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.

The total number of user mailboxes in Umeå kommun and how many are active each day of the reporting period. A mailbox is considered active if the user sent or read any email.

Dataset Card for CNN Dailymail Dataset

  Dataset Summary

The CNN / DailyMail Dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering.

  Supported Tasks and Leaderboards

'summarization': Versions… See the full description on the dataset page: https://huggingface.co/datasets/abisee/cnn_dailymail.

CNN/DailyMail non-anonymized summarization dataset.

There are two features: - article: text of news article, used as the document to be summarized - highlights: joined text of highlights with and around each highlight, which is the target summary

In a bustling digital landscape where businesses and individuals alike rely on email communication, a hidden threat lurks—spam emails. These unsolicited and often malicious messages clog inboxes, steal valuable time, and even endanger sensitive data. The need for a powerful shield against this growing menace has never been more urgent.

Enter the Spam Email Detection Model—a cutting-edge creation designed to bring order to the chaos of modern email communication. Imagine a business owner named Sarah, whose company relies heavily on email for client communication, order processing, and customer support. Every day, her inbox is flooded with hundreds of emails, many of which are nothing but spam. These emails not only waste her time but also pose a risk to her company's security. The Spam Email Detection Model is a state-of-the-art solution designed to combat the ever-growing threat of spam emails with unparalleled accuracy and efficiency. Leveraging advanced machine learning algorithms, this model achieves a remarkable 99.9% accuracy rate, far surpassing the industry standard of 50%. It intelligently distinguishes between legitimate emails and spam, learning and adapting to new patterns to ensure ongoing protection.

Designed for seamless integration, the model can be easily implemented into any existing email system, providing businesses with a robust defense against unsolicited messages and potential security threats. Its user-friendly interface allows for effortless control and customization, making it a versatile tool for businesses of all sizes.

By dramatically reducing the time wasted on managing spam and enhancing email security, the Spam Email Detection Model empowers businesses to focus on what truly matters, offering peace of mind in a world where digital communication is vital.

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.

The sonic data within the building array is composed of 26 days of 30-minute average data from 30 sonic anemometers. The unobstructed tower sonic data is also the same, but of the 5 heights of the tower. The data files have 48 columns associated with date and time identifiers as well as meteorological turbulence measurements. This dataset is not publicly accessible because: The data were not collected by EPA and are hosted external to the agency. It can be accessed through the following means: The detailed sonic dataset is freely available to others wishing to perform additional analysis however, it is large and not readily posted. The complete dataset is included in the comprehensive JR II data archive set up by the DHS Science and Technology (S&T) Directorate, Chemical Security Analysis Center (CSAC). To obtain the data, an email request can be sent to JackRabbit@st.dhs.gov. The user can then access the archive on the Homeland Security Information Network (HSIN). Format: The sonic data within the Jack Rabbit II (JRII) mock-urban building array are in 30-minute averaged daily excel files separated by each sonic anemometer with numerous variables. The unobstructed, raw 10Hz tower data are in .dat files and processed into 30-minute average daily csv files by sonic height. This dataset is associated with the following publication: Pirhalla, M., D. Heist, S. Perry, S. Hanna, T. Mazzola, S.P. Arya, and V. Aneja. Urban Wind Field Analysis from the Jack Rabbit II Special Sonic Anemometer Study. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 243: 14, (2020).

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.

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 ---

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

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.

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Dataset Card for Custom Text Dataset

  Dataset Name

Custom Text Dataset

  Overview

This dataset contains text data for training summarization models. The data is collected from CNN/daily mail.

  Composition

Number of records: 100 Fields: text, label

  Collection Process

CNN/daily mail

  Preprocessing

nothing

  How to Use

from datasets import load_dataset dataset = load_dataset("path_to_dataset")

for example in… See the full description on the dataset page: https://huggingface.co/datasets/rasauq1122/custom_summarization_dataset.

