In real life, many things are related to each other.
For example, more study hours usually mean better marks.
Machine Learning tries to find this relationship automatically using data.

In machine learning, we call the input X and the output Y.

Machine Learning is a technique where a computer learns patterns from data.
It tries to understand how input values (X) are related to output values (Y).
By learning this relationship, the machine can make predictions for new data.

X is what we give to the machine.
Y is what we want the machine to predict.

Suppose we want a computer to predict marks.

You give it data like:

Hours Studied	Marks
2	40
4	60
6	80

How the Machine “Finds” the Relationship

First, we give the machine many examples of X and Y.

Second, the machine looks for patterns in the data.

Third, it creates a rule or model that best connects X and Y.

That is

The machine learning process starts by providing historical data containing X and Y values.
The algorithm analyzes this data and identifies patterns.
Based on these patterns, it learns a model that represents the relationship between X and Y.

We don’t tell the computer the formula.
We only give examples.
The computer learns the relationship.

That learning = Machine Learning

If we plot X on the horizontal axis and Y on the vertical axis,
we can see a pattern.

Machine learning finds the best line or curve that fits these points.

Often, the relationship between X and Y can be visualized using a graph.
Machine learning algorithms find the best-fitting line or curve that explains how changes in X affect Y.

Why Finding X–Y Relationship is Important

Once the machine learns the relationship, it can predict Y for a new X.

If a student studies for 5 hours, the model can predict expected marks.

After learning the relationship between X and Y, the model can be used to make predictions on new, unseen data.
This ability to predict makes machine learning useful in many real-world applications.

So remember, machine learning is not magic.
It is just finding relationships in data and using them to make predictions.

Conclusion:

To summarize, machine learning works by identifying relationships between input (X) and output (Y).
Instead of manually programming rules, machines learn these relationships from data.
This allows systems to improve automatically and make intelligent predictions.

Machine Learning learns the relationship between X and Y using data and uses it to predict future outputs.