Logistic-Regression-Python

Logistic Regression is a Machine Learning classification algorithm that is used to predict the probability of a categorical dependent variable. In logistic regression, the dependent variable is a binary variable that contains data coded as 1 (yes, success, etc.) or 0 (no, failure, etc.). In other words, the logistic regression model predicts P(Y=1) as a function of X.

Logistic Regression Assumptions

• Binary logistic regression requires the dependent variable to be binary.
• For a binary regression, the factor level 1 of the dependent variable should represent the desired outcome.
• Only the meaningful variables should be included.
• The independent variables should be independent of each other. That is, the model should have little or no multicollinearity.
• The independent variables are linearly related to the log odds.
• Logistic regression requires quite large sample sizes.

Data:

• iris dataset,This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.

  Predicted attribute: class of iris plant.

  Attribute Information:

  1. sepal length in cm
  2. sepal width in cm
  3. petal length in cm
  4. petal width in cm
  5. class: -- Iris Setosa -- Iris Versicolour -- Iris Virginica

Let's build logistic regression model and predict the class of iris plant.