NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship ๐Ÿ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lesson
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular ๐Ÿ”ฅ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New ๐ŸŽ‰ Generative AI for Finance New ๐ŸŽ‰ Generative AI for Marketing New ๐ŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular ๐Ÿ”ฅ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular ๐Ÿ”ฅ Data Science R: Machine Learning Designing and Implementing Production MLOps New ๐ŸŽ‰ Natural Language Processing for Production (NLP) New ๐ŸŽ‰
Find Inspiration
Get Course Recommendation Must Try ๐Ÿ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lessons
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > Python > The Secrets to Host a Successful Meetup Event

The Secrets to Host a Successful Meetup Event

Chia-An (Anne) Chen
Posted on Aug 20, 2016

Meetup is an extraordinary platform for bringing people together who share a common interest. Hosts, who schedule meetup events, frequently do so with high expectations. So why do some meetups attract as few as ten or fewer people while others attract over five hundred? This project provides insights to discover the secrets of hosting an event with a high number of participants.

According to this article, New York, San Francisco, Chicago, Washington DC, Palo Alto, Boston, Los Angeles, Mountain View, Seattle, and Austin are the top ten cities in terms of meetup membership. Data for events within 10 miles of these ten cities was scraped from meetup.com using Beautiful Soup, a python package (see code here). Only public events are included in the analysis, which is done using R code (see code here). Information about 12,123 events was collected and event dates ranged from 08/10/2016 to 10/07/2016. Below is a table to display the attributes that were scraped:

Group information Event information

โ€ข Founded date
โ€ข Group keywords
โ€ข Group name
โ€ข Number of members
โ€ข Number of past meetups
โ€ข Number of upcoming meetups
โ€ข Number of Sponsors

โ€ข Event date
โ€ข Event start time
โ€ข Event title
โ€ข Number of participants
โ€ข Price
โ€ข Number of comment and reply

Exploratory Data Analysis

bars
The number of events in these ten cities follows a trend that mirrors the membership size. New York and San Francisco both have more than two thousands events in total. However, cities in California that are tech-oriented, like Palo Alto, Mountain View, and San Francisco, rank top three if we are looking at the average number of participants per event.

heat maps

Note: Y-axes are the starting time of an event. If a certain time block has less then three events, the data is filtered out to prevent bias.

The heat map on the left shows how the total number of events distributes in weekdays and during the time of a day. The most popular time to host an event is Thursday from 6pm to 9pm, and most events started in the same time frame in the evening across weekdays. Interestingly, if we look at the heat map on the right, which plots the distribution of number of participants per event, a slightly different pattern is shown. Events on Monday through Thursday from 3pm to 9pm seem to have more participants whereas events hosted on Friday and weekend have more participants from 9pm to 12am.

Frequent Words Used

Word clouds were generated with the information collected from the meetup events, providing insights into the most popular words used to market these events. The words used to generate the word cloud exclude English stop words, as well as the following: โ€œNYCโ€, โ€œNew Jerseyโ€, โ€œNew Yorkโ€, โ€œChicagoโ€, โ€œSeattleโ€, โ€œBay Areaโ€, โ€œAustinโ€, โ€œBostonโ€, โ€œSilicon Valleyโ€, "Washington", "Los Angeles", "Mountain View", "San Francisco", "Central Parkโ€, "Meetup", "Meeting", "Meet", "Event", "Group", "Club", "Eventsโ€ "North", "South", "East", "West", "Areaโ€, "City", "Brooklyn", "Jersey", "Manhattan", "Hoboken", "Queens", "Hudsonโ€, "2016", "August", "September", "Octoberโ€, "Day", "Monday", "Tuesday", โ€œWednesdayโ€, "Thursday", "Friday", "Saturday", โ€œSundayโ€.
The size of the word in word cloud is proportional to the frequency count. The bar charts shows the count of the top ten most frequent words.
event titles
We can see that โ€œFreeโ€ and โ€œNightโ€ are the top two most frequent words used in the event titles. Not surprisingly, who doesnโ€™t like free events and/or hang out at night?

gr names

As for group names, โ€œsoccerโ€ is the second most frequent word among group names. This is surprising given that soccer is not that popular of a spectator sport in the US, and the US is not thought of as a country that is passionate about soccer. And โ€œBookโ€ indicates that there may be plenty of reading clubs on meetup.com.

gr keywords

The most frequent keywords of a group are โ€œSocialโ€ and โ€œNetworkingโ€, which perfectly match with the purpose of meetup events. Interestingly, โ€œwomenโ€ ranks third while โ€œmenโ€ does not appear at all in the top ten. This finding fits the stereotype that girls like to hang out as a group. And โ€œProfessionalโ€, โ€œDevelopmentโ€, โ€œNewโ€, โ€œTechnologyโ€, and โ€œBusinessโ€ indicate that meetup is not only a platform to boost organized outings for leisure purposes but is also used to build oneโ€™s professional network or to advance oneโ€™s knowledge.

