Welcome back to our AWS Machine Learning Associate Series, a series we started for our readers to learn about AWS Machine learning free.
In the last post, we had seen whether a business problem really needs an ML solution and fundamentals of data . Now, let’s imagine the answer is yes. We’ve collected data — but raw
data is messy. Before we can train any model, we need to first clean the data! So,
lets’ talk about data cleaning in machine learning, outlier
detection, and the AWS services that make this easier in this post.
Alright, let’s begin..
The Problem: Dirty Data, Data Quality & Data Preprocessing
Jake sat at his counter one evening, flipping through his
old leather ledger. To him, it was just a habit — jotting down sales, customer
notes, and little reminders. But as Ethan, his nephew and budding data analyst,
leaned over, he noticed something troubling.
Some entries were neat and clear:
But others were confusing:
- “??
iPhone sold.” (Missing Values in data)
- “Customer
bought 1000 iPhones.” (Outlier in machine learning dataset)
- “N/A”
vs. “Not Applicable.” (Structural Errors in data quality)
- Duplicate
rows of the same sale (Duplicate Data problem)
Ethan smiled knowingly.
“Uncle Jake, this is what we call dirty data. In machine learning, we
can’t just feed this into a model. If we do, the predictions will be
unreliable. That’s why data cleaning in machine learning is so
important.”
Jake frowned. “So my ledger is useless?”
“Not at all,” Ethan replied. “It’s valuable — but only if we improve its data
quality. That means making sure it has accuracy, validity, completeness,
consistency, and uniformity. Without those, even the best machine
learning algorithms will fail.”
Jake scratched his head. “Alright, but what exactly is a machine learning algorithm?”
Ethan smiled. “Think of it like a recipe — but instead of cooking food, it’s cooking insights. You give the algorithm your data (the ingredients), and it follows a set of rules to produce a predictive model (the finished dish). That model can then be used for predictive analytics, artificial intelligence applications, and even AI in business decisions.”
Jake leaned forward. “So the algorithm is like my grandmother’s recipe, but instead of biryani, it’s producing predictions?”
“Exactly,” Ethan laughed. “For example, a spam filter uses a machine learning algorithm. It learns from thousands of emails which ones are spam and which ones are safe. In the same way, businesses use algorithms for fraud detection in banking, customer churn prediction, and demand forecasting in retail. With cloud computing solutions like AWS, these algorithms can scale to millions of records.”
Jake nodded slowly. “So the algorithm is the recipe, the data is the ingredients, and the model is the finished product that helps businesses make smarter decisions.”
“Perfect,” Ethan said. “That’s machine learning in data science — turning raw information into actionable intelligence.”
Why This Matters for Businesses
Ethan explained further:
“Think about it, Uncle Jake. If Apple predicts iPhone sales, or Amazon
recommends products, they rely on high‑quality data. If their data is
full of errors, their forecasts will be wrong, costing millions. Even for a
small shop like yours, bad data leads to bad decisions — too much stock,
too little stock, or missed opportunities.”
He added:
“This is why data preprocessing techniques like data profiling, data
wrangling, and exploratory data analysis (EDA) are the first steps
in any machine learning pipeline. Before we even think about training a
model, we must clean the data.”
Ethan leaned closer to the ledger.
“Let me break it down for you, Uncle Jake:
- Data Profiling: This is like inspecting your ledger to see what’s inside — checking for missing values, unusual entries, or inconsistent formats.
- Data Wrangling: Once we spot the issues, we fix them — removing duplicates, correcting errors, and reshaping the data so it’s usable.
- Exploratory Data Analysis (EDA): Finally, we visualize and summarize the data to understand patterns, spot outliers, and decide what needs more cleaning.”
- Data preprocessing = the “clean‑up and preparation” stage before machine learning.- Data profiling = checking what’s inside the dataset (types of values, ranges, missing entries).- Data wrangling = fixing and reshaping the data so it’s usable (removing duplicates, correcting errors, standardizing formats).- Exploratory Data Analysis (EDA) = looking at the data visually/statistically to understand patterns, spot outliers, and decide what needs fixing.- Machine learning pipeline = the step‑by‑step process of building a model.
Jake nodded. “So it’s like first checking my shelves, then rearranging the stock, and finally walking around the shop to see what’s selling and what’s not.”
