Lesson 6.2 – Linear & Logistic Regression Models | Business Analytics Applications

📈 Linear Regression

Linear Regression models the relationship between a dependent variable (outcome) and one or more independent variables (predictors) using a straight line. It is mainly used for continuous outcomes.

• ✔ Helps predict sales, revenue, or prices
• ✔ Useful for trend forecasting (e.g., monthly sales growth)
• ✔ Identifies how much each factor influences the outcome

# Example: Predicting sales from advertising spend
import pandas as pd
from sklearn.linear_model import LinearRegression

# Dataset
data = {'Ad_Spend': [10, 20, 30, 40, 50], 'Sales': [25, 40, 55, 70, 85]}
df = pd.DataFrame(data)

X = df[['Ad_Spend']]
y = df['Sales']

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

print("Coefficient:", model.coef_[0])
print("Intercept:", model.intercept_)
    

Business Example: A company estimates how advertising spend impacts revenue.

📊 Logistic Regression

Logistic Regression is used when the outcome variable is binary (Yes/No, True/False, 1/0). Instead of predicting exact values, it predicts the probability of an event occurring.

• ✔ Used in customer churn prediction (Will a customer leave?)
• ✔ Helps in fraud detection (Is this transaction fraudulent?)
• ✔ Supports HR (Will a candidate accept the job offer?)

# Example: Predicting if customer will purchase
import pandas as pd
from sklearn.linear_model import LogisticRegression

# Dataset
data = {'Age': [22, 25, 30, 35, 40], 'Purchase': [0, 0, 1, 1, 1]}
df = pd.DataFrame(data)

X = df[['Age']]
y = df['Purchase']

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

print("Prediction for Age 28:", model.predict([[28]]))
    

Business Example: Predicting whether a customer will buy a product based on their age or profile.

🔍 Linear vs Logistic Regression