-: Effective data preparation is the foundation
for successful AI outcomes.
Just like a chef needs fresh ingredients,
AI models require clean, structured data to perform well.
When data is inconsistent or contains errors,
it often leads to inaccurate results.
With studies showing that poor data handling is why
up to 85% of AI projects fall short of expectations.
This lesson will explore essential data preparation steps,
examples, and how ChatGPT can support you in this process.
Data preparation is the critical first step
that can make or break an AI project.
Inconsistent data with errors, duplicates,
or missing values creates unreliable results,
leading to misinformed decisions
and potential financial losses.
By contrast, clean, organized data enables AI models
to recognize patterns and deliver accurate insights.
With a reliable dataset,
your AI model can focus on generating
valuable predictions and insights.
Data preparation involves several key tasks,
each essential for preparing high-quality datasets
for AI use.
Standardizing data ensures all formats,
units, and values are consistent.
For instance, if your dataset includes weights
from different sources, some entities might use pounds,
and others kilograms.
Standardizing them into a single unit allows the AI model
to accurately compare values without misinterpretation.
By cleaning the data, you are reducing inconsistencies
and ensuring that each data point is accurate and complete.
This step is essential for reliable AI results.
It prevents models from being influenced by erroneous
or irrelevant information.
In this step, data is adapted into a format
that an AI model can easily process.
Transformations often include scaling numerical data,
encoding categorical variables,
like converting yes/no to 1/0,
or normalizing distributions to match the model's needs.
These transformations improve the data's capabilities
and help streamline analysis.
Let's head into ChatGPT to explore how we can make
the data preparation process easier with SOPs.
Having SOPs in place ensure
that everybody follows the same steps
and data-handling standards,
reducing the likelihood of errors.
A streamlined approach to SOP creation involves,
one, recording process with a tool like Loom
to create a transcript.
Two, convert the recording into a structured SOP,
using tools like ChatGPT,
which can turn the transcript into an organized document.
Three, storing the SOP in a centralized location
for easy team access,
promoting consistent data practices across your platform.
By creating clear and accessible SOPs,
you ensure that everybody in your team
can follow consistent steps
to maintain data quality,
even as the team grows or evolves.
Here, we create a template prompt that will be used
whenever we have meeting transcripts
that can be turned into a specific tasks SOP.
We will try it out with an example from Leap Your Learning,
where our consultants are discussing the SOP
for the script creation.
We upload this meeting transcript into ChatGPT
alongside our template prompt
and have it create our task SOP.
As you can see, this provides a format
for the whole company to follow, and most importantly,
AI solutions like Custom GPTs or AI chatbots
can easily read this format
and use the data in different ways.
Going back to data preparation
and one of its key aspects, data cleaning and processing,
ChatGPT can streamline many data-cleaning tasks
by automating processes
that would otherwise be time-consuming.
ChatGPT can identify and remove duplicate entries,
ensuring each record is unique and relevant,
standardizing data formats, such as converting dates
to a unified year, month, day format,
or ensuring all monetary values use the same currency.
Fill in missing values, using imputation techniques,
reducing gaps in your data, and improving completeness.
Additionally, ChatGPT can generate Python scripts
that can detect and remove outliers.
Outliers, or extreme values, can mislead an AI model
if left unchecked.
For example, a price listed at a million dollars
due to a data entry error could skew predictions.
ChatGPT's automated outlier detection script
can flag these values,
ensuring the model is not misinformed.
Picking it out of your dataset
ensures that your statistics are not skewed.
Beyond cleaning, ChatGPT is valuable
for transforming data into enhanced AI model compatibility.
For instance, ChatGPT can convert unstructured data,
e.g., text fields, into structured formats
by categorizing entries or creating sentient labels,
generate code for feature scaling, or normalization,
preparing continuous variables
for machine learning algorithms.
Now that we understand ChatGPT's capabilities
in the context of data preparation,
let's experiment with two examples.
First, I wanna clean this dataset
of customer purchase data,
which contains dates in different formats,
duplicate records, as well as a few extreme values
that seem to be outliers.
Let's start by asking for a ChatGPT
to help us standardize the data formats,
delete duplicates, and highlight outliers.
Now, if you see this unable to display visualization error,
don't worry, that happens because we're showcasing
a thread created earlier
so it's just skipping the document preview.
It doesn't impact the data processing at all.
As you can see, ChatGPT has identified what it has to do
and went ahead with cleaning the dataset.
As we scroll through the dates,
it seems that ChatGPT got it right.
Now we can work with this data externally
more consistently, but we're not done yet.
We want showcase how we can transform data,
so using the same dataset,
let's ask ChatGPT to take the dataset we provided,
analyze the purchase date column to extract new columns,
such as year, month, and day.
We could transform purchase amount into categories
such as low, medium, and high based on predefined ranges,
so let's do that and ask ChatGPT
to transform data according to those instructions.
As you can see, there are many ways you can use AI
and ChatGPT specifically for taking existing data,
cleaning it, and enhancing it
by transforming existing information into new data.
Understand your data, familiarize yourself with the purpose
and source of the data.
Knowing its context reduces the need for extensive cleaning.
Maintain consistency.
Regularly monitor and standardize data formats.
Document procedures to support long-term data integrity.
Start small and scale.
Begin with a single dataset or process,
refining your approach before expanding to larger datasets
or more complex transformations.
While data preparation might seem tedious,
it is essential for the success of your AI projects.
Following best practices in cleaning, standardization,
and transformation ensures that your AI model
is accurate and reliable.
With tools like ChatGPT, you can assist with automation.
You can streamline data preparation,
setting a strong foundation for impactful AI insights.