“Exactly,” Ethan said. “That’s why these data preprocessing techniques are always the first step in any machine learning pipeline.
Core Data Cleaning Techniques & Feature Engineering Basics
(Cleaning the Ledger )
Jake stared at his ledger, shaking his head.
“Ethan, this book is a mess. Half the numbers don’t make sense. How can a
machine ever learn from this?”
Ethan smiled. “That’s exactly why we need data cleaning
in machine learning. Think of it like sweeping your shop before customers
arrive. If the floor is dirty, no one will want to step in. If your data is
dirty, no algorithm will give you reliable predictions.”
The First Sweep: Handling Missing Values
Ethan pointed to an entry:
“?? iPhone sold.”
“That’s a missing value,” he explained. “In ML, we
can’t just leave blanks. We use data preprocessing techniques like imputation
to fill them in.”
Jake raised an eyebrow. “Impu‑what?”
Ethan pointed again to the blank entry in Jake’s ledger.
“See this missing number? We can’t just leave it empty. In machine learning, we use something called imputation — basically, smart ways to fill in the blanks.”
Jake squinted. “Alright, but what do you mean by mean or median? Those sound like math class words.”
Ethan laughed. “Let me explain in shopkeeper language first, then I’ll give you the proper terms.”
- Mean Imputation: “Imagine you forgot how many iPhones you sold on Tuesday. One way is to take the average of all the other days and use that number. That’s called the mean.”
- Mean = the sum of all numbers ÷ how many numbers there are.
- Median Imputation: “But what if one day you had a crazy festival sale and sold 500 phones? That would make the average too high. Instead, we can use the middle value when all the numbers are lined up. That’s the median.”
- Median = the middle number in an ordered list.
- Mode Imputation: “Now, if the missing value is not a number but a category — like the phone color — we just use the most common one. That’s the mode.”
- Mode = the value that appears most often.
- Deletion: “And if too much is missing — like half the ledger is blank — sometimes it’s better to just drop that row or column completely.”
Jake chuckled. “So it’s like guessing what I forgot to write
down.”
“Exactly,” Ethan said. “But we do it smartly, so the dataset stays consistent.”
The Second Sweep: Fixing Structural Errors
Next, Ethan showed him entries like:
- “N/A”
- “Not
Applicable”
- “na”
“These are structural errors,” Ethan explained. “They
look different, but they mean the same thing. We standardize them so the
computer doesn’t get confused.”
Jake nodded. “So it’s like making sure all my prices are in
rupees, not some in dollars and some in yen.”
“Perfect analogy,” Ethan said. “That’s called data uniformity.”
The Third Sweep: Tackling Duplicates and Irrelevant Data
Ethan flipped to another page.
“Look, you wrote the same sale twice. That’s duplicate data. It inflates
your numbers.”
“And here,” he pointed, “you wrote ‘Rainy day, fewer walk‑ins.’
That’s useful for you, but not for predicting iPhone sales. That’s irrelevant
data.”
Jake laughed. “So I’ve been making my own dataset messy
without even knowing it.”
“Don’t worry,” Ethan said. “Every business does. That’s why data wrangling
and data transformation are so important.”
The Final Sweep: Scaling and Encoding
Ethan opened his laptop.
“Now, let’s prepare your data for the model. We need to make sure numbers are
on the same scale and categories are machine‑readable.”
Jake frowned. “On the same scale? Machine‑readable? You’re losing me again.”
Ethan smiled. “Let me explain. Imagine you’re comparing the height of phone boxes in centimeters and the weight of phones in kilograms. The numbers are on completely different scales — one in hundreds, the other in single digits. A machine learning algorithm might think the bigger numbers are more important, even when they’re not. That’s why we use scaling and encoding techniques.”
Normalization (Min‑Max Scaling)
“Suppose your daily sales range from 10 to 500. With normalization, we squeeze all values into a range between 0 and 1. So 10 becomes 0, 500 becomes 1, and everything else falls in between. This way, no number dominates just because it’s bigger.”
Standardization (Z‑Score Normalization)
“Another method is standardization. Here, we shift the data so the average (mean) becomes 0 and the spread (standard deviation) becomes 1. It’s like re‑centering your ledger so we measure how far each day’s sales are from the ‘usual’ day.”
