• No products in the cart.

Logistic Regression

The Logistic Regression or Logistic Model also called the Logit Model is a classification algorithm that predicts a categorical feature based on a set of independent variables. Logistic Regression is one of the simplest classification algorithms that can be used to predict values for a categorical dependent variable.

For a deeper understanding of Logistic Regression, use the following resources:

In this practise session, we will learn to code Logistic Regression.We will perform the following steps to build a simple classifier using the popular Iris dataset. You can find the dataset here.

Step 1. Data Preprocessing 

  • Importing the libraries.
  • Importing dataset (Dataset Link https://archive.ics.uci.edu/ml/datasets/iris).
  • Dealing with the categorical variable.
  • Classifying dependent and independent variables.
  • Splitting the data into a training set and test set.
  • Feature scaling.

Step 2. Logistic Regression 

  • Create a Logistic classifier.
  • Feed the training data to the classifier.
  • Predicting the species for the test set.
  • Using the confusion matrix to find accuracy.

Hackathon Reviews


  • 5 stars0
  • 4 stars0
  • 3 stars0
  • 2 stars0
  • 1 stars0

No Reviews found for this hackathon.


© Analytics India Magazine