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 practice 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.
Click on Start/Continue Hackathon to go to the Practice page.