applying visualisation and ml to pima dataset from kaggle
First, we describe the dataset and its various attributes, using visualisation tools such as histograms and seaborn plots. Data cleaning is performed.
We then train a Decision Tree classifier, and use ML to predict the 'Outcome' variable. The accuracy on training data is 95% and 72% on test data. This result is illustrated with a confusion matrix and colour map.