One‑Hot Encoding
Jake pointed at a column. “What about this — it says ‘Red iPhone,’ ‘Blue iPhone,’ ‘Black iPhone.’ How does a computer read colors?”
“Good question,” Ethan said. “The key here is that colors
are just different names—they have no inherent order (Nominal
data). For these, we use one-hot encoding. We turn each category into
its own column: Red = 1 or 0, Blue = 1 or 0, Black = 1 or 0. It’s like having
separate shelves for each color—either there’s stock (1) or there isn’t (0).”
“And sometimes,” Ethan continued, “the order does matter. Imagine a Customer_Rating column (Poor, Medium, High). These categories are Ordinal because 'High' is clearly better than 'Poor.' In this case, we use label encoding and assign numbers based on the rank (Poor=1, High=3). It’s simpler and faster, and since the order is real, the machine can use it.”
- Normalization
(Min‑Max Scaling): Brings values into a [0,1] range.
- Standardization
(Z‑Score Normalization): Centers data around mean 0, standard
deviation 1.
- One‑Hot
Encoding: Turns categories into binary columns.
- Label Encoding: Assigns numbers to categories.
Jake nodded slowly. “So normalization and standardization keep numbers fair, and encoding turns words into numbers the computer can understand.”
“Exactly,” Ethan said. “That’s how we make your data machine‑readable and ready for the machine learning pipeline.”
Jake raised an eyebrow. “So you’re teaching the computer to read my handwriting?”
Ethan grinned. “That’s one way to think about it. In machine learning, we call this feature engineering — taking raw notes, messy records, or unstructured details and transforming them into useful signals the algorithm can actually learn from. Just like you turn scribbles in your ledger into neat sales reports, we turn raw data into features that drive accurate predictions.”
Why This Matters for Businesses
Ethan leaned back.
“Uncle Jake, whether it’s Apple predicting iPhone sales, Amazon recommending
products, or a small shopkeeper avoiding wasted stock, the principle is the
same: clean data leads to better decisions. Without it, even the most
advanced machine learning algorithms won’t work.”
Advanced Outlier & Anomaly Detection in Machine Learning (The Detective’s Toolkit )
Jake leaned over Ethan’s laptop. On the screen was a simple
chart of his daily iPhone sales. Most days showed between 1 and 10 sales. But
one bar shot up like a skyscraper: 1000 iPhones sold in a single day.
Jake laughed. “That’s ridiculous. I’ve never sold that many
in one day.”
Ethan nodded. “Exactly. That’s what we call an outlier in machine learning. Outliers are data points that don’t fit the usual pattern. They can be caused by mistakes — like someone adding an extra zero — or they can be real but rare events, like a bulk order.”
Why Outliers Matter
Ethan explained:
- Outliers
can skew averages and make your model think sales are higher than
they really are.
- They
can confuse algorithms like regression or clustering, leading to
poor predictions.
- But
sometimes, outliers are golden signals — like fraud detection in
banking or anomaly detection in healthcare.
Jake scratched his head. “So, sometimes they’re weeds, and
sometimes they’re rare flowers.”
“Exactly,” Ethan smiled.
Tools of the Trade: Detecting Outliers
Ethan showed Jake a few methods:
Think of them as tools you already use every day without realizing it.
1. Z‑Score Method
“Say most of your customers buy one or two iPhones a day. If suddenly someone buys a thousand, that’s like a customer asking for a truckload of rice instead of a bag. It’s so far from normal that it sticks out. That’s what the Z‑Score does—it measures how far something is from the usual crowd.”
Jake chuckled. “Ah, so it’s like spotting the oddball order.”
“Exactly, Uncle,” Ethan nodded
2. Interquartile Range (IQR) Technique
“Think of your daily earnings. Most days you make between $5,000 and $15,000. If one day you see $1,00,000, you’d immediately raise an eyebrow. The IQR is like building a fence around your usual earnings. Anything way outside that fence is suspicious.”
Jake leaned back. “So it’s like keeping an eye on the cash drawer.”
“Right, Jake,” Ethan said warmly.!
