Datacamp Supervised Learning with scikit-learn answers
Datacamp Supervised Learning with scikit-learn answers

Datacamp Supervised Learning with scikit-learn answers

Chapter 1: Classification


Which of these is a classification problem?

Once you decide to leverage supervised machine learning to solve a new problem, you need to identify whether your problem is better suited to classification or regression. This exercise will help you develop your intuition for distinguishing between the two. Provided below are 4 example applications of machine learning. Which of them is a supervised classification problem?


Numerical EDA

Get started with your EDA now by exploring this voting records dataset numerically. It has been pre-loaded for you into a DataFrame called df. Use pandas’ .head().info(), and .describe() methods in the IPython Shell to explore the DataFrame, and select the statement below that is not true.


Visual EDA


K-Nearest Neighbors: Fit

Instructions:

  • Import KNeighborsClassifier from sklearn.neighbors.
  • Create arrays X and y for the features and the target variable. Here this has been done for you. Note the use of .drop() to drop the target variable 'party' from the feature array X as well as the use of the .values attribute to ensure X and y are NumPy arrays. Without using .valuesX and y are a DataFrame and Series respectively; the scikit-learn API will accept them in this form also as long as they are of the right shape.
  • Instantiate a KNeighborsClassifier called knn with 6 neighbors by specifying the n_neighbors parameter.
  • Fit the classifier to the data using the .fit() method.
# Import KNeighborsClassifier from sklearn.neighbors
from sklearn.neighbors import KNeighborsClassifier

# Create arrays for the features and the response variable
y = df['party'].values
X = df.drop('party', axis=1).values

# Create a k-NN classifier with 6 neighbors
knn = KNeighborsClassifier(n_neighbors=6)

# Fit the classifier to the data
knn.fit(X,y)


K-Nearest Neighbors: Predict

Instructions:

  • Create arrays for the features and the target variable from df. As a reminder, the target variable is 'party'.
  • Instantiate a KNeighborsClassifier with 6 neighbors.
  • Fit the classifier to the data.
  • Predict the labels of the training data, X.
  • Predict the label of the new data point X_new.
# Import KNeighborsClassifier from sklearn.neighbors
from sklearn.neighbors import KNeighborsClassifier 

# Create arrays for the features and the response variable
y = df['party'].values
X = df.drop('party', axis=1).values

# Create a k-NN classifier with 6 neighbors: knn
knn = KNeighborsClassifier(n_neighbors=6)

# Fit the classifier to the data
knn.fit(X,y)

# Predict the labels for the training data X
y_pred = knn.predict(X)

# Predict and print the label for the new data point X_new
new_prediction = knn.predict(X_new)
print("Prediction: {}".format(new_prediction))


The digits recognition dataset

Instructions:

  • Import datasets from sklearn and matplotlib.pyplot as plt.
  • Load the digits dataset using the .load_digits() method on datasets.
  • Print the keys and DESCR of digits.
  • Print the shape of images and data keys using the . notation.
  • Display the 1011th image using plt.imshow(). This has been done for you, so hit submit to see which handwritten digit this happens to be!
# Import necessary modules
from sklearn import datasets
import matplotlib.pyplot as plt

# Load the digits dataset: digits
digits = datasets.load_digits()

# Print the keys and DESCR of the dataset
print(digits.keys())
print(digits['DESCR'])

# Print the shape of the images and data keys
print(digits.images.shape)
print(digits.data.shape)

# Display digit 1010
plt.imshow(digits.images[1010], cmap=plt.cm.gray_r, interpolation='nearest')
plt.show()


Train/Test Split + Fit/Predict/Accuracy

Instructions:

  • Import KNeighborsClassifier from sklearn.neighbors and train_test_split from sklearn.model_selection.
  • Create an array for the features using digits.data and an array for the target using digits.target.
  • Create stratified training and test sets using 0.2 for the size of the test set. Use a random state of 42. Stratify the split according to the labels so that they are distributed in the training and test sets as they are in the original dataset.
  • Create a k-NN classifier with 7 neighbors and fit it to the training data.
  • Compute and print the accuracy of the classifier’s predictions using the .score() method.
# Import necessary modules
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split

