An easy way to create interactive geographical scatter plots

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“Where Tim Hortons is, that’s my home.” Tim Hortons, a Starbucks-like coffee shop founded by a hockey player, always has special meanings for Canadians. It is Canada’s largest fast-food restaurant chain with 4,846 stores in 14 countries, by December 31, 2018, according to Wikipedia. In this article, we will use a dataset of Tim Hortons Locations from Kaggle to show how to create interactive visualizations of latitude and longitude on maps with Plotly and Python. From these plots, we can easily answer a question:

Which Tim Hortons is the one Santa Claus usually visits?

Import libraries:

Fuzzycouple: A solution for fuzzy match using tf-idf and cosine similarity

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Why we need fuzzy string match and what are the use cases?

Languages are ambiguous. The text referring to the same thing could be written slightly differently, or even misspelled. Assuming that you are trying to join two tables by the column of addresses, the same location shows in table A as “520 Xavier Ave, California City” while “520 Xavier Avenue, CA” in table B. How would you handle this issue?

There are a few more examples demonstrating the same content written in different ways:

  • The Queen’s Gambit vs Netflix The Queen’s Gambit (miniseries)
  • Toronto Raptors vs Raptors
  • Los Angeles Lakers vs Lakers
  • vs helloworld@gmail.con
  • Tesla, Inc. vs TSLA

We want…

An experiment of GPT-3 in Generating Content

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GPT-3 is OpenAI’s new language generator which blew my mind with its power in reasoning, summarizing, classification, transformation, and so on. How is GPT-3’s capability in generating text?

Since it is the holiday season, you might want to write some greeting cards to your colleagues. The problem is that if we copy the Christmas wishes found online directly, potentially, your coworker could receive the same wish from you and others.

Are you curious about what wishes GPT-3 would create? The wishes that don’t exist in the world currently? …

Plot COVID-19 Data Using Plotly and Seaborn to Catch Outliers

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Detecting outliers is a crucial step in EDA (exploratory data analysis), and sometimes itself is the goal of machine learning projects. There are outliers in almost any dataset in the world. Catch and understand outliers can inspire business insights, and lead to further research or possible solutions.

How to apply data visualization to identify outliers? How to plot with Plotly or Seaborn by writing a few lines of Python code? This article will use a Covid-19 dataset as an example to demonstrate how to catch anomalies from the 50 states of America. The answers we can get from outliers include:

Every Chatbot Is Born for a Purpose, Just Like Humans

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When you create a new agent in Dialogflow console, there are two default intents set up for you already: Default Welcome Intent and Default Fallback Intent. Unfortunately, if you want to build and deploy the chatbot to solve real-world problems, these default intents and default responses cannot satisfy the users.

How to modify these intents and responses? The answer depends on the specific problem your chatbots want to solve. Every chatbot is born for a purpose, just like humans. The common chatbots in the industry are:

  • FAQ chatbot to answer the frequented asked questions from customers. For example, what are…

How to Build and Deploy Coffee Order Chatbot Lilis Using Dialogflow

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Build and Deploy Coffee Order Chatbot Lilis

Imagine you walk into a Starbucks, there are no humans but a chatbot waitress Lilis busily working to take your orders. What are the top seven must-have features of this minimum viable product (MVP): a chatbot waiter/waitress?

Before we move forward to technical details, let’s have a quick demo of chatbot Lilis. Lilis was built with Dialogflow and deployed on Google Assistant. Users can activate it by speaking “Talk to Coffee Order Chatbot Lilis.”

a quick demo of coffee order chatbot Lilis

Top seven MVP features of a chatbot waiter

As shown in the demo, chatbot waiters working in a coffee shop or restaurant who take orders from…

Alina Zhang

Data Scientist: Keep it simple.

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