Using Scikit-learn (Basic Example)

Step-by-Step — Building a Classification Model

The following example uses the built-in Iris dataset — a classic beginner dataset containing measurements of three species of flowers. The goal is to classify which species a flower belongs to based on its measurements.

Step 1 — Load the Data

The dataset has 150 rows, 4 features (sepal length, sepal width, petal length, petal width), and 3 species (0, 1, 2).

Step 2 — Prepare Features and Target

Step 3 — Split Data into Training and Test Sets

Never train and test on the same data, the model would simply memorize the answers. Use train_test_split to divide your data.

Step 4 — Scale the Features

Feature scaling ensures all features are on the same numerical scale, which improves model performance and training speed.

Important: Always fit the scaler on training data only. Applying it to test data separately prevents data leakage, where test data influences the training process.

Step 5 — Choose and Train the Model

Here a Logistic Regression model is used — a simple, effective classification algorithm.

fit() is where the learning happens. The model analyses the training data and learns the patterns that separate the three species.

Step 6 — Make Predictions

Step 7 — Evaluate the Model

Making a Single Prediction

Once trained, the model can predict any new input instantly.

The Complete Code — All Steps Together

python

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score

import pandas as pd

import numpy as np

# Load data

iris = load_iris()

X = pd.DataFrame(iris.data, columns=iris.feature_names)

y = iris.target

# Split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Scale

scaler = StandardScaler()

X_train = scaler.fit_transform(X_train)

X_test  = scaler.transform(X_test)

# Train

model = LogisticRegression(random_state=42)

model.fit(X_train, y_train)

# Evaluate

y_pred = model.predict(X_test)

print(f"Accuracy: {accuracy_score(y_test, y_pred) * 100:.2f}%")

# Predict new sample

sample = scaler.transform([[5.1, 3.5, 1.4, 0.2]])

print("Prediction:", iris.target_names[model.predict(sample)[0]])

Output:

Accuracy: 96.67%

Prediction: setosa