Please use jupyter notebook application

departmen status agesalary count sales sales senior 31..3546k..50k 30 junior 26.3026k..30k40 sales junior 31.3531k..35k40 systemsjunior 21..2546k..50k20 systemssenio31..3566k..70k5 systemsjunior 26..3046k..50k3 systems senior 41.4566k..70k3 marketing senior36.40 46k..50k 10 marketing junior 31..3541k.45k4 secretary senior 46..50 36k..40k 4 secretary junior 26.3026k..30k6 The data is a summary of the original data table. For example, the first row indicates that 30 employees in the sales department has an age between 31 and 35 inclusive and a salary between 46K and 50K inclusive. The attribute status is the class label 4. [25] Write Python code to use sklearn.naive_bayes to learn a Guassian Naive Bayes classifier using df3 as the training data, and use the learned predictive model to predict the status of a user provided unseen data, for example, t department: systems, status:?,age: 28, salary: 50K> Again, you need to encode the department. departmen status agesalary count sales sales senior 31..3546k..50k 30 junior 26.3026k..30k40 sales junior 31.3531k..35k40 systemsjunior 21..2546k..50k20 systemssenio31..3566k..70k5 systemsjunior 26..3046k..50k3 systems senior 41.4566k..70k3 marketing senior36.40 46k..50k 10 marketing junior 31..3541k.45k4 secretary senior 46..50 36k..40k 4 secretary junior 26.3026k..30k6 The data is a summary of the original data table. For example, the first row indicates that 30 employees in the sales department has an age between 31 and 35 inclusive and a salary between 46K and 50K inclusive. The attribute status is the class label 4. [25] Write Python code to use sklearn.naive_bayes to learn a Guassian Naive Bayes classifier using df3 as the training data, and use the learned predictive model to predict the status of a user provided unseen data, for example, t department: systems, status:?,age: 28, salary: 50K> Again, you need to encode the department