Regression model

regression model

A regression model is a machine learning / statistical model used to predict a continuous numerical value based on one or more input variables.

1️⃣ What is a Regression Model?

A regression model finds the relationship between variables.

📌 Example

• Predict house price based on:

  • size (sq ft)
  • number of rooms
  • location

Here:

• Input (X) → size, rooms, location
• Output (Y) → price (a number)

2️⃣ Why Do We Use Regression?

We use regression to: ✔ Predict values ✔ Understand relationships ✔ Analyze trends

📌 Common predictions

• Student marks
• Salary
• Sales
• Temperature
• House prices

3️⃣ Simple Example (Classroom Friendly)

Predict marks based on study hours

📈 The model learns:

“As study hours increase, marks increase.”

4️⃣ Types of Regression Models

🔹 1. Linear Regression

Most basic and commonly used.

Formula:

y = mx + b

• x → input
• y → output
• m → slope
• b → intercept

📌 Example:

Marks = 10 × StudyHours + 25

🔹 2. Multiple Linear Regression

Uses more than one input

📌 Example:

Salary = Experience + Education + Skills

🔹 3. Polynomial Regression

Used when data is curved, not straight.

📌 Example:

• Speed vs fuel consumption

🔹 4. Logistic Regression (Special Case)

Used for Yes/No outcomes (classification).

📌 Example:

• Pass / Fail
• Spam / Not Spam

⚠ Although named “regression”, it’s used for classification.

5️⃣ Python Example

from sklearn.linear_model import LinearRegression

# Input data (study hours)
X = [[1], [2], [3], [4], [5]]
y = [35, 45, 55, 65, 75]

model = LinearRegression()
model.fit(X, y)

# Predict marks for 6 hours
print(model.predict([[6]]))

6️⃣ Real-Life Activities for Students 👨‍🏫

✔ Measure height vs weight ✔ Predict electricity bill from units ✔ Predict sales from advertising cost ✔ Predict marks from attendance

7️⃣ Key Points to Remember

✅ Regression predicts numbers ✅ Shows relationship between variables ✅ Linear regression draws a best-fit line ✅ Widely used in ML, data science, economics