5 Simple Linear regression.ipynb

Kernel: Python 3 (ipykernel)

Regression in Machine Learning

What is Regression?

Regression is a supervised learning technique used when the target variable is continuous. It models the relationship between a dependent variable (target) and one or more independent variables (features).

Goal: Predict a numerical value (e.g., salary, price, temperature) based on input data.

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Why Use Regression?

• Predict housing prices based on size, location, etc.

• Forecast sales or stock prices

• Estimate student marks based on study hours

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Types of Regression

Type	Description	Example
Linear Regression	One dependent, one/multiple independent vars	Salary vs Experience
Polynomial Regression	Features raised to a power (non-linear curves)	Age vs Cholesterol levels
Ridge/Lasso Regression	Regularized linear regression	Used to prevent overfitting
Logistic Regression (Special)	Used for classification problems	Yes/No outcomes

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Simple Linear Regression Formula

Example: Salary Prediction

Experience (Years)	Salary (Lakhs)
1	2.5
2	3.2
3	3.8
4	4.5
5	5.0

Model may learn: [ \text{Salary} = 0.58 \times \text{Experience} + 1.95 ]

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Visual Representation

• The regression line tries to minimize the distance (error) between actual and predicted values.

• In simple linear regression, it’s a straight line.

• In polynomial regression, it’s a curve.

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Summary

• Regression is used when the target is continuous.

• Linear regression is the most common starting point.

• Helps understand relationships and make predictions.

# Importing required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

#  Step 1: Create Simple Dataset
data = {
    'Experience_Years': [1, 2, 3, 4, 5, 6, 7, 8],
    'Salary_Lakhs': [2.5, 3.2, 3.8, 4.5, 5.0, 5.8, 6.4, 7.0]
}

df = pd.DataFrame(data)
print("Sample Data")
print(df)

# 🔧 Step 2: Define features (X) and target (y)
X = df[['Experience_Years']]  # 2D input
y = df['Salary_Lakhs']        # 1D target

# 🔧 Step 3: Build and Train Model
model = LinearRegression()
model.fit(X, y)

# Step 4: Predict on training data
df['Predicted_Salary'] = model.predict(X)

# Step 5: Visualize the regression line
plt.figure(figsize=(8,5))
plt.scatter(X, y, color='blue', label='Actual Salary')
plt.plot(X, df['Predicted_Salary'], color='red', label='Predicted Line')
plt.xlabel("Years of Experience")
plt.ylabel("Salary (in Lakhs)")
plt.title("Simple Linear Regression Example")
plt.legend()
plt.grid(True)
plt.show()

# Step 6: Display model coefficient and intercept
print("\n Regression Equation:")
print(f"Salary = {model.coef_[0]:.2f} * Experience + {model.intercept_:.2f}")

Sample Data
   Experience_Years  Salary_Lakhs
0                 1           2.5
1                 2           3.2
2                 3           3.8
3                 4           4.5
4                 5           5.0
5                 6           5.8
6                 7           6.4
7                 8           7.0

 Regression Equation:
Salary = 0.64 * Experience + 1.88