Every machine learning project starts with data. You can have the most sophisticated algorithm in the world, but without proper data it’s completely useless. Understanding how data works in machine learning is fundamental to everything else you’ll learn.
Most beginners jump straight into algorithms and models without grasping what they’re actually learning from. This creates confusion when their models don’t work or produce strange results. Before building any machine learning model, you need to understand what you’re feeding it.
Data in machine learning isn’t just random numbers in a spreadsheet. It has structure, meaning, and purpose. The way you organize and prepare your data directly impacts how well your model performs. Let me show you exactly how data works in ML and why it matters so much.
What are features and labels in machine learning
The most important distinction in machine learning data is between features and labels. This is often written as X and Y, and understanding this difference is crucial.
Features are the input variables your model uses to make predictions. These are the characteristics or attributes that describe whatever you’re trying to predict. Think of features as the information you already know.
If you’re predicting house prices, your features might include square footage, number of bedrooms, location, age of the house, and whether it has a garage. Each of these is a separate feature that provides information about the house.
Labels are the outputs or targets you’re trying to predict. This is what you want your model to learn. In the house price example, the label would be the actual sale price of each house.
Your model learns by studying many examples where it can see both the features and the corresponding labels. It figures out patterns connecting the features to the labels. Once trained, you can give it just the features for a new house, and it will predict the label.
This X and Y relationship forms the foundation of supervised learning. X represents your input features. Y represents your output labels. The model learns the function that maps X to Y.
Different types of data your model can handle
Machine learning models work with several types of data, and knowing which type you have affects how you prepare it.
Numerical data consists of numbers that have mathematical meaning. Age, price, temperature, and distance are all numerical. You can perform math operations on this data that make sense. The average of 25 and 35 is 30, and that means something.
Numerical data splits into continuous and discrete. Continuous data can take any value within a range like temperature at 72.5 degrees. Discrete data comes in whole units like number of bedrooms which can be 2 or 3 but not 2.5.
Categorical data represents distinct categories or groups. Color, gender, country, and yes/no questions all produce categorical data. You can’t do meaningful math with categories. The average of red and blue isn’t purple in terms of data.
Ordinal data is a special type of categorical data where the categories have a natural order. Education level with categories like high school, bachelor’s, master’s, and doctorate has clear ordering even though the values aren’t numbers.
Text data requires special handling because models can’t directly work with words. You need to convert text into numerical representations through techniques like word embeddings or counting word frequencies.
Image data consists of pixels with color values. A 100×100 pixel color image has 30,000 numbers representing it when you consider red, green, and blue values for each pixel.
Understanding your data type matters because different types need different preparation techniques before feeding them to your model.
How datasets are structured for machine learning
A dataset is your collection of examples that the model learns from. Think of it as a table where each row is one example and each column is either a feature or the label.
In a house price dataset, each row represents one house. The columns might be square_feet, bedrooms, bathrooms, location, year_built, and price. The first five columns are your features. The last column is your label.
Most machine learning work uses tabular data that fits naturally into this row and column format. CSV files are the most common way to store and share this type of data.
Your dataset typically splits into training data and testing data. The model learns from the training data. You evaluate its performance on testing data it has never seen before. This split helps you know if your model actually learned useful patterns or just memorized the training examples.
A good dataset has enough examples for the model to learn meaningful patterns. How many is enough depends on the problem complexity, but hundreds or thousands of examples are typical minimum requirements.
Preparing your data for machine learning
Raw data almost never works directly in machine learning models. You need to clean and prepare it first. This process is called data preprocessing and takes up most of the time in real ML projects.
Missing values are common in real datasets. Some rows might have blank cells where data wasn’t recorded. You need to decide whether to remove those rows, fill in the blanks with reasonable guesses, or use techniques that handle missing values.
Scaling numerical features often improves model performance. If one feature ranges from 0 to 100 and another from 0 to 1000000, the larger numbers can dominate the model’s learning. Scaling puts all features on similar ranges.
Encoding categorical features converts categories into numbers that models can process. If you have a color feature with values red, blue, and green, you might convert these to numbers like 0, 1, and 2. More sophisticated encoding techniques exist for different situations.
Outliers are extreme values that differ significantly from other data points. A house listed for 50 million dollars when most houses cost 200,000 to 500,000 is an outlier. These can throw off your model’s learning if not handled properly.
Loading and exploring data with Python
Let me show you how to actually work with machine learning data using Python and pandas.
import pandas as pd
import numpy as np
# Load a dataset from a CSV file
data = pd.read_csv('house_prices.csv')
# Look at the first few rows
print(data.head())
# Check the shape (rows, columns)
print(f"Dataset has {data.shape[0]} rows and {data.shape[1]} columns")
# Get information about data types and missing values
print(data.info())
# See basic statistics for numerical columns
print(data.describe())
# Check for missing values
print(data.isnull().sum())
This code loads your data and gives you an immediate understanding of what you’re working with. You can see the features, check for problems, and understand the data types.
Separating features from labels looks like this:
# Assuming 'price' is your label column
X = data.drop('price', axis=1) # Features
y = data['price'] # Labels
print(f"Features shape: {X.shape}")
print(f"Labels shape: {y.shape}")
Now you have your features in X and labels in y, ready for model training.
Handling missing values with pandas:
# Remove rows with any missing values
data_clean = data.dropna()
# Or fill missing values with the column mean
data['bedrooms'].fillna(data['bedrooms'].mean(), inplace=True)
# Fill categorical missing values with the most common value
data['location'].fillna(data['location'].mode()[0], inplace=True)
Why data quality matters more than algorithms
You can have the most advanced machine learning algorithm available, but if your data is poor quality, your results will be poor. The old saying garbage in, garbage out applies perfectly to machine learning.
Good data means accurate labels, relevant features, sufficient examples, and proper representation of the problem you’re solving. If you’re building a model to predict customer behavior but your data only includes customers from one demographic, your model won’t work well for others.
Biased data creates biased models. If your training data reflects historical prejudices or unbalanced representation, your model learns and perpetuates those biases. This is why data collection and preparation require careful thought about fairness and representation.
Feature engineering, the process of creating new features from existing ones, often improves model performance more than switching to a fancier algorithm. Understanding your data well enough to create meaningful features is a valuable skill.
Moving forward with your data
Understanding data in machine learning gives you the foundation for everything else. You now know what features and labels are, how different data types work, what makes a good dataset, and how to prepare data for training.
The next step in your machine learning journey involves understanding how models measure their performance. Ready to learn how ML models know when they’re making mistakes? Check out our guide on loss functions in machine learning to see how models calculate and minimize their errors during training.