# Create feature and target arrays
X = digits.data
y = digits.target

# Split into training and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)

# Create a k-NN classifier with 7 neighbors: knn
knn = knn = KNeighborsClassifier(n_neighbors = 7)

# Fit the classifier to the training data
knn.fit(X_train, y_train)

# Print the accuracy
print(knn.score(X_test, y_test))


Overfitting and underfitting

Instructions:

  • Inside the for loop:
    • Setup a k-NN classifier with the number of neighbors equal to k.
    • Fit the classifier with k neighbors to the training data.
    • Compute accuracy scores the training set and test set separately using the .score() method and assign the results to the train_accuracy and test_accuracy arrays respectively.
# Setup arrays to store train and test accuracies
neighbors = np.arange(1, 9)
train_accuracy = np.empty(len(neighbors))
test_accuracy = np.empty(len(neighbors))

# Loop over different values of k
for i, k in enumerate(neighbors):
    # Setup a k-NN Classifier with k neighbors: knn
    knn = KNeighborsClassifier(n_neighbors = k)

    # Fit the classifier to the training data
    knn.fit(X_train, y_train)
    
    #Compute accuracy on the training set
    train_accuracy[i] = knn.score(X_train, y_train)

    #Compute accuracy on the testing set
    test_accuracy[i] = knn.score(X_test, y_test)

# Generate plot
plt.title('k-NN: Varying Number of Neighbors')
plt.plot(neighbors, test_accuracy, label = 'Testing Accuracy')
plt.plot(neighbors, train_accuracy, label = 'Training Accuracy')
plt.legend()
plt.xlabel('Number of Neighbors')
plt.ylabel('Accuracy')
plt.show()


Chapter 2: Regression


Which of the following is a regression problem?

Andy introduced regression to you using the Boston housing dataset. But regression models can be used in a variety of contexts to solve a variety of different problems. Given below are four example applications of machine learning. Your job is to pick the one that is best framed as a regression problem.


Importing data for supervised learning

Instructions:

  • Import numpy and pandas as their standard aliases.
  • Read the file 'gapminder.csv' into a DataFrame df using the read_csv() function.
  • Create array X for the 'fertility' feature and array y for the 'life' target variable.
  • Reshape the arrays by using the .reshape() method and passing in -1 and 1.
# Import numpy and pandas
import numpy as np
import pandas as pd

# Read the CSV file into a DataFrame: df
df = pd.read_csv('gapminder.csv')

# Create arrays for features and target variable
y = df['life']
X = df['fertility']

# Print the dimensions of y and X before reshaping
print("Dimensions of y before reshaping: ", y.shape)
print("Dimensions of X before reshaping: ", X.shape)

# Reshape X and y
y_reshaped = y.reshape(-1,1)
X_reshaped = X.reshape(-1,1)

# Print the dimensions of y_reshaped and X_reshaped
print("Dimensions of y after reshaping: ", y_reshaped.shape)
print("Dimensions of X after reshaping: ", X_reshaped.shape)


Exploring the Gapminder data


Fit & predict for regression

Instructions:

  • Import LinearRegression from sklearn.linear_model.
  • Create a LinearRegression regressor called reg.
  • Set up the prediction space to range from the minimum to the maximum of X_fertility. This has been done for you.
  • Fit the regressor to the data (X_fertility and y) and compute its predictions using the .predict() method and the prediction_space array.
  • Compute and print the R2 score using the .score() method.
  • Overlay the plot with your linear regression line. This has been done for you, so hit submit to see the result!

