 2785 SNAP/email-Eu-core-temporal E-mails between users at a research institution 2786 SNAP/higgs-twitter twitter messages re: Higgs boson on 4th July 2012. 2787 SNAP/loc-Brightkite Brightkite location based online social network 2788 SNAP/loc-Gowalla Gowalla location based online social network 2789 SNAP/soc-Pokec Pokec online social network 2790 SNAP/soc-sign-bitcoin-alpha Bitcoin Alpha web of trust network 2791 SNAP/soc-sign-bitcoin-otc Bitcoin OTC web of trust network 2792 SNAP/sx-askubuntu Comments, questions, and answers on Ask Ubuntu 2793 SNAP/sx-mathoverflow Comments, questions, and answers on Math Overflow 2794 SNAP/sx-stackoverflow Comments, questions, and answers on Stack Overflow 2795 SNAP/sx-superuser Comments, questions, and answers on Super User 2796 SNAP/twitter7 A collection of 476 million tweets collected between June-Dec 2009 2797 SNAP/wiki-RfA Wikipedia Requests for Adminship (with text) 2798 SNAP/wiki-talk-temporal Users editing talk pages on Wikipedia 2799 SNAP/wiki-topcats Wikipedia hyperlinks (with communities)

The following 13 graphs/networks were in the SNAP data set in July 2018 but have not yet been imported into the SuiteSparse Matrix Collection. They may be added in the future:

amazon-meta ego-Facebook ego-Gplus ego-Twitter gemsec-Deezer gemsec-Facebook ksc-time-series memetracker9 web-flickr web-Reddit web-RedditPizzaRequests wiki-Elec wiki-meta wikispeedia

The 2010 description of the SNAP data set gave these categories:

• Social networks: online social networks, edges represent interactions between people

• Communication networks: email communication networks with edges representing communication

• Citation networks: nodes represent papers, edges represent citations

• Collaboration networks: nodes represent scientists, edges represent collaborations (co-authoring a paper)

• Web graphs: nodes represent webpages and edges are hyperlinks

• Blog and Memetracker graphs: nodes represent time stamped blog posts, edges are hyperlinks [revised below]

• Amazon networks : nodes represent products and edges link commonly co-purchased products

• Internet networks : nodes represent computers and edges communication

• Road networks : nodes represent intersections and edges roads connecting the intersections

• Autonomous systems : graphs of the internet

• Signed networks : networks with positive and negative edges (friend/foe, trust/distrust)

By July 2018, the following categories had been added:

• Networks with ground-truth communities : ground-truth network communities in social and information networks

• Location-based online social networks : Social networks with geographic check-ins

• Wikipedia networks, articles, and metadata : Talk, editing, voting, and article data from Wikipedia

• Temporal networks : networks where edges have timestamps

• Twitter and Memetracker : Memetracker phrases, links and 467 million Tweets

• Online communities : Data from online communities such as Reddit and Flickr

• Online reviews : Data from online review systems such as BeerAdvocate and Amazon

https://sparse.tamu.edu/SNAP

Context

An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses.

The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals. Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%.

Now, although X Education gets a lot of leads, its lead conversion rate is very poor. For example, if, say, they acquire 100 leads in a day, only about 30 of them are converted. To make this process more efficient, the company wishes to identify the most potential leads, also known as ‘Hot Leads’. If they successfully identify this set of leads, the lead conversion rate should go up as the sales team will now be focusing more on communicating with the potential leads rather than making calls to everyone.

There are a lot of leads generated in the initial stage (top) but only a few of them come out as paying customers from the bottom. In the middle stage, you need to nurture the potential leads well (i.e. educating the leads about the product, constantly communicating, etc. ) in order to get a higher lead conversion.

X Education wants to select the most promising leads, i.e. the leads that are most likely to convert into paying customers. The company requires you to build a model wherein you need to assign a lead score to each of the leads such that the customers with higher lead score h have a higher conversion chance and the customers with lower lead score have a lower conversion chance. The CEO, in particular, has given a ballpark of the target lead conversion rate to be around 80%.

Content

Variables Description * Prospect ID - A unique ID with which the customer is identified. * Lead Number - A lead number assigned to each lead procured. * Lead Origin - The origin identifier with which the customer was identified to be a lead. Includes API, Landing Page Submission, etc. * Lead Source - The source of the lead. Includes Google, Organic Search, Olark Chat, etc. * Do Not Email -An indicator variable selected by the customer wherein they select whether of not they want to be emailed about the course or not. * Do Not Call - An indicator variable selected by the customer wherein they select whether of not they want to be called about the course or not. * Converted - The target variable. Indicates whether a lead has been successfully converted or not. * TotalVisits - The total number of visits made by the customer on the website. * Total Time Spent on Website - The total time spent by the customer on the website. * Page Views Per Visit - Average number of pages on the website viewed during the visits. * Last Activity - Last activity performed by the customer. Includes Email Opened, Olark Chat Conversation, etc. * Country - The country of the customer. * Specialization - The industry domain in which the customer worked before. Includes the level 'Select Specialization' which means the customer had not selected this option while filling the form. * How did you hear about X Education - The source from which the customer heard about X Education. * What is your current occupation - Indicates whether the customer is a student, umemployed or employed. * What matters most to you in choosing this course An option selected by the customer - indicating what is their main motto behind doing this course. * Search - Indicating whether the customer had seen the ad in any of the listed items. * Magazine
* Newspaper Article * X Education Forums
* Newspaper * Digital Advertisement * Through Recommendations - Indicates whether the customer came in through recommendations. * Receive More Updates About Our Courses - Indicates whether the customer chose to receive more updates about the courses. * Tags - Tags assigned to customers indicating the current status of the lead. * Lead Quality - Indicates the quality of lead based on the data and intuition the employee who has been assigned to the lead. * Update me on Supply Chain Content - Indicates whether the customer wants updates on the Supply Chain Content. * Get updates on DM Content - Indicates whether the customer wants updates on the DM Content. * Lead Profile - A lead level assigned to each customer based on their profile. * City - The city of the customer. * Asymmetric Activity Index - An index and score assigned to each customer based on their activity and their profile * Asymmetric Profile Index * Asymmetric Activity Score * Asymmetric Profile Score
* I agree to pay the amount through cheque - Indicates whether the customer has agreed to pay the amount through cheque or not. * a free copy of Mastering The Interview - Indicates whether the customer wants a free copy of 'Mastering the Interview' or not. * Last Notable Activity - The last notable activity performed by the student.

Acknowledgements

UpGrad Case Study

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