What is the difference between linear regression and logistic regression?

Linear Regression vs Logistic Regression

Linear regression and logistic regression are both powerful statistical methods used for predicting outcomes, but they differ in their target variable and how they model the relationship between variables.

What is Linear Regression?

Linear regression is a statistical technique that uses a linear equation to model the relationship between a dependent variable (the outcome) and one or more independent variables (predictors). It aims to find the best-fitting line that represents the relationship between the variables, allowing you to predict the value of the dependent variable based on the independent variable(s).

What is Logistic Regression?

Logistic regression is a statistical technique used to predict the probability of a binary outcome (e.g., yes/no, success/failure) based on one or more predictor variables. It uses a sigmoid function to transform the linear combination of predictors into a probability between 0 and 1.

Key Differences

Applications

Linear Regression

• Predicting housing prices based on size, location, and number of bedrooms
• Estimating sales revenue based on advertising spend
• Forecasting temperature based on time of day and season

Logistic Regression

• Predicting whether a customer will click on an ad based on their demographics and browsing history
• Identifying whether a loan applicant will default based on their credit score and income
• Classifying emails as spam or not spam based on keywords and sender address

Example Code (Python)

Linear Regression

import pandas as pd
from sklearn.linear_model import LinearRegression

# Load data
data = pd.read_csv("data.csv")

# Create linear regression model
model = LinearRegression()

# Fit model to data
model.fit(data[['independent_variable']], data['dependent_variable'])

# Predict outcome for new data
new_data = pd.DataFrame({'independent_variable': [value]})
prediction = model.predict(new_data)

print(prediction)

Logistic Regression

import pandas as pd
from sklearn.linear_model import LogisticRegression

# Load data
data = pd.read_csv("data.csv")

# Create logistic regression model
model = LogisticRegression()

# Fit model to data
model.fit(data[['independent_variable']], data['dependent_variable'])

# Predict probability for new data
new_data = pd.DataFrame({'independent_variable': [value]})
prediction = model.predict_proba(new_data)[:, 1]

print(prediction)

Conclusion

Linear regression and logistic regression are distinct statistical methods that cater to different types of data and prediction goals. Understanding their differences and applications is crucial for choosing the right tool for your predictive modeling tasks.