# Import LinearRegression
from sklearn.linear_model import LinearRegression

# Create the regressor: reg
reg = LinearRegression()

# Create the prediction space
prediction_space = np.linspace(min(X_fertility), max(X_fertility)).reshape(-1,1)

# Fit the model to the data
reg.fit(X_fertility,y)

# Compute predictions over the prediction space: y_pred
y_pred = reg.predict(prediction_space)

# Print R^2 
print(reg.score(X_fertility, y))

# Plot regression line
plt.plot(prediction_space, y_pred, color='black', linewidth=3)
plt.show()


Train/test split for regression

Instructions:

  • Import LinearRegression from sklearn.linear_modelmean_squared_error from sklearn.metrics, and train_test_split from sklearn.model_selection.
  • Using X and y, create training and test sets such that 30% is used for testing and 70% for training. Use a random state of 42.
  • Create a linear regression regressor called reg_all, fit it to the training set, and evaluate it on the test set.
  • Compute and print the R2 score using the .score() method on the test set.
  • Compute and print the RMSE. To do this, first compute the Mean Squared Error using the mean_squared_error() function with the arguments y_test and y_pred, and then take its square root using np.sqrt().

# Import necessary modules
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split

# Create training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state=42)

# Create the regressor: reg_all
reg_all = LinearRegression()

# Fit the regressor to the training data
reg_all.fit(X_train,y_train)

# Predict on the test data: y_pred
y_pred = reg_all.predict(X_test)

# Compute and print R^2 and RMSE
err = mean_squared_error(y_test,y_pred)
print("R^2: {}".format(reg_all.score(X_test, y_test)))
rmse = np.sqrt(err)
print("Root Mean Squared Error: {}".format(rmse))


5-fold cross-validation

Instructions:

  • Import LinearRegression from sklearn.linear_model and cross_val_score from sklearn.model_selection.
  • Create a linear regression regressor called reg.
  • Use the cross_val_score() function to perform 5-fold cross-validation on X and y.
  • Compute and print the average cross-validation score. You can use NumPy’s mean() function to compute the average.

# Import the necessary modules
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score

# Create a linear regression object: reg
reg = LinearRegression()

# Compute 5-fold cross-validation scores: cv_scores
cv_scores = cross_val_score(reg, X, y, cv=5)

# Print the 5-fold cross-validation scores
print(cv_scores)

print("Average 5-Fold CV Score: {}".format(cv_scores.mean()))


K-Fold CV comparison

Instructions:

  • Import LinearRegression from sklearn.linear_model and cross_val_score from sklearn.model_selection.
  • Create a linear regression regressor called reg.
  • Perform 3-fold CV and then 10-fold CV. Compare the resulting mean scores.

# Import necessary modules
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score

# Create a linear regression object: reg
reg = LinearRegression()

# Perform 3-fold CV
cvscores_3 = cross_val_score(reg, X, y, cv = 3)
print(np.mean(cvscores_3))

# Perform 10-fold CV
cvscores_10 = cross_val_score(reg, X, y, cv = 10)
print(np.mean(cvscores_10))


Regularization I: Lasso

Instructions:

  • Import Lasso from sklearn.linear_model.
  • Instantiate a Lasso regressor with an alpha of 0.4 and specify normalize=True.
  • Fit the regressor to the data and compute the coefficients using the coef_ attribute.
  • Plot the coefficients on the y-axis and column names on the x-axis. This has been done for you, so hit submit to view the plot!

# Import Lasso
from sklearn.linear_model import Lasso

# Instantiate a lasso regressor: lasso
lasso = Lasso(alpha=0.4, normalize=True)

# Fit the regressor to the data
lasso.fit(X, y)

# Compute and print the coefficients
lasso_coef = lasso.fit(X, y).coef_
print(lasso_coef)

# Plot the coefficients
plt.plot(range(len(df_columns)), lasso_coef)
plt.xticks(range(len(df_columns)), df_columns.values, rotation=60)
plt.margins(0.02)
plt.show()


Regularization II: Ridge

Instructions:

  • Instantiate a Ridge regressor and specify normalize=True.
  • Inside the for loop:
    • Specify the alpha value for the regressor to use.
    • Perform 10-fold cross-validation on the regressor with the specified alpha. The data is available in the arrays X and y.
    • Append the average and the standard deviation of the computed cross-validated scores. NumPy has been pre-imported for you as np.
  • Use the display_plot() function to visualize the scores and standard deviations.

