🍊 This Week's Juice: AI for Data Analysis, AI Plays Pokemon, and How I Learn Sign Language Using AI!

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Hey friends!

In today’s newsletter, I’ll be learning sign language haha.

You’ll see how later (I use AI for it).

Also, I share about how I would use AI for data analytics!!

For those who celebrate the Lunar New Year, happy new year to you!

Here’s wishing your a prosperous dragon year 🐉 filled with blessings! 🍊🍊

Here’s today’s menu:

How to Use AI for Data Analytics 📈
AI Plays Pokemon! 🎮
How I’m Learning Sign Language Using AI! ✋🏻

Okay, let’s serve you up your weekly juice! 🍊

- Justin

Read time: ~6.5 minutes

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1. How to Use AI for Data Analytics 📈

Multi faceted graphs by ChatGPT including a histogram, bar graph, scatterplot, and a boxplot graph.

I’m a data analyst - and I know how important data analytics is for any organization!

But can AI, more specifically, ChatGPT, do a good job of analyzing datasets?

I took to ChatGPT to find out:

1/ Download Kaggle Dataset

Firstly, I downloaded a dataset from Kaggle on Volcanic Eruptions.

This will serve as the main flat dataset that I’ve be analyzing.

I went to ChatGPT-4 and uploaded the file.

Asking to show the top 5 rows of the dataset is a standard practice in data analysis work, to understand how the data looks like.

Looks like there are a number of NaN values here. We have the Year, Month, Day, Name, Location, Country, Latitude, and Longitude columns.

Here’s the Python code behind the scenes:

# Let's read the uploaded CSV file to display the top 5 rows of the dataset
import pandas as pd

# Load the dataset
file_path = '/mnt/data/volcano-events.csv'
data = pd.read_csv(file_path)

# Display the top 5 rows
data.head()

2/ Ask for an EDA

Secondly, I asked for an EDA (Exploratory Data Analysis) of the dataset, with graphs.

Here’s what ChatGPT came up with (in just 30 secs!)

Multi faceted graphs by ChatGPT including a histogram, bar graph, scatterplot, and a boxplot graph.

Here’s ChatGPT importing data visualization libraries within Python like matplotlib and seaborn

import matplotlib.pyplot as plt
import seaborn as sns

# Remove the first row with NaN values to clean the dataset for EDA
cleaned_data = data.dropna(how='all')

# Summary of numerical features
summary_numerical = cleaned_data.describe()

# Checking for missing values
missing_values = cleaned_data.isnull().sum()

# Plotting some graphs for EDA
fig, axes = plt.subplots(2, 2, figsize=(16, 12))

# Histogram of 'Elevation (m)'
sns.histplot(cleaned_data['Elevation (m)'], bins=30, ax=axes[0, 0], color='skyblue')
axes[0, 0].set_title('Distribution of Elevation (m)')

# Countplot of 'Type'
sns.countplot(y=cleaned_data['Type'], ax=axes[0, 1], palette='viridis')
axes[0, 1].set_title('Count of Volcano Types')

# Scatter plot of 'Latitude' vs 'Longitude'
sns.scatterplot(x='Longitude', y='Latitude', hue='Type', data=cleaned_data, ax=axes[1, 0], palette='coolwarm')
axes[1, 0].set_title('Geographical Distribution of Volcanoes')

# Boxplot of 'Elevation (m)' by 'Type'
sns.boxplot(x='Elevation (m)', y='Type', data=cleaned_data, ax=axes[1, 1], palette='autumn')
axes[1, 1].set_title('Elevation (m) by Volcano Type')

plt.tight_layout()
plt.show()

(summary_numerical, missing_values)

Here’s a full analysis that ChatGPT came up with, along with recommendations for further analysis!

Summary of Numerical Features

The dataset spans years from -4360 to 2023, indicating historical events are included.
The elevation of volcanoes ranges from -642 meters (below sea level) to 6377 meters, with a mean elevation of 1956 meters.
The Volcanic Explosivity Index (VEI) ranges from 0 to 7, with an average of 2.86, suggesting a mix of eruption intensities.

Missing Values

A significant amount of data is missing for columns such as Month, Day, VEI, Deaths, and Injuries.
Missing and Total Missing data are almost entirely absent, indicating these events are rarely reported or recorded.

