Update README.md

Author: tenosel

README.md

@@ -10,6 +10,7 @@ To simplify this task, we will again use the popular python library for machine
 1. Navigate to the `__main__` function of `src/ex1_linear_svm.py` and load the iris dataset from `sklearn.datasets`.
 2. Get access to the data, the labels and the class names. In the lecture, you saw how an SVM can be used for the binary classification problem. Find out how `sklearn` implements multi-class classification.
 3. Split the dataset into training and test data using ``sklearn.model_selection.train_test_split``. The test set should contain 25% of the data. Use `random_state=29` in the split function to generate reproducible results.
+   
 4.1. Implement a function `train_test_svc` to create a Linear SVM classifier from the ``sklearn.svm.LinearSVC`` class and fit it to the training data.
 
 4.2. In the same function, test the classifier on the test set and evaluate its performance by computing the accuracy. ``sklearn.metrics`` provides functions to evaluate this metric. Express the accuracy as a percentage and round to one decimal place. Your function `train_test_svc` should return the classifier and the accuracy value.