# Import necessary modules
from sklearn.linear_model import Ridge
from sklearn.model_selection import cross_val_score

# Setup the array of alphas and lists to store scores
alpha_space = np.logspace(-4, 0, 50)
ridge_scores = []
ridge_scores_std = []

# Create a ridge regressor: ridge
ridge = Ridge(normalize=True)

# Compute scores over range of alphas
for alpha in alpha_space:

    # Specify the alpha value to use: ridge.alpha
    ridge.alpha = alpha
    
    # Perform 10-fold CV: ridge_cv_scores
    ridge_cv_scores = cross_val_score(ridge, X, y, cv=10)
    
    # Append the mean of ridge_cv_scores to ridge_scores
    ridge_scores.append(np.mean(ridge_cv_scores))
    
    # Append the std of ridge_cv_scores to ridge_scores_std
    ridge_scores_std.append(np.std(ridge_cv_scores))

# Display the plot
display_plot(ridge_scores, ridge_scores_std)


Chapter 3: Fine-tuning your model


Metrics for classification

Instructions:

  • Import classification_report and confusion_matrix from sklearn.metrics.
  • Create training and testing sets with 40% of the data used for testing. Use a random state of 42.
  • Instantiate a k-NN classifier with 6 neighbors, fit it to the training data, and predict the labels of the test set.
  • Compute and print the confusion matrix and classification report using the confusion_matrix() and classification_report() functions.

# Import necessary modules
from sklearn.metrics import classification_report, confusion_matrix

# Create training and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state=42)

# Instantiate a k-NN classifier: knn
knn = KNeighborsClassifier(n_neighbors=6)

# Fit the classifier to the training data
knn.fit(X_train,y_train)

# Predict the labels of the test data: y_pred
y_pred = knn.predict(X_test)

# Generate the confusion matrix and classification report
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))


Building a logistic regression model

Instructions:

  • Import:
    • LogisticRegression from sklearn.linear_model.
    • confusion_matrix and classification_report from sklearn.metrics.
  • Create training and test sets with 40% (or 0.4) of the data used for testing. Use a random state of 42. This has been done for you.
  • Instantiate a LogisticRegression classifier called logreg.
  • Fit the classifier to the training data and predict the labels of the test set.
  • Compute and print the confusion matrix and classification report. This has been done for you, so hit submit to see how logistic regression compares to k-NN!

# Import the necessary modules
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report

# Create training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state=42)

# Create the classifier: logreg
logreg = LogisticRegression()

# Fit the classifier to the training data
logreg.fit(X_train,y_train)

# Predict the labels of the test set: y_pred
y_pred = logreg.predict(X_test)

# Compute and print the confusion matrix and classification report
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))


Plotting an ROC curve

Instructions:

  • Import roc_curve from sklearn.metrics.
  • Using the logreg classifier, which has been fit to the training data, compute the predicted probabilities of the labels of the test set X_test. Save the result as y_pred_prob.
  • Use the roc_curve() function with y_test and y_pred_prob and unpack the result into the variables fprtpr, and thresholds.
  • Plot the ROC curve with fpr on the x-axis and tpr on the y-axis.

