📈 Linear regression is used to predict a continuous outcome based on independent variables.
📊 Logistic regression is used to predict a binary outcome, especially for classification problems.
📊 Linear regression is used to model the relationship between the size and price of houses.
🔍 Logistic regression can be used to predict customer behavior or identify spam emails.
✨ By fitting a line of best fit to the data, linear regression can be used to predict the price of a new house based on its size.
📉 Linear regression is used to predict continuous outcomes by modeling the relationship between variables.
📊 Logistic regression is used to predict binary outcomes by modeling the relationship between variables.
🔎 Logistic regression can be used to predict the probability of a certain outcome.
🔑 Linear regression and logistic regression are two different models used for different types of problems.
💡 Logistic regression can be used to model various types of outcomes, not just binary outcomes.
❓ Linear and logistic regression can be used together in multi-class classification problems.
📊 Linear regression is used to predict numerical values, while logistic regression is used to predict categorical values.
🍎🍊🍐 Logistic regression can be used to predict the type of fruit based on its color, shape, and other features.
💰📉 Both linear and logistic regression are valuable tools in data science for predicting stock prices and customer churn.
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