Putting It All Together

In this challenge, you will apply everything you learned throughout the course. Here are the steps you need to take:

Remove the rows that hold too little information;
Encode the y;
Split the dataset into training and test sets;
Build a pipeline with all the preprocessing steps and the GridSearchCV as the final estimator to find the best hyperparameters;
Train the model using the pipeline;
Evaluate the model using the pipeline;
Predict the target for X_new and decode it using the LabelEncoder's .inverse_transform().

Let's get to w̵o̵r̵k̵ code!

Завдання

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Encode the target using LabelEncoder.
Split the data so that 33% is used for a test set and the rest – for a training set.
Make a ColumnTransformer to encode only the 'island' and 'sex' columns. Make the others remain untouched. Use a proper encoder for nominal data.
Fill the gaps in a param_grid to try the following values for the number of neighbors: [1, 3, 5, 7, 9, 12, 15, 20, 25].
Create a GridSearchCV object with the KNeighborsClassifier as a model.
Make a pipeline with ct as a first step and grid_search as a final estimator.
Train the model using a pipeline on the training set.
Evaluate the model on the test set. (Print its score)
Get a predicted target for X_test.
Print the best estimator found by grid_search.

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