3. Box Plots & Scatter Plots
Ethan sketched a box with dots. “Imagine your shelves. If all the iPhones are neatly stacked but one box is lying on the floor, you’d notice it instantly. Box plots and scatter plots are just pictures that make those ‘fallen boxes’ easy to spot.”
4. Isolation Forest
“Picture this, Jake. Most customers buy one or two phones. But one customer buys 500. If you imagine a forest of decision trees, that customer gets separated quickly—like a stranger in a small town. That’s how Isolation Forest works: it isolates the oddballs fast.”
5. One‑Class SVM
“This one’s like you knowing your regulars. You know who usually comes in, how they talk, and what they buy. If a stranger walks in and behaves very differently, you’d notice. One‑Class SVM learns what ‘normal’ looks like and flags anyone who doesn’t fit.
Jake whistled. “So you’re like a detective, using different
tools to spot suspicious entries.”
“Exactly,” Ethan said. “That’s why we call it anomaly detection in machine
learning.”
6. DBSCAN Clustering
“Finally, Uncle Jake, imagine groups of customers chatting in your shop. Families, students, office workers—they all form little clusters. But if one person is standing alone in the corner, not fitting into any group, you’d spot them. DBSCAN does the same with data—it finds the loners.”
Jake laughed. “So all these fancy names are just ways of saying: spot the odd customer in the shop.”
“Exactly, Uncle,” Ethan grinned. “Machine learning is just giving computers the same instincts you already use every day.”
Handling Outliers
Ethan continued:
“Once we find outliers, we have choices.”
- Remove
them if they’re errors.
- Cap
or Winsorize them (replace extreme values with percentiles).
- Transform
the data (like using a log scale to reduce skewness).
- Keep
them if they’re meaningful — like a genuine bulk order.
Jake nodded. “So it’s not just about deleting them. It’s
about understanding the story behind them.”
“Exactly,” Ethan said.
Outlier Detection at Scale with AWS
Ethan leaned back. “Now imagine you’re not just cleaning one
ledger, but millions of sales records. That’s where AWS services for outlier
detection come in.”
- Amazon
SageMaker Data Wrangler: Lets you visualize and detect anomalies
during preprocessing.
- AWS
Glue DataBrew: Offers built‑in transformations to spot and handle
outliers.
- Amazon
QuickSight: Helps visualize anomalies in dashboards.
- Amazon
EMR with Spark: Runs large‑scale anomaly detection jobs.
“These tools,” Ethan explained, “help businesses from small shops to global enterprises keep their data clean and their models accurate.”
Key Takeaways
- Outlier
detection in machine learning is critical for accurate models.
- Methods
include Z‑Score, IQR, Isolation Forest, One‑Class SVM, and DBSCAN.
- Outliers
can be errors or valuable insights — context matters.
- AWS Glue, SageMaker Data Wrangler, and QuickSight make anomaly detection scalable.
Chapter 4 – Enter AWS (
Jake leaned back in his chair. “Ethan, I get it now. My
ledger is messy, and cleaning it makes sense. But what if I had not one ledger,
but thousands? What if I ran shops across the country? I can’t sit here fixing
missing values and outliers by hand.”
Ethan grinned. “That’s exactly the kind of problem cloud
computing was built to solve. And when it comes to data cleaning in
machine learning, AWS has some of the best tools in the world.”
AWS Glue – The ETL Wizard
Ethan pulled up a demo.
“Think of AWS Glue as your automated assistant. It’s a serverless ETL
(Extract, Transform, Load) service. You point it to your raw data — whether
it’s in Amazon S3, a database, or logs — and Glue helps you clean,
transform, and prepare it for analysis.”
- Data
Catalog: Keeps track of all your datasets and their schema.
- Transformations:
Fixes structural errors, removes duplicate data, and
standardizes formats.
- Scalability:
Handles millions of rows without breaking a sweat.
Jake raised an eyebrow. “So Glue is like a cleaning crew
that works overnight, no matter how big the mess?”
“Exactly,” Ethan said.
AWS Glue DataBrew – No‑Code Magic
“But what if I don’t know how to code?” Jake asked.
“That’s where AWS Glue DataBrew comes in,” Ethan
explained.
“It’s a visual data preparation tool. You can click, drag, and apply
over 250 built‑in transformations — like handling missing values,
detecting outliers, or normalizing columns — all without writing a
single line of code.”
