Build a Classifier With Python

What Are We Building?

Our goal is simple:

This is a classification problem because the output is a category rather than a continuous numerical value.

Step 1 — Prepare the Dataset

First, we need some training data. Each student has a number of study hours and a result.

Machine Learning models work with numbers, so we will represent the classes as:

Step 2 — Create X and y

In scikit-learn, we normally separate the input features from the target labels.

We commonly call them X and y.

X = [
    [1],
    [2],
    [3],
    [5],
    [6],
    [7]
]

y = [
    0,
    0,
    0,
    1,
    1,
    1
]

Step 3 — Import the Classifier

We will use Logistic Regression from scikit-learn.

from sklearn.linear_model import LogisticRegression

This imports the Logistic Regression classifier that we will use to build our model.

Step 4 — Create the Model

Next, we create an instance of Logistic Regression.

model = LogisticRegression()

At this point, we have created the model object, but it has not learned anything yet.

Step 5 — Train the Model

Now we give the training data to the model using the fit() method.

model.fit(X, y)

This is where the model learns the relationship between study hours and the Pass/Fail labels.

Step 6 — Make a Prediction

After training, we can give the model a new student's study hours.

Suppose a new student studied for 6 hours.

prediction = model.predict([[6]])

print(prediction)

The model uses what it learned from the training data and predicts the class for the new student.

Step 7 — See the Prediction Probability

We can also ask the model for the probability of each class using predict_proba().

probability = model.predict_proba([[6]])

print(probability)

The output will contain a probability for each class. For example, it might look conceptually like:

This means the model considers the Pass class more likely for this example.

The Complete Python Code

Now let's put all the steps together.

from sklearn.linear_model import LogisticRegression

# Training data
X = [
    [1],
    [2],
    [3],
    [5],
    [6],
    [7]
]

# Target labels
y = [
    0,
    0,
    0,
    1,
    1,
    1
]

# Create the model
model = LogisticRegression()

# Train the model
model.fit(X, y)

# Make a prediction
prediction = model.predict([[6]])

# Get probabilities
probability = model.predict_proba([[6]])

print("Prediction:", prediction)
print("Probability:", probability)

The important part is not memorizing every line. Understand the sequence.

What Actually Happened?

Let's translate the Python code into simple Machine Learning language.

One More Simple Example

The same process can be used for other binary classification problems.

The problem changes, but the basic Machine Learning workflow remains similar:

Check Your Understanding