The big secret is in your data!
Bad Data = Bad Model (No Matter How Fancy Your Algorithm Is)
Let’s use a real-life example. As data scientists or machine learning engineers, you’ve been asked to build a machine learning model to predict customer spending habits. You collect a dataset, train your model and expect magic. Instead, your model produces nonsense predictions.
Confused, you check your data and see:
Customer ages range from 5 to 300 years old
Missing income values are replaced with zero instead of reasonable estimates
Duplicate entries where the same user appears multiple times
At this point, it doesn’t matter if you’re using Linear Regression or Support Vector Machine, your model is doomed from the start. Because in machine learning, garbage in = garbage out.
So, where do you begin?
Step 1: Where Do You Even Get Good Data?
Before you can clean anything, you need quality data. And not just any data; relevant, structured and usable data.
Some of the Best Places to Find Machine Learning Datasets
Kaggle – One of the best places to find datasets for all sorts of ML problems.
Google Dataset Search – Think of it like Google, but for datasets.
Government & Research Portals – Real world structured data from data.gov, WHO and other research sources.
Your Own Data – If you’re working on a real-world project, the best data often comes from your own app, website or APIs.
Finding data is just Step 1. Now comes the real work: cleaning and preparing it.
Step 2: Data Cleaning – Because Raw Data is Always a Mess
Raw data is never perfect, it comes with so many issues such as missing values, null values, etc. Before training any model, you have to clean it. Below are the steps you can take to clean your data.
Essential Data Cleaning Steps
Handle Missing Values
Drop rows if there’s too much missing data.
Fill gaps using the mean, median or a relevant estimate (Don’t use zero!).
Remove Duplicates
- If the same user appears multiple times, your model will learn the wrong patterns.
Fix Outliers
- If most customers are between 20-50 years old, and suddenly you have 200 years old, you need to investigate.
At this point, your data is clean but still raw. Next, we make it useful.
Step 3: Feature Engineering – The secret of every ML
After cleaning your data, you don’t just feed it into the ML model. You need to do feature engineering. Feature engineering is about making good data even better by streamlining your data columns to have only relevant data.
How to Engineer Better Features for Your Model
Following the example above, you can get insights from your data by:
Transforming Raw Text into Something Useful → Instead of using raw customer reviews, convert them into:
Sentiment scores (positive, neutral, negative)
Keyword frequency (how often specific words appear)
Text embeddings (turning words into numerical representations for better model learning)
Extracting Insights from Transactions → Instead of just tracking purchases, break them into:
Total purchase amount (spending behavior)
Purchase frequency (loyal customers vs. one-time buyers)
Time since last purchase (customer retention insights)
Scaling Numerical Data → Some models work better when values are normalized between 0 and 1.
These small changes can have a massive impact on your model’s accuracy.
Step 4: Now, You Can Train Your Model
At this point, your data is clean, structured and optimized. Now, it’s time to feed it into your ML model. Regardless of the ML model you’re using which could be Scikit-learn, TensorFlow, or PyTorch, your model will now have a much better chance of producing accurate and reliable predictions.
Final Thoughts
Most people think machine learning is all about selecting the best model. But in reality, your data is the most important. The best algorithm in the world can’t fix bad data.
Spend time collecting, cleaning and engineering your features and your model will thank you.
What’s one thing you wish you knew about data before training your first ML model? Let’s discuss!