Jake chuckled. “So even I could use it?”
“Absolutely. It’s designed for business users as much as data scientists.”
Amazon SageMaker Data Wrangler – The Strategist
Ethan continued:
“Now, if you’re building ML models, SageMaker Data Wrangler is your best
friend. It’s part of Amazon SageMaker, and it gives you a single
interface to:
- Aggregate
data from multiple sources.
- Perform
feature engineering.
- Detect
anomalies with built‑in outlier detection methods.
- Export
clean datasets directly into training pipelines.”
Jake nodded. “So it’s like a one‑stop shop for preparing
data before training?”
“Exactly. It saves hours of manual work.”
Amazon EMR – The Muscle
Ethan leaned in. “But what if your dataset isn’t thousands
of rows, but billions? That’s when you need Amazon EMR. It’s a managed
cluster platform that runs Apache Spark and other big data frameworks.
Perfect for large‑scale data wrangling and anomaly detection in
machine learning.”
Jake whistled. “So EMR is like hiring an army of workers
instead of just one cleaner.”
“Exactly,” Ethan said.
Amazon S3 – The Vault
Finally, Ethan pointed to the cloud icon.
“All of this data — raw, cleaned, transformed — needs a home. That’s Amazon
S3 (Simple Storage Service). It’s the vault where your data lives, ready to
be pulled into Glue, DataBrew, or SageMaker whenever you need it.”
Why AWS Matters for Businesses
Ethan summed it up:
“Uncle Jake, whether you’re Apple predicting iPhone sales, Amazon recommending
products, or a small shopkeeper avoiding wasted stock, the principle is the
same: clean data fuels better machine learning models. AWS just makes it
scalable, automated, and cost‑effective.”
Jake smiled. “So AWS is like upgrading from my broom and
dustpan to a full‑blown cleaning factory.”
“Exactly,” Ethan laughed.
The ML Lifecycle, Data Drift, and Continuous Monitoring
Jake leaned back, satisfied. His ledger was now neat, his
outliers investigated, and his data transformed into a clean, structured
format.
He smiled. “So that’s it, right? Once the data is clean, I
can just build my machine learning model and relax?”
Ethan chuckled. “If only it were that simple, Uncle Jake.
Data cleaning isn’t a one‑time chore. It’s an ongoing battle. Why?
Because the world keeps changing — and so does your data.”
What is Data Drift?
Ethan pulled up another chart.
“See this? Last year, most of your sales were iPhone 14 models. This year, it’s
iPhone 15. That’s a shift in data distribution. In machine learning, we
call this data drift.”
- Data
Drift in Machine Learning: When the statistical properties of your
input data change over time.
- Concept
Drift: When the relationship between inputs and outputs changes (e.g.,
rainy days used to mean fewer sales, but now online orders balance it
out).
Jake frowned. “So even if my model was perfect last year, it
might fail this year?”
“Exactly,” Ethan said. “That’s why we monitor and retrain models regularly.”
The Role of Training, Validation, and Test Sets
Ethan explained further:
“When we build a model, we split the data into three parts:
- Training
Set: Used to teach the model.
- Validation
Set: Used to tune hyperparameters and prevent overfitting.
- Test
Set: Used to evaluate final performance on unseen data.
But here’s the catch: if your training set is clean but your production data drifts, your model performance will drop. That’s why continuous data cleaning and monitoring are essential.”
The ML Lifecycle: A Continuous Loop
Ethan drew a circle on the whiteboard:
- Data
Collection (structured, semi‑structured, and unstructured data).
- Data
Cleaning & Preprocessing (handling missing values, outliers,
duplicates).
- Feature
Engineering (turning raw data into predictive signals).
- Model
Training (using the training set).
- Validation
& Testing (fine‑tuning with the validation set, evaluating with
the test set).
- Deployment
(putting the model into production).
- Monitoring
& Feedback (detecting drift, anomalies, and errors).
- Back
to Cleaning (when drift or bias is detected).
“This is the machine learning lifecycle,” Ethan said.
“It’s not a straight line — it’s a loop. Every time your data changes, you go
back, clean again, and retrain.”
AWS Tools for Fighting Drift
Ethan added:
“AWS even helps with this part.