# Import necessary modules
from sklearn.metrics import roc_curve

# Compute predicted probabilities: y_pred_prob
y_pred_prob = logreg.predict_proba(X_test)[:,1]

# Generate ROC curve values: fpr, tpr, thresholds
fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)

# Plot ROC curve
plt.plot([0, 1], [0, 1], 'k--')
plt.plot(fpr, tpr)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.show()


Precision-recall Curve


AUC computation

Instructions:

  • Import roc_auc_score from sklearn.metrics and cross_val_score from sklearn.model_selection.
  • Using the logreg classifier, which has been fit to the training data, compute the predicted probabilities of the labels of the test set X_test. Save the result as y_pred_prob.
  • Compute the AUC score using the roc_auc_score() function, the test set labels y_test, and the predicted probabilities y_pred_prob.
  • Compute the AUC scores by performing 5-fold cross-validation. Use the cross_val_score() function and specify the scoring parameter to be 'roc_auc'.

# Import necessary modules
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import cross_val_score

# Compute predicted probabilities: y_pred_prob
y_pred_prob = logreg.predict_proba(X_test)[:,1]

# Compute and print AUC score
print("AUC: {}".format(roc_auc_score(y_test, y_pred_prob)))

# Compute cross-validated AUC scores: cv_auc
cv_auc = cross_val_score(logreg, X, y, cv=5, scoring='roc_auc')

# Print list of AUC scores
print("AUC scores computed using 5-fold cross-validation: {}".format(cv_auc))


Hyperparameter tuning with GridSearchCV

Instructions:

  • Import LogisticRegression from sklearn.linear_model and GridSearchCV from sklearn.model_selection.
  • Setup the hyperparameter grid by using c_space as the grid of values to tune C over.
  • Instantiate a logistic regression classifier called logreg.
  • Use GridSearchCV with 5-fold cross-validation to tune C:
    • Inside GridSearchCV(), specify the classifier, parameter grid, and number of folds to use.
    • Use the .fit() method on the GridSearchCV object to fit it to the data X and y.
  • Print the best parameter and best score obtained from GridSearchCV by accessing the best_params_ and best_score_ attributes of logreg_cv.

# Import necessary modules
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV

# Setup the hyperparameter grid
c_space = np.logspace(-5, 8, 15)
param_grid = {'C': c_space}

# Instantiate a logistic regression classifier: logreg
logreg = LogisticRegression(solver="liblinear")

# Instantiate the GridSearchCV object: logreg_cv
logreg_cv = GridSearchCV(logreg, param_grid, cv=5)

# Fit it to the data
logreg_cv.fit(X,y)

# Print the tuned parameters and score
print("Tuned Logistic Regression Parameters: {}".format(logreg_cv.best_params_)) 
print("Best score is {}".format(logreg_cv.best_score_))


Hyperparameter tuning with RandomizedSearchCV

Instructions:

  • Import DecisionTreeClassifier from sklearn.tree and RandomizedSearchCV from sklearn.model_selection.
  • Specify the parameters and distributions to sample from. This has been done for you.
  • Instantiate a DecisionTreeClassifier.
  • Use RandomizedSearchCV with 5-fold cross-validation to tune the hyperparameters:
    • Inside RandomizedSearchCV(), specify the classifier, parameter distribution, and number of folds to use.
    • Use the .fit() method on the RandomizedSearchCV object to fit it to the data X and y.
  • Print the best parameter and best score obtained from RandomizedSearchCV by accessing the best_params_ and best_score_ attributes of tree_cv.

# Import necessary modules
from scipy.stats import randint
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import RandomizedSearchCV


# Setup the parameters and distributions to sample from: param_dist
param_dist = {"max_depth": [3, None],
              "max_features": randint(1, 9),
              "min_samples_leaf": randint(1, 9),
              "criterion": ["gini", "entropy"]}

# Instantiate a Decision Tree classifier: tree
tree = DecisionTreeClassifier()

# Instantiate the RandomizedSearchCV object: tree_cv
tree_cv = RandomizedSearchCV(tree, param_dist, cv=5)

# Fit it to the data
tree_cv.fit(X, y)

# Print the tuned parameters and score
print("Tuned Decision Tree Parameters: {}".format(tree_cv.best_params_))
print("Best score is {}".format(tree_cv.best_score_))


Hold-out set reasoning


Hold-out set in practice I: Classification

Instructions:

