Machine Learning (ML) enables computers to learn from data and make predictions without being explicitly programmed.

Python is widely used in ML because of libraries like:

• Scikit-learn
• NumPy
• Pandas
• Matplotlib

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What is Scikit-learn?

Scikit-learn is a popular Python library for machine learning.

Used for:

• Regression
• Classification
• Clustering
• Model evaluation

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Installing Scikit-learn

pip install scikit-learn

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Importing Scikit-learn

from sklearn import datasets

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Machine Learning Workflow

Typical ML process:

1. Collect Data
2. Prepare Data
3. Train Model
4. Test Model
5. Evaluate Results

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Types of Machine Learning

Type	Description
Supervised Learning	Uses labeled data
Unsupervised Learning	Uses unlabeled data
Reinforcement Learning	Learns using rewards

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Regression

Regression predicts continuous numerical values.

Examples:

• House prices
• Temperature
• Sales prediction

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Linear Regression

Linear regression finds relationship between variables.

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Example: Linear Regression

from sklearn.linear_model import LinearRegression
import numpy as np

# Input data
X = np.array([[1], [2], [3], [4], [5]])

# Output data
y = np.array([2, 4, 6, 8, 10])

# Create model
model = LinearRegression()

# Train model
model.fit(X, y)

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

print(prediction)

Output Example:

[12.]

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Regression Terms

Term	Meaning
Features	Input variables
Target	Output variable
Training Data	Data used for learning
Prediction	Model output

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Train-Test Split

Used to separate training and testing data.

from sklearn.model_selection import train_test_split
import numpy as np

X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 6, 8, 10])

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

print(X_train)
print(X_test)

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Classification

Classification predicts categories or labels.

Examples:

• Spam detection
• Disease prediction
• Image recognition

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Logistic Regression Example

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris

# Load dataset
iris = load_iris()

X = iris.data
y = iris.target

# Create model
model = LogisticRegression(max_iter=200)

# Train model
model.fit(X, y)

# Predict
prediction = model.predict([X[0]])

print(prediction)

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Decision Tree Classifier

from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris

iris = load_iris()

X = iris.data
y = iris.target

model = DecisionTreeClassifier()

model.fit(X, y)

print(model.predict([X[1]]))

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Clustering

Clustering groups similar data points.

It is an:

Unsupervised Learning

technique.

Examples:

• Customer segmentation
• Product grouping

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K-Means Clustering

from sklearn.cluster import KMeans
import numpy as np

X = np.array([
    [1, 2],
    [1, 4],
    [5, 8],
    [8, 8]
])

model = KMeans(
    n_clusters=2,
    random_state=0
)

model.fit(X)

print(model.labels_)

Output Example:

[1 1 0 0]

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Model Evaluation

Model evaluation checks model performance.

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Regression Evaluation

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Mean Absolute Error (MAE)

from sklearn.metrics import mean_absolute_error

actual = [10, 20, 30]
predicted = [12, 18, 29]

mae = mean_absolute_error(
    actual,
    predicted
)

print(mae)

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Mean Squared Error (MSE)

from sklearn.metrics import mean_squared_error

mse = mean_squared_error(
    actual,
    predicted
)

print(mse)

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R² Score

from sklearn.metrics import r2_score

score = r2_score(
    actual,
    predicted
)

print(score)

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Classification Evaluation

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Accuracy Score

from sklearn.metrics import accuracy_score

actual = [1, 0, 1, 1]
predicted = [1, 0, 1, 0]

accuracy = accuracy_score(
    actual,
    predicted
)

print(accuracy)

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Confusion Matrix

from sklearn.metrics import confusion_matrix

cm = confusion_matrix(
    actual,
    predicted
)

print(cm)

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Classification Report

from sklearn.metrics import classification_report

print(classification_report(
    actual,
    predicted
))

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Cross Validation

Improves model reliability.

from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LinearRegression
import numpy as np

X = np.array([[1], [2], [3], [4]])
y = np.array([2, 4, 6, 8])

model = LinearRegression()

scores = cross_val_score(
    model,
    X,
    y,
    cv=2
)

print(scores)

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Saving and Loading Models

Use:

joblib

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Save Model

import joblib

joblib.dump(model, "model.pkl")

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Load Model

model = joblib.load("model.pkl")

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Practical Example

House Price Prediction

from sklearn.linear_model import LinearRegression
import numpy as np

# Area of houses
X = np.array([
    [500],
    [1000],
    [1500],
    [2000]
])

# Prices
y = np.array([
    100000,
    200000,
    300000,
    400000
])

model = LinearRegression()

model.fit(X, y)

price = model.predict([[2500]])

print("Predicted Price:", price[0])

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Advantages of Scikit-learn

✅ Simple and easy API
✅ Fast machine learning models
✅ Good documentation
✅ Supports many algorithms
✅ Works well with NumPy and Pandas

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Summary

In this chapter you learned:

✅ Scikit-learn basics
✅ Regression
✅ Classification
✅ Clustering
✅ Model evaluation