To Know What People Twitter About #Coronavirus In One Minute

Year 2020 is not off to a good start. The ongoing Coronavirus outbreak that originated in Wuhan, China has infected thousands of people worldwide and killed hundreds. Numbers are still rising everyday. With all the quarantine controls and vaccine development, hope this global epidemic will be soon under control.

When we are facing such a global challenge, we take our emotions and concerns to social media and share Coronavirus news with others. Since the outbreak, each day there are hundreds of thousands of tweets about Coronavirus. I decided to run analyses on Twitter feeds and see if I could generate some highlights.

Auto Generated Insights of 2019 HR Tech Conference Twitter – Part 2 (Topic Modeling)

In our last post, we extract #HRTechConf tweets, clean up the texts, and generate a word cloud that highlights some of the buzzwords from the conference. But, what are the tweets talking about? Without reviewing each of the 7,000 tweets, how could we find out the popular topics? Let’s explore and see if tweet topics could be auto detected by developing a Latent Dirichlet Allocation (LDA) model.

Feature Extraction

Tweets or any text must be converted to a vector of numbers – the dictionary that describes the occurrence of words in the text (or corpus). The technique we use is called Bag of Words, a simple method of extracting text features. Here are the steps.

Auto Generated Insights of 2019 HR Tech Conference Twitter – Part 1 (Word Cloud)

HR Technology Conference and Expo, world’s leading and largest conference for HR and IT professionals, just took place in Las Vegas, from Oct 1 – 4, 2019. An incredibly amount of HR technology topics were covered at the conference. Unfortunately not everyone could be there, including myself. Is it possible to tell what the buzzwords and topics are without being there? The answer is YES! I dig into Twitter for some quick insights.

I scrape tweets with #HRTechConf, and build Latent Dirichlet Allocation (LDA) model for auto detecting and interpreting topics in the tweets. Here is my pipeline:

Data gathering – twitter scrape
Data pre-processing
Generating word cloud
Train LDA model
Visualizing topics

I use Python 3.6 and the following packages:

TwitterScraper, a Python script to scrape for tweets
NLTK (Natural Language Toolkit), a NLP package for text processing, e.g. stop words, punctuation, tokenization, lemmatization, etc.
Gensim, “generate similar”, a popular NLP package for topic modeling
Latent Dirichlet Allocation (LDA), a generative, probabilistic model for topic clustering/modeling
pyLDAvis, an interactive LDA visualization package, designed to help interpret topics in a topic model that is trained on a corpus of text data

Things Employees Like and Dislike About Their Companies

I work in people analytics and have been wondering all the time what make employees feel great or bad about their companies. Is it money? Workload? Opportunities to grow? Or team around them? I know the answer depends on the company, but is there anything in common for companies that employees like or dislike the most?

I went to Glassdoor for help. Glassdoor is one of the world’s largest growing job sites where employees anonymously review current or former employers. I did my studies based on the 6,000 companies that have an office in Vancouver, BC.

Web App For Border Crossing Wait Time Forecast – Part 2

Keywords: Web App, Flask, AJAX, API, AWS, Virtual Environment

Previously, I built the Flask web app that runs on my local machine for predicting border crossing wait time. This time I’ll show how it gets deployed on AWS and becomes a public available web app.

Here is the link to web app http://35.164.32.109:5000/

There is a small change to my workflow. Instead of using Facebook Prophet, I changed to build an XGBoost model due to Prophet requirement of minimum 4GB memory. AWS free tier EC2 service only has 1GB memory.

Model is rebuilt daily using the new wait time records available from prior day, and makes forecasts for the next 7 days. The last 7 days records are held out for model validation and RMSE is used for model evaluation.

Web App For Border Crossing Wait Time Forecast – Part 1

Keywords: Facebook Prophet, Web App, Flask, AJAX, API, AWS

About a year ago I built a predictive model for predicting border crossing wait time. There were a lot of feature manipulation and parameter tweaking. Although results were encouraging, I always wanted to simplify the process and also make the model available for public use.

After spending two weekends researching and coding (as I have no prior knowledge of Prophet or Flask), here is the improved workflow:

Retrieve border crossing wait time from Cascade Gateway API
Build predictive model for future crossing using Python + Facebook Prophet
Develop web app REST API using Flask, HTML, CSS, ajax
Deploy web app on AWS
Refresh data and re-build predictive model daily

Credit Card Fraud Detection Using SMOTE Technique

Outlier detection is is an interesting application of machine learning. The goal is to identify those useful data records that can accurately profile abnormal behavior of the system. However, in real life examples, such special data like fraud and spam takes very small percentage of overall data population, which imposes challenges for developing machine learning models.

In this experiment, we will examine Kaggle’s Credit Card Fraud Detection dataset and develop predictive models to detect fraud transactions which accounts for only 0.172% of all transactions. To deal with the unbalanced dateset issue, we will first balance the classes of our training data by a resampling technique (SMOTE), and then build a Logistic Regression model by optimizing the average precision score.

We will build and train our model on Google Colab, a free Jupyter notebook environment that runs on Google cloud and gives free GPU! For more information on Colab, check Colab official page.

People Analytics – Attrition Predictions

According to the U.S. Bureau of Labor Statistics, 4.5 years is the average amount of time employees stay with their company today. It hurts an organization’s financials and morale , considering the amount of time they spend training. Can management learn from the past attrition and manage to reduce turnovers? Answer is yes. We will build some predicative models using the fictional IBM data set which contains 1470 employee attrition records.

This post is part of a series of people analytics experiments I am putting together:

Job skill match (Recruitment )
Employee attrition prediction (Employee Management)
Pay gap by gender, ethnicity, profession (Employee Compensation) FUTURE WORK
Organizational network analysis (ONA) FUTURE WORK

Visualizing Twitter social network of HRanalytics

Feature Extraction