Graphical Exploration

Distribution of Elevation (m): Most volcanoes are between 0 and 3000 meters elevation, with a peak around 1000-2000 meters.
Count of Volcano Types: Caldera, Stratovolcano, and Shield volcano types are the most common in the dataset.
Geographical Distribution of Volcanoes: Volcanoes are scattered around the globe, with concentrations in specific regions reflecting well-known volcanic belts.
Elevation (m) by Volcano Type: There's a wide range of elevations within each type of volcano, but some types, like Stratovolcanoes, tend to be taller.

Insights

The dataset includes a wide variety of volcano types and locations, highlighting the global nature of volcanic activity.
The missing data, particularly in the Month and Day columns, suggests that the exact timing of many events is not recorded, possibly due to historical records being incomplete or the events occurring over uncertain timeframes.
The variability in the Elevation (m) and VEI columns indicates the diverse nature of volcanic eruptions and their physical characteristics.

Recommendations for Further Analysis

Impute missing values for key columns where appropriate, especially for columns like VEI, which is crucial for understanding the intensity of eruptions.
Conduct a temporal analysis to explore trends in volcanic activity over time, taking into account the limitations of historical data accuracy.
Investigate the relationship between volcano elevation, type, and eruption intensity to understand if certain characteristics are predictive of eruption severity.

2/ Ask for More Details & Analysis

Thirdly, I then simply asked ChatGPT for the country with the most volcanic eruptions.

It’s Indonesia. And it even explains that it’s called the Pacific “Ring of Fire” where tectonic plates boundaries are.

Again the Python code:

# Counting the number of volcanic eruptions by country
eruptions_by_country = cleaned_data['Country'].value_counts()

# Finding the country with the most volcanic eruptions
most_eruptions_country = eruptions_by_country.idxmax()
most_eruptions_count = eruptions_by_country.max()

(most_eruptions_country, most_eruptions_count)

We could do further analysis here - but you get the idea on how this can work!

All in all, ChatGPT is really handy for learning data analytics and programming in Python.

You’ll be able to view the code for each step, so you can understand what’s going on behind the scenes.

Also, if you’re not going to hire a data analyst, this is a great way to get large datasets analyzed! (Be sure to anonymize your data before uploading it!)

2. AI Plays Pokemon! 🎮

Ever wondered how it’d be like if AI played Pokemon?

A guy on YouTube name Peter Whidden made a single viral video on it.

Here’s an AI-powered summary of the video!

Highlights

[00:00] 🕹️ AI begins with no knowledge, only random button presses.
[5 years] ⚡ AI gains capabilities and learns from experience.
[100 iterations] 🏆 AI catches Pokémon, evolves them, and defeats a gym leader.
[Version 45] 🧠 AI shows substantial improvement, evolving a Pidgeotto for the first time.
[Version 65] 🌳 AI navigates through Mt. Moon but gets stuck in a visually uniform area.
[300 days] 💥 AI defeats Brock by accidentally switching to Bubble as a default move.
[100 iterations] 💡 AI’s behavior shows preference for counterclockwise navigation.

Key Insights

[🔍] Studying AI behavior can teach us about ourselves and our own sources of motivation.
[🧠] AI’s experience and biases can both help and limit its ability to solve problems.
[🔄] Humans also struggle with relying on past experiences and biases when facing new challenges.
[🔀] AI’s counterclockwise navigation pattern aids in memory and planning.
[🪜] AI can get physically stuck in certain areas due to one-way paths or traps.
[🌲] The AI’s behavior evolves throughout training, adapting to different paths in Vidiian Forest.
[⏳] AI’s accidental breakthrough in using Bubble as a default move demonstrates the importance of exploring new strategies.

Cool isn’t it? 😄

3. How I’m Learning Sign Language Using AI: PopSignAI! ✋🏻

Did you know?

95% of deaf infants are born to hearing parents, who often do not know ASL (American Sign Language).

To help with this, Google has helped improve a sign language recognition game app: PopSign AI.

It’s to help parents learn and practice sign language with their children.

Best part? It's free.

‍PopSign AI is an educational bubble shooter game developed by the Georgia Institute of Technology and RIT/NTID’s Center on Access Technology.

The game teaches over 500 American Sign Language (ASL) signs based on the MacArthur-Bates Communicative Development Inventories, which are the first concepts used to teach a language to a child.

Watch this video to learn more about it:

I tried it - here’s me giving it a try!

To download it, make sure you try the PopSign AI Preview app on the App Store. This is the one that has the image recognition abilities.

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That's all, folks - see you next Wednesday!

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