Challenge: Putting It All Together

In this challenge, you will apply everything you learned throughout the course from data preprocessing to training and evaluating the model.

Compito

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Encode the target.
Split the data so that 33% is used for the test set and the remainder for the training set.
Make a ColumnTransformer to encode only the 'island' and 'sex' columns. Make sure the others columns 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.
Construct a pipeline that begins with ct as the first step, followed by imputation using the most frequent value, standardization, and concludes with GridSearchCV as the 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.

Soluzione

import pandas as pd

from sklearn.impute import SimpleImputer

from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler

from sklearn.compose import make_column_transformer

from sklearn.pipeline import make_pipeline

from sklearn.neighbors import KNeighborsClassifier

from sklearn.model_selection import GridSearchCV, train_test_split

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins.csv')

# Removing rows with more than 1 null

df = df[df.isna().sum(axis=1) < 2]

# Assigining X, y variables

X, y = df.drop('species', axis=1), df['species']

# Encode the target

label_enc = LabelEncoder()

y = label_enc.fit_transform(y)

# Make a train-test split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

# Create the ColumnTransformer for encoding features

ct = make_column_transformer((OneHotEncoder(), ['island', 'sex']), remainder='passthrough')

# Make a param_grid for the grid search and initialize the GridSearchCV object

param_grid = {'n_neighbors': [1, 3, 5, 7, 9, 12, 15, 20, 25],

'weights': ['distance', 'uniform'],

'p': [1, 2]

}

grid_search = GridSearchCV(KNeighborsClassifier(), param_grid)

# Make a Pipeline of ct, SimpleImputer, StandardScaler, and GridSearchCV

pipe = make_pipeline(ct, SimpleImputer(strategy='most_frequent'), StandardScaler(), grid_search)

# Train the model

pipe.fit(X_train, y_train)

# Print score

print(pipe.score(X_test, y_test))

# Print predictions

y_pred = pipe.predict(X_test) # Get encoded predictions

print(label_enc.inverse_transform(y_pred[:5])) # Decode predictions and print 5 first

# Print the best estimator

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Sezione 4. Capitolo 10

import pandas as pd

from sklearn.impute import SimpleImputer

from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler

from sklearn.compose import make_column_transformer

from sklearn.pipeline import make_pipeline

from sklearn.neighbors import KNeighborsClassifier

from sklearn.model_selection import GridSearchCV, train_test_split

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins.csv')

# Removing rows with more than 1 null

df = df[df.isna().sum(axis=1) < 2]

# Assigining X, y variables

X, y = df.drop('species', axis=1), df['species']

# Encode the target

label_enc = LabelEncoder()

y = ___

# Make a train-test split

X_train, X_test, y_train, y_test = ___(X, y, ___=0.33)

# Create the ColumnTransformer for encoding features

ct = ___((___(), ___, ___=___)

# Make a param_grid for the grid search and initialize the GridSearchCV object

param_grid = {'n_neighbors': ___,

'weights': ['distance', 'uniform'],

'p': [1, 2]

}

grid_search = ___

# Make a Pipeline of ct, SimpleImputer, and StandardScaler

pipe = ___

# Train the model

___

# Print score

print(pipe.___)

# Print predictions

y_pred = pipe.___ # Get encoded predictions

print(label_enc.inverse_transform(y_pred[:5])) # Decode predictions and print 5 first

# Print the best estimator

print(grid_search.___)

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