Pranay Jha's Insights

Classification

Regression Analysis

1. Linear Regression
   1. Simple Linear Regression
   2. Multiple Linear Regression
2. Logistic Regression

Linear vs Logistic Regression

More about Logistic Regression

• Logistic regression is the appropriate regression analysis to conduct when the dependent variable is binary.
• It is a predictive analysis like all other regression analysis.
• It is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval, or ratio-level independent variables.
• It used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead, healthy/sick, or normal/malicious.
• It predicts a dependent data variable by analyzing the relationship between one or more existing independent variables.
• For example, a logistic regression could be used to predict whether a network traffic is normal or malicious.
• Create S shaped logistic function. Curve goes to 0 to 1. That means that curve tells the probability that a network traffic is normal or malicious based o its weight.
• If we weighed a very heavy mouse
• If weight is more than 50 percent then traffic is malicious, if less than 50% then it is normal.

Steps in Preparing Model using Logistic Regression

• Importing the libraries
• Importing the dataset
• Splitting the dataset into Training Set and Test Set
• Feature Scaling
• Training the Logistic Regression model on the Training Set
• Predicting a new result
• Predicting the Test set results
• Making the Confusion Matrix
• Visualizing the Training set results
• Visualizing the Test set results

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Dr Pranay Jha

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