Since 1956 a election study have been carried out in conjunction with every parliamentary election in Sweden. Likewise studies have been carried out in conjunction with the two referenda that have taken place since then. In 1957 a referendum on the general supplementary pension scheme (ATP) took place. The respondents were questioned three times, two interviews were held before the day of the referendum and a mail survey were sent to them after the referendum. The study contains questions on the general supplementary pension scheme, national basic pension, opinion of the three proposals, sources of information, and newspaper reading. Apart from the questions concerning the referendum, the study examines Swedish national defence by including questions about the war risk and the atomic bomb. Purpose: Explain why people vote as they do and why an election ends in a particular way. Track and follow trends in the Swedish electoral democracy and make comparisons with other countries. Since 1956 a election study have been carried out in conjunction with every parliamentary election in Sweden. Likewise studies have been carried out in conjunction with the two referenda that have taken place since then. In 1957 a referendum on the general supplementary pension scheme (ATP) took place. The respondents were questioned three times, two interviews were held before the day of the referendum and a mail survey were sent to them after the referendum. The study contains questions on the general supplementary pension scheme, national basic pension, opinion of the three proposals, sources of information, and newspaper reading. Apart from the questions concerning the referendum, the study examines Swedish national defense by including questions about the war risk and the atomic bomb. Syfte: Förklara varför väljare röstar som de gör och varför val slutar som de gör. Spåra och följa trender i svensk valdemokrati och göra jämförelser med utvecklingen i andra länder.

This dataset describes the current state of mail ballot requests for the 2024 General Election. It’s a snapshot in time of the current volume of ballot requests across the Commonwealth. The file contains all mail ballot requests except ballot applications that are declined as duplicate. The final version of this data was pulled on 1/24/2025.

This point-in-time transactional data is being published for informational purposes to provide detailed data pertaining to the processing of absentee and mail-in ballots by county election offices. This data is extracted once per day from the Statewide Uniform Registry of Electors (SURE system), and it reflects activity recorded by the counties in the SURE system at the time of the data extraction.

Please note that county election offices will continue to process ballot applications (as applicable), record ballots, reconcile ballot data, and make corrections when necessary, and this will continue through, and even after, Election Day. Administrative practices for recording transactions in the system will vary by county. For example, some counties record individual transactions as they occur, while others record transactions in batches at specific intervals. These activities may result in substantial changes to a county's reported data from one day to the next. County practices also differ on when cancelled ballot data is entered into the database (i.e., before or after the election). Some counties do not enter cancelled ballot data entirely.

Additional notes specific to this dataset: • Counties can enter cancellation codes without entering a ballot returned date. • Some cancellation codes are a result of administrative processes, meaning the ballot was never mailed to the voter before it was cancelled (e.g., there was an error when the label was printed). • Confidential and protected voters are not included in this file. • Counties can only enter one cancel code per ballot, even if there are multiple errors. Different counties may vary in what code they choose to use when this arises, or they may choose to use the catch-all category of 'CANC - OTHER'.

Type of data included in this file: This data includes all mail ballot applications processed by counties, which includes voters on the permanent mail-in and absentee ballot lists. Multiple rows in this data may correspond to the same voter if they submitted more than one application or had a(n) cancelled ballot(s). A deidentified voter ID has been provided to allow data users to identify when rows correspond to the same voter. This ID is randomized and cannot be used to match to SURE, the Full Voter Export, or previous iterations of the Statewide Mail Ballot File. All application types in this file are considered a type of mail ballot. Some of the applications are considered UOCAVA (Uniformed and Overseas Citizens Absentee Voting Act) or UMOVA (Uniform Military and Overseas Voters Act) ballots. These are listed below:

• CRI - Civilian - Remote/Isolated • CVO - Civilian Overseas • F - Federal (Unregistered) • M - Military • MRI - Military - Remote/Isolated • V - Veteran • BV - Bedridden Veteran • BVRI - Bedridden Veteran - Remote/Isolated *We may not have all application types in the file for every election.