  • Create the hyperparameter grid:
    • Use the array c_space as the grid of values for 'C'.
    • For 'penalty', specify a list consisting of 'l1' and 'l2'.
  • Instantiate a logistic regression classifier.
  • Create training and test sets. Use a test_size of 0.4 and random_state of 42. In practice, the test set here will function as the hold-out set.
  • Tune the hyperparameters on the training set using GridSearchCV with 5-folds. This involves first instantiating the GridSearchCV object with the correct parameters and then fitting it to the training data.
  • Print the best parameter and best score obtained from GridSearchCV by accessing the best_params_ and best_score_ attributes of logreg_cv.

# Import necessary modules
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV

# Create the hyperparameter grid
c_space = np.logspace(-5, 8, 15)
param_grid = {'C': c_space, 'penalty': ['l1', 'l2']}

# Instantiate the logistic regression classifier: logreg
logreg = LogisticRegression(solver='liblinear')

# Create train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)

# Instantiate the GridSearchCV object: logreg_cv
logreg_cv = GridSearchCV(logreg, param_grid, cv=5)

# Fit it to the training data
logreg_cv.fit(X_train, y_train)

# Print the optimal parameters and best score
print("Tuned Logistic Regression Parameter: {}".format(logreg_cv.best_params_))
print("Tuned Logistic Regression Accuracy: {}".format(logreg_cv.best_score_))


Hold-out set in practice II: Regression

Instructions:

  • Import the following modules:
    • ElasticNet from sklearn.linear_model.
    • mean_squared_error from sklearn.metrics.
    • GridSearchCV and train_test_split from sklearn.model_selection.
  • Create training and test sets, with 40% of the data used for the test set. Use a random state of 42.
  • Specify the hyperparameter grid for 'l1_ratio' using l1_space as the grid of values to search over.
  • Instantiate the ElasticNet regressor.
  • Use GridSearchCV with 5-fold cross-validation to tune 'l1_ratio' on the training data X_train and y_train. This involves first instantiating the GridSearchCV object with the correct parameters and then fitting it to the training data.
  • Predict on the test set and compute the R2 and mean squared error.

# Import necessary modules
from sklearn.linear_model import ElasticNet 
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV, train_test_split

# Create train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)

# Create the hyperparameter grid
l1_space = np.linspace(0, 1, 30)
param_grid = {'l1_ratio': l1_space}

# Instantiate the ElasticNet regressor: elastic_net
elastic_net = ElasticNet()

# Setup the GridSearchCV object: gm_cv
gm_cv = GridSearchCV(elastic_net, param_grid, cv=5)

# Fit it to the training data
gm_cv.fit(X_train, y_train)

# Predict on the test set and compute metrics
y_pred = gm_cv.predict(X_test)
r2 = gm_cv.score(X_test, y_test)
mse = mean_squared_error(y_test, y_pred)
print("Tuned ElasticNet l1 ratio: {}".format(gm_cv.best_params_))
print("Tuned ElasticNet R squared: {}".format(r2))
print("Tuned ElasticNet MSE: {}".format(mse))


Chapter 4: Preprocessing and pipelines


Exploring categorical features

Instructions:

  • Import pandas as pd.
  • Read the CSV file 'gapminder.csv' into a DataFrame called df.
  • Use pandas to create a boxplot showing the variation of life expectancy ('life') by region ('Region'). To do so, pass the column names in to df.boxplot() (in that order).

# Import pandas
import pandas as pd

# Read 'gapminder.csv' into a DataFrame: df
df = pd.read_csv('gapminder.csv')

# Create a boxplot of life expectancy per region
df.boxplot('life', 'Region', rot=60)

# Show the plot
plt.show()


Creating dummy variables

Instructions:

  • Use the pandas get_dummies() function to create dummy variables from the df DataFrame. Store the result as df_region.
  • Print the columns of df_region. This has been done for you.
  • Use the get_dummies() function again, this time specifying drop_first=True to drop the unneeded dummy variable (in this case, 'Region_America').
  • Hit submit to print the new columns of df_region and take note of how one column was dropped!