- Amazon
SageMaker Model Monitor: Detects data drift and alerts you when
input data no longer matches training data.
- AWS
Glue Data Catalog: Tracks data lineage, so you know where your
data came from and how it’s been transformed.
- Amazon
CloudWatch: Monitors metrics and anomalies in real time.
- Amazon
EMR: Handles large‑scale reprocessing when retraining is needed.
These services make sure your models stay accurate, even as
the world shifts.”
Real‑World Analogy
Jake thought for a moment. “So it’s like farming. I can’t
just plant once and expect crops forever. I have to water, weed, and replant
every season.”
Ethan nodded. “Exactly. In ML, data drift is like changing weather. If you don’t adapt, your harvest — your predictions — will fail.”
Key Takeaways
- Data
drift in machine learning is inevitable — models degrade over time.
- Splitting
data into training, validation, and test sets ensures fair
evaluation.
- The ML
lifecycle is a continuous loop of cleaning, training, deployment, and
monitoring.
- AWS
SageMaker Model Monitor, Glue Data Catalog, and CloudWatch
help detect drift and maintain data quality.
- Businesses
must adapt to semi‑structured data (like JSON logs) and unstructured
data (like text, images, video) — cleaning them is just as important
as structured tables.
Dimensionality Reduction Techniques (PCA & Feature Selection)
Jake looked at Ethan’s laptop. The dataset now had dozens of
columns: sales numbers, dates, weather, promotions, customer types, even notes
about holidays.
Jake frowned. “This feels overwhelming. Do we really need
all of this?”
Ethan smiled. “Not always. Sometimes, too many features
confuse the model. That’s where dimensionality reduction in machine learning
comes in. It’s like decluttering your storeroom — keeping only what matters.”
Techniques Ethan Explained:
1. Principal Component Analysis (PCA)
Ethan drew a quick sketch. “Uncle, imagine you’ve got ten different brands of PC in your storeroom. Customers don’t care about all the tiny differences — they just see ‘premium PC’ and ‘regular PC.’ PCA does the same: it takes many details and combines them into a few big categories, while still keeping most of the important information.”
Jake nodded. “So instead of ten shelves, I just need two — premium and regular.”
“Exactly, Uncle,” Ethan said.
2. Feature Selection
“Now think about your sales records,” Ethan continued. “Does the color of your shop walls affect how many iPhones you sell?”
Jake laughed. “Of course not.”
“Right. But promotions or holidays definitely do. Feature selection is like you deciding which details matter for sales and ignoring the rest. It’s like keeping track of discounts and festival days, but not wasting time recording the color of the curtains.”
3. Binning / Discretization
Ethan pulled up a chart of customer ages. “Suppose you have ages like 21, 22, 23, 24… all the way to 70. Instead of treating each age separately, you could group them into bins: 20s, 30s, 40s, and so on. That way, patterns become clearer. It’s like you arranging your stock in price ranges — budget phones, mid-range, and premium — instead of tracking every single rupee difference.”
Jake chuckled. “So it’s like keeping the best-selling iPhones in stock and ignoring the ones nobody buys.”
“Exactly, Uncle,” Ethan said with a grin. “Less clutter, better focus. The model learns faster, and you get clearer insights.”
- Principal
Component Analysis (PCA): Transforms many variables into fewer
“principal components” that still capture most of the information.
- Feature
Selection: Choosing only the most relevant features (e.g., sales date
and promotions might matter more than the color of the shop walls).
- Binning/Discretization: Grouping continuous values into intervals to simplify patterns.
Jake chuckled. “So it’s like keeping the best-selling
iPhones in stock and ignoring the ones nobody buys.”
“Exactly,” Ethan said. “Less clutter, better focus.”
Real-World Business Case Studies & ML Pipelines
Ethan leaned forward. “Uncle Jake, let me show you how big companies use these same principles.”
Case Study 1: Fraud Detection in Banking
Banks use outlier detection in machine learning to
spot unusual transactions. A sudden $10,000 withdrawal from a small account?
That’s flagged as an anomaly. Techniques like Isolation Forest and One‑Class
SVM help prevent fraud.
