Commit b6b5a9ad authored by Florent Chatelain's avatar Florent Chatelain
Browse files

fix typos

parent a1415ee1
......@@ -10,12 +10,11 @@
 
It is a well-understood dataset. All of the variables are continuous and generally in the range of 0 to 1. As such we will not have to normalize the input data, which is often a good practice with the Perceptron algorithm. The output variable is a string “M” for mine and “R” for rock, which will need to be converted to integers 1 and 0.
 
By predicting the class with the most observations in the dataset (M or mines) the Zero Rule Algorithm can achieve an accuracy of 53%.
 
You can learn more about this dataset at the
[UCI Machine Learning repository][UCI Machine Learning repository documentation](http://archive.ics.uci.edu/ml/datasets/connectionist+bench+(sonar,+mines+vs.+rocks)). You can download the dataset for free and place it in your working directory with the filename `sonar.all-data.csv` (also avaible in the gitlab repo of the course).
You can learn more about this dataset at the [UCI Machine Learning repository documentation](http://archive.ics.uci.edu/ml/datasets/connectionist+bench+(sonar,+mines+vs.+rocks)). You can download the dataset for free and place it in your working directory with the filename `sonar.all-data.csv` (also avaible in the gitlab repo of the course).
 
%% Cell type:code id: tags:
 
``` python
from csv import reader
......
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