# Create dummy variables: df_region
df_region = pd.get_dummies(df)

# Print the columns of df_region
print(df_region.columns)

# Create dummy variables with drop_first=True: df_region
df_region = pd.get_dummies(df, drop_first=True)

# Print the new columns of df_region
print(df_region.columns)


Regression with categorical features

Instructions:

  • Import Ridge from sklearn.linear_model and cross_val_score from sklearn.model_selection.
  • Instantiate a ridge regressor called ridge with alpha=0.5 and normalize=True.
  • Perform 5-fold cross-validation on X and y using the cross_val_score() function.
  • Print the cross-validated scores.

# Import necessary modules
from sklearn.linear_model import Ridge
from sklearn.model_selection import cross_val_score

# Instantiate a ridge regressor: ridge
ridge = Ridge(alpha=0.5, normalize=True)

# Perform 5-fold cross-validation: ridge_cv
ridge_cv = cross_val_score(ridge, X, y, cv=5)

# Print the cross-validated scores
print(ridge_cv)


Dropping missing data

Instructions:

  • Explore the DataFrame df in the IPython Shell. Notice how the missing value is represented.
  • Convert all '?' data points to np.nan.
  • Count the total number of NaNs using the .isnull() and .sum() methods. This has been done for you.
  • Drop the rows with missing values from df using .dropna().
  • Hit submit to see how many rows were lost by dropping the missing values.

# Convert '?' to NaN
df[df == '?'] = np.nan

# Print the number of NaNs
print(df.isnull().sum())

# Print shape of original DataFrame
print("Shape of Original DataFrame: {}".format(df.shape))

# Drop missing values and print shape of new DataFrame
df = df.dropna()

# Print shape of new DataFrame
print("Shape of DataFrame After Dropping All Rows with Missing Values {}".format(df.shape))


Imputing missing data in a ML Pipeline I

Instructions:

  • Import Imputer from sklearn.preprocessing and SVC from sklearn.svm. SVC stands for Support Vector Classification, which is a type of SVM.
  • Setup the Imputation transformer to impute missing data (represented as 'NaN') with the 'most_frequent' value in the column (axis=0).
  • Instantiate a SVC classifier. Store the result in clf.
  • Create the steps of the pipeline by creating a list of tuples:
    • The first tuple should consist of the imputation step, using imp.
    • The second should consist of the classifier.

# Import the Imputer module
from sklearn.preprocessing import Imputer
from sklearn.svm import SVC

# Setup the Imputation transformer: imp
imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=0)

# Instantiate the SVC classifier: clf
clf = SVC()

# Setup the pipeline with the required steps: steps
steps = [('imputation', imp), ('SVM', clf)]


Imputing missing data in a ML Pipeline II

Instructions:

  • Import the following modules:
  • Create the pipeline using Pipeline() and steps.
  • Create training and test sets. Use 30% of the data for testing and a random state of 42.
  • Fit the pipeline to the training set and predict the labels of the test set.
  • Compute the classification report.

# Import necessary modules
from sklearn.preprocessing import Imputer
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC

# Setup the pipeline steps: steps
steps = [('imputation', Imputer(missing_values='NaN', strategy='most_frequent', axis=0)),
        ('SVM', SVC())]

# Create the pipeline: pipeline
pipeline = Pipeline(steps)

# Create training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Fit the pipeline to the train set
pipeline.fit(X_train, y_train)

# Predict the labels of the test set
y_pred = pipeline.predict(X_test)

# Compute metrics
print(classification_report(y_test, y_pred))


Centering and scaling your data

Instructions:

  • Import scale from sklearn.preprocessing.
  • Scale the features X using scale().
  • Print the mean and standard deviation of the unscaled features X, and then the scaled features X_scaled. Use the numpy functions np.mean() and np.std() to compute the mean and standard deviations.