Case Study 2: Healthcare Anomaly Detection
Hospitals monitor patient vitals. If a heart rate suddenly
spikes outside the normal range, it’s an outlier. Detecting it early can
save lives. Here, data cleaning ensures no missing or corrupted sensor
readings mislead the system.
Case Study 3: Retail Demand Forecasting
Just like Jake’s shop, global retailers use data
preprocessing techniques to predict demand. By cleaning sales data,
handling missing values, and reducing noise, they avoid overstocking or
understocking. AWS services like SageMaker Data Wrangler and AWS Glue
automate this at scale.
Jake’s eyes widened. “So the same tools that help me can
also help banks, hospitals, and global retailers?”
“Exactly,” Ethan said. “That’s the beauty of machine learning pipelines
— they scale from small shops to Fortune 500 companies.”
Data Cleaning Best Practices & Common Pitfalls
Ethan leaned back. “Now, Uncle Jake, let me give you the
golden rules of data cleaning best practices.”
Best Practices
- Document
Every Transformation: Keep track of what you cleaned, imputed, or
removed.
- Automate
with ETL Pipelines: Use AWS Glue or DataBrew to avoid
manual errors.
- Visualize
Before You Decide: Box plots, scatter plots, and histograms reveal
hidden issues.
- Balance
Cleaning with Preservation: Don’t over‑clean — sometimes outliers are
valuable.
- Monitor
Continuously: Use SageMaker Model Monitor to detect data
drift.
Common Pitfalls
- Over‑Cleaning:
Removing too much data and losing important signals.
- Ignoring
Bias: Cleaning without checking for fairness can reinforce
discrimination.
- One‑Time
Cleaning: Forgetting that cleaning is part of the ML lifecycle,
not a one‑off task.
Jake nodded. “So it’s like running my shop. I can’t just
clean once, I need a routine. And I can’t throw away everything unusual —
sometimes that’s where the profit is.”
“Exactly,” Ethan said.
Practice AWS ML Cleaning for Free/Low Cost
Ethan looked at Jake, anticipating his next concern.
"Now, all these tools sound expensive, Uncle, but remember we're aiming
for smart practice. For a student, you don't need to run a billion-row
job."
"The AWS Free Tier" is your best friend here. You
can practice most of the core concepts at low or no cost:
- Amazon
S3: The Free Tier includes 5GB of standard storage, which is more than
enough for storing many small-to-medium datasets (like your 'ledger').
- AWS
Glue DataBrew: This tool is often the cheapest way to start. It
charges per session, not per hour, and the free tier includes a
significant number of interactive sessions each month. You can visually
clean a dataset without incurring Spark cluster costs.
- Amazon
SageMaker Studio Lab: This is a completely free offering from
AWS. It gives you an environment with CPU and GPU compute power to run
notebooks. You can perform data wrangling and feature
engineering using Python libraries (like Pandas and Scikit-learn) on
small datasets you store in S3, without paying for SageMaker compute
time.
"The key is to use small, structured datasets for learning the concepts and shut down any resources like EMR or SageMaker when you're done. That's how you learn the tools without emptying your wallet."
The Lesson (Conclusion)
As the evening ended, Jake closed his ledger with a smile.
“I never thought my messy notes could become the foundation of something
powerful. But now I see — clean data is the soil, outliers are the weeds,
and AWS is the farming equipment that makes it scalable.”
Ethan grinned. “That’s the secret, Uncle Jake. Whether it’s
Apple predicting iPhone sales, Amazon recommending products, or your shop
avoiding wasted stock — the principle is the same: garbage in, garbage out.
Clean data leads to smarter models, better insights, and stronger business
outcomes.”
Jake nodded. “So the journey isn’t just about machine
learning. It’s about building trust in the numbers.”
That's the end of this post.
Final Takeaways for you from exam point of you, i will explain the topics in our next posts in detail wherever required. !
- Data
Cleaning in Machine Learning is the foundation of every successful
model.
- Outlier
Detection separates errors from valuable insights.
- Dimensionality
Reduction simplifies datasets without losing meaning.
- Business
Case Studies prove these concepts matter in finance, healthcare, and
retail.
- AWS
Services (Glue, DataBrew, SageMaker, EMR, S3) make cleaning scalable
and automated.
- Best Practices ensure long‑term success, while avoiding pitfalls like over‑cleaning or ignoring bias.