🟦🟩🟨 Logistic Regression Using Python

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🎯 Aim

To implement the Logistic Regression algorithm using Python to classify whether a patient has a disease based on their age.

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🧠 Theory

Logistic Regression is a Supervised Machine Learning algorithm used for classification problems. Unlike Linear Regression, which predicts continuous values, Logistic Regression predicts categories or classes.

Examples

• 📧 Spam / Not Spam
• 🏥 Disease / Healthy
• 🎓 Pass / Fail
• 💳 Fraud / Not Fraud

The output is usually:

• 0 → No
• 1 → Yes

Logistic Regression uses the Sigmoid Function to convert predictions into probabilities between 0 and 1.

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🌍 Real-Life Example

A hospital wants to predict whether a patient has diabetes based only on Age.

Training Data

Age	Disease
20	0
25	0
30	0
35	0
40	1
45	1
50	1
55	1

Here,

• 0 = Healthy
• 1 = Disease

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📝 Step 1: Import Required Libraries

from sklearn.linear_model import LogisticRegression

Explanation

• sklearn → Machine Learning library.
• linear_model → Contains regression algorithms.
• LogisticRegression → Used for classification.

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📝 Step 2: Create Input Data

X = [
    [20],
    [25],
    [30],
    [35],
    [40],
    [45],
    [50],
    [55]
]

Explanation

X stores the input feature (Age).

Each value is written inside another list because Scikit-learn expects 2D input.

X

20
25
30
35
40
45
50
55

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📝 Step 3: Create Output Data

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

Explanation

y stores the output labels.

Value	Meaning
0	Healthy
1	Disease

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📝 Step 4: Create the Model

model = LogisticRegression()

Explanation

This creates an empty Logistic Regression model.

At this stage:

✔ No learning

✔ No prediction

✔ Just an empty model

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📝 Step 5: Train the Model

model.fit(X, y)

Explanation

fit() teaches the model using the training data.

The model learns:

• Relationship between Age and Disease
• Probability of Disease

This is called the Training Phase.

Training Data
      │
      ▼
Model Learning
      │
      ▼
Trained Model

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📝 Step 6: Predict New Data

Suppose a new patient is 42 years old.

prediction = model.predict([[42]])

Explanation

The model compares age 42 with the learned pattern and predicts:

Either

0

or

1

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📝 Step 7: Display Prediction

print(prediction)

Output

[1]

Meaning

Disease

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📝 Step 8: Display User-Friendly Output

if prediction[0] == 1:
    print("Patient has Disease")
else:
    print("Patient is Healthy")

Output

Patient has Disease

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✅ Complete Python Program

# Logistic Regression Example

from sklearn.linear_model import LogisticRegression

# Input Data (Age)
X = [
    [20],
    [25],
    [30],
    [35],
    [40],
    [45],
    [50],
    [55]
]

# Output Data
# 0 = Healthy
# 1 = Disease

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

# Create Model
model = LogisticRegression()

# Train Model
model.fit(X, y)

# Predict
prediction = model.predict([[42]])

# Display Prediction
if prediction[0] == 1:
    print("Patient has Disease")
else:
    print("Patient is Healthy")

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💻 Expected Output

Patient has Disease

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🔍 Step-by-Step Flow

Import Library
      │
      ▼
Create Dataset
      │
      ▼
Create Logistic Regression Model
      │
      ▼
Train Model using fit()
      │
      ▼
Predict using predict()
      │
      ▼
Display Result

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📌 Understanding fit()

model.fit(X, y)

This is the most important line.

It means:

Teach the computer using
X → Input

y → Output

After this line, the model becomes trained.

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📌 Understanding predict()

model.predict([[42]])

Meaning:

Predict the output for a new patient whose age is 42 years.

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📌 Understanding prediction[0]

predict() returns a list (or array).

Example:

prediction = [1]

To access the first value:

prediction[0]

Result

1

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📊 Training Data Visualization

Age	Disease
20	Healthy
25	Healthy
30	Healthy
35	Healthy
40	Disease
45	Disease
50	Disease
55	Disease

The model learns that higher ages in this small example are associated with the "Disease" class.

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📈 Why Logistic Regression?

Linear Regression	Logistic Regression
Predicts numbers	Predicts categories
Output can be any value	Output is a probability (0–1) and a class
Used for Regression	Used for Classification

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⭐ Advantages

• ✔ Easy to implement
• ✔ Fast training
• ✔ Works well for binary classification
• ✔ Produces probability estimates
• ✔ Easy to interpret

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❌ Limitations

• ❌ Assumes a linear relationship between features and the log-odds
• ❌ Less effective for highly complex, non-linear data
• ❌ Sensitive to outliers in some situations

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🌍 Applications

• 🏥 Disease Prediction
• 📧 Spam Detection
• 💳 Credit Card Fraud Detection
• 🎓 Student Pass/Fail Prediction
• 🛒 Customer Purchase Prediction
• 🏦 Loan Approval Prediction

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🎯 Viva Questions

1. What is Logistic Regression?
2. Why is it called "Regression" if it is used for classification?
3. What is the role of the Sigmoid Function?
4. What does fit() do?
5. What does predict() do?
6. Why is the input written as [[42]] instead of [42]?
7. What is the meaning of prediction[0]?
8. What types of problems can Logistic Regression solve?

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📝 One-Line Revision

Logistic Regression is a supervised machine learning algorithm that predicts the probability of a data point belonging to a particular class and is mainly used for binary classification problems.