Predictive Model Building

The table below provides a detailed descriptions of the data collected.

Category Variable Meaning
Group member_count Number of members in the group
past_meetup_count Number of past meetup events hosted by the group
upcoming_event_count Number of upcoming meetup events planned by the group
review_count Number of reviews given to the group
sponsor_count Number of sponsors of the group
Event event_day The day, Monday through Sunday, that the event is being hosted
event_time The starting time of an event. A day is divided into eight time frames, each of which is three hours
price The price of an event if a ticket is required

Besides the raw information scraped online, several other features were created for building a model. See table below for detailed descriptions.

Category Variable Meaning
Event comment_reply_count The sum of the comments and reply counts for an event
top_title_count The count of words in the Event Title that are in the top 1% of all words used.
For example, โ€œfreeโ€, โ€œnightโ€, and โ€œhappyโ€ are in the top 1%
Group days_gr_has_founded The age of the meetup group (in days) at the time the event is hosted
top_gr_name_count The count of words in the Group Name that are in the top 1% of all words used
top_gr_keyword_count The count of word in the Group Keywords that are in top 1% of all words used

First, a multiple linear regression was built, but the model violated all assumptions which are linearity, constant variance, normality, and independent errors. Then ridge and lasso regressions were conducted with the use of 5-fold cross validation to locate the best lambdas. Both regressions have an MSE of ~485. The coefficients for Tuesday, Wednesday, Thursday, 6pm-9pm, Mountain View, and Palo Alto are higher, which match to the findings in the bar charts and heat maps in the exploratory data analysis section above.
pruned_tree_meetup

An alternative model, decision tree, was used and a pruned tree was built. The above tree plot is based on three variables: member_count, upcoming_event_count, and past_meetup_count. The MSE for this model has slightly decreased to ~450, which means a decision tree model may be a better choice for this dataset. In an attempt to increase the over all predictive accuracy, a random forest model was then constructed. The workflow is as follows:

  1. Set the seed to ensure reproducibility
  2. Randomly subset 80% of data into training set and 20% to test set
  3. Plot density plots to check if both subset have the same distributions for the number of participants
  4. Find the optimized number of variables randomly sampled as candidates at each split
    with the lowest out-of-bag error
  5. Run random forest and get the variable importance plot
  6. Predict the number of participants for the test set and calculate the MSE

Screen Shot 2016-08-18 at 8.30.36 PM

The MSE of the random forest model is ~225, which outperforms all the other previous regressions. From the variable importance plot above, we can see that the group information (highlighted in red) such as upcoming event count and member count are more important than event information (highlighted in blue). Besides picking the right day and time to host an event, how active the group is may also affect the number of participants. Activity is defined as a combination of number of events (both past and upcoming), as well as the count of comments and replies. These emerge as high value variables.

Takeaways?

Start your meetup group now and grow your member counts, then host a free, socializing, networking event on Thursday starting from 6pm to 9pm!


Technical Development (Examples)

See full code on GitHub.

  • Web scraping
  • Model building

Contributor: Anne Chen

About Author

Chia-An (Anne) Chen

Anne Chen has a Masters degree in Bioengineering from the University of Pennsylvania. Prior to working at a biotech startup developing a liver cancer diagnosis device, Anne researched and evaluated open-source Electronic Health Records software for small-scale hospitals...
View all posts by Chia-An (Anne) Chen >

Related Articles

Capstone
Catching Fraud in the Healthcare System
Capstone
The Convenience Factor: How Grocery Stores Impact Property Values
Capstone
Acquisition Due Dilligence Automation for Smaller Firms
Machine Learning
Pandemic Effects on the Ames Housing Market and Lifestyle
Machine Learning
The Ames Data Set: Sales Price Tackled With Diverse Models

Leave a Comment

Cancel reply

You must be logged in to post a comment.

No comments found.

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day ChatGPT citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay football gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income industry Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    ยฉ 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application