Introduction to Regression

 

 What is a Regression Problem?

A regression problem is a type of supervised machine learning task where the goal is to predict a continuous value based on input features.

📌 Examples of Regression Problems:

• Predicting house prices based on size, location, and number of rooms.

• Estimating the temperature for tomorrow based on weather conditions.

• Predicting student scores based on study hours.

In all these examples, the output is a number (not a category), so we use regression models.

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Key Concepts

Term	Meaning
Features (X)	Input variables (e.g., hours studied)
Target (y)	The value we want to predict (e.g., score)
Model	A function that learns the relationship between X and y
Training	Feeding the model with known X and y values to learn the pattern
Prediction	Using the model to estimate unknown y for a given X

🐍 Python Example: Simple Linear Regression

We'll use pandas, scikit-learn, and matplotlib (optional for plotting).

🔧 Step 1: Sample CSV File

Assume we have a CSV file named student_scores.csv with the following contents:

Hours,Score

2.5,21

5.1,47

3.2,27

8.5,75

3.5,30

1.5,20

9.2,88

This file has:

• Input feature: Hours studied

• Output/Target: Score

Python Code

import pandas as pd

from sklearn.linear_model import LinearRegression

import matplotlib.pyplot as plt  # optional for plot

# Step 1: Read the CSV file

data = pd.read_csv('student_scores.csv')

# Step 2: Separate the input (X) and output (y)

X = data[['Hours']]   # 2D array

y = data['Score']     # 1D array

# Step 3: Create the Linear Regression model

model = LinearRegression()

# Step 4: Train the model using the data

model.fit(X, y)

# Step 5: Make a prediction (e.g., for 6.5 hours of study)

predicted_score = model.predict([[6.5]])

print(f"Predicted Score for 6.5 hours of study: {predicted_score[0]:.2f}")

# Optional: Plot the data and regression line

plt.scatter(X, y, color='blue')  # actual data points

plt.plot(X, model.predict(X), color='red')  # regression line

plt.xlabel("Hours Studied")

plt.ylabel("Score")

plt.title("Study Hours vs Score")

plt.show()

Output

Predicted Score for 6.5 hours of study: 59.58

• Blue dots = actual data

• Red line = prediction line

💡 Notes:

• X = data[['Hours']] uses double square brackets because scikit-learn expects a 2D array for features.

• model.fit(X, y) tells the model to learn the best-fit line.

• model.predict([[6.5]]) returns the predicted score for 6.5 hours.