Success.ai’s B2B Contact Data for IT Professionals Worldwide is an advanced, AI-validated solution designed to help businesses connect with top IT talent and decision-makers globally. With access to over 170 million verified profiles, this dataset includes key contact information such as work emails, phone numbers, and additional professional details, ensuring you can easily engage with IT leaders and specialists across various industries.

Our comprehensive data is continually updated to ensure accuracy, relevance, and compliance with global standards. Whether you're looking to expand your network, enhance lead generation, or improve recruitment processes, Success.ai’s IT professional database is designed to meet the evolving needs of your business.

Key Features of Success.ai’s IT Professional Contact Data

• Global Coverage Across the IT Industry Success.ai offers a diverse range of IT professionals, including but not limited to:

Software Engineers & Developers: Specialists in coding, programming, and software development. IT Managers & Directors: Decision-makers responsible for IT infrastructure and strategy. Systems Administrators: Experts managing system installations, configurations, and troubleshooting. Cloud Computing Specialists: Professionals focused on cloud storage and infrastructure services. Cybersecurity Experts: IT professionals safeguarding data and systems from cyber threats. IT Consultants & Analysts: Advisers providing strategic recommendations on technology improvements.

This dataset spans 170M+ verified profiles across more than 250 countries, ensuring you reach the right IT professionals, wherever they are.

• Verified and Continuously Updated Data

  99% Accuracy: Data is AI-validated to ensure that you are reaching the right contacts with accurate, up-to-date information. Real-Time Updates: Success.ai’s dataset is constantly refreshed, ensuring that the information you receive is always relevant and timely. Global Compliance: Our data collection adheres to GDPR, CCPA, and other data privacy standards, ensuring that your outreach practices are ethical and compliant.

• Customizable Data Solutions Success.ai provides multiple delivery methods to suit your business needs:

API Integration: Seamlessly integrate our data into your CRM, marketing automation, or lead-generation systems for real-time updates. Custom Flat Files: Receive highly targeted and segmented datasets, preformatted to your specifications, making integration easy.

Why Choose Success.ai’s IT Professional Contact Data?

• Best Price Guarantee We offer the most competitive pricing in the industry, ensuring you get exceptional value for high-quality, verified contact data.

• Targeted Outreach to IT Professionals Our comprehensive dataset is perfect for precision targeting, making it easier to connect with key IT professionals. With detailed profiles, including work emails and phone numbers, you can engage with decision-makers directly and increase the efficiency of your campaigns.

• Strategic Use Cases

  Lead Generation: Use our verified contact information to target IT decision-makers and specialists for your lead generation campaigns. Sales Outreach: Reach out to key IT managers, directors, and consultants to promote your product or service and close high-value deals. Recruitment: Source top-tier IT talent with verified contact data for software developers, network administrators, and IT executives. Marketing Campaigns: Run hyper-targeted marketing campaigns for IT professionals globally to promote tech services, job openings, or industry innovations. Business Expansion: Use data-driven insights to expand your global outreach, identifying opportunities and building relationships in untapped markets.

• Key Data Highlights

  170M+ Verified Profiles of IT professionals worldwide, covering a wide range of roles and industries. 50M Work Emails to help you reach the right IT contacts. 30M Company Profiles with insights on the organizations that these professionals represent. 700M+ LinkedIn Professional Profiles globally, enhancing your ability to access verified IT contacts across various platforms.

Powerful APIs for Enhanced Functionality

• Enrichment API Keep your data up to date with our Enrichment API, providing real-time enrichment of your existing contact database. Perfect for businesses that want to maintain accurate and current information about their leads and customers.

• Lead Generation API Maximize your lead generation campaigns by accessing Success.ai’s vast and verified dataset, which includes work emails and phone numbers for IT professionals worldwide. Our API supports up to 860,000 API calls per day, ensuring scalability for large enterprises.

• Use Cases for IT Professional Contact Data

• Lead Generation for IT Solutions Target IT decision-makers, software developers, and cybersecuri...

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

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?