# Import scale
from sklearn.preprocessing import scale

# Scale the features: X_scaled
X_scaled = scale(X)

# Print the mean and standard deviation of the unscaled features
print("Mean of Unscaled Features: {}".format(np.mean(X))) 
print("Standard Deviation of Unscaled Features: {}".format(np.std(X)))

# Print the mean and standard deviation of the scaled features
print("Mean of Scaled Features: {}".format(np.mean(X_scaled))) 
print("Standard Deviation of Scaled Features: {}".format(np.std(X_scaled)))


Centering and scaling in a pipeline

Instructions:

  • Import the following modules:
    • StandardScaler from sklearn.preprocessing.
    • Pipeline from sklearn.pipeline.
  • Complete the steps of the pipeline with StandardScaler() for 'scaler' and KNeighborsClassifier() for 'knn'.
  • Create the pipeline using Pipeline() and steps.
  • Create training and test sets, with 30% used for testing. Use a random state of 42.
  • Fit the pipeline to the training set.
  • Compute the accuracy scores of the scaled and unscaled models by using the .score() method inside the provided print() functions.

# Import the necessary modules
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

# Setup the pipeline steps: steps
steps = [('scaler', StandardScaler()),
        ('knn', KNeighborsClassifier())]
        
# Create the pipeline: pipeline
pipeline = Pipeline(steps)

# Create train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Fit the pipeline to the training set: knn_scaled
knn_scaled = pipeline.fit(X_train, y_train)

# Instantiate and fit a k-NN classifier to the unscaled data
knn_unscaled = KNeighborsClassifier().fit(X_train, y_train)

# Compute and print metrics
print('Accuracy with Scaling: {}'.format(knn_scaled.score(X_test, y_test)))
print('Accuracy without Scaling: {}'.format(knn_unscaled.score(X_test, y_test)))


Bringing it all together I: Pipeline for classification

Instructions:

  • Setup the pipeline with the following steps:
    • Scaling, called 'scaler' with StandardScaler().
    • Classification, called 'SVM' with SVC().
  • Specify the hyperparameter space using the following notation: 'step_name__parameter_name'. Here, the step_name is SVM, and the parameter_names are C and gamma.
  • Create training and test sets, with 20% of the data used for the test set. Use a random state of 21.
  • Instantiate GridSearchCV with the pipeline and hyperparameter space and fit it to the training set. Use 3-fold cross-validation (This is the default, so you don’t have to specify it).
  • Predict the labels of the test set and compute the metrics. The metrics have been computed for you.

# Setup the pipeline
steps = [('scaler', StandardScaler()),
         ('SVM', SVC())]

pipeline = Pipeline(steps)

# Specify the hyperparameter space
parameters = {'SVM__C':[1, 10, 100],
              'SVM__gamma':[0.1, 0.01]}

# Create train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=21)

# Instantiate the GridSearchCV object: cv
cv = GridSearchCV(pipeline, parameters, cv=3)

# Fit to the training set
cv.fit(X_train, y_train)

# Predict the labels of the test set: y_pred
y_pred = cv.predict(X_test)

# Compute and print metrics
print("Accuracy: {}".format(cv.score(X_test, y_test)))
print(classification_report(y_test, y_pred))
print("Tuned Model Parameters: {}".format(cv.best_params_))


Bringing it all together II: Pipeline for regression

Instructions:

  • Set up a pipeline with the following steps:
    • 'imputation', which uses the Imputer() transformer and the 'mean' strategy to impute missing data ('NaN') using the mean of the column.
    • 'scaler', which scales the features using StandardScaler().
    • 'elasticnet', which instantiates an ElasticNet() regressor.
  • Specify the hyperparameter space for the l1 ratio using the following notation: 'step_name__parameter_name'. Here, the step_name is elasticnet, and the parameter_name is l1_ratio.
  • Create training and test sets, with 40% of the data used for the test set. Use a random state of 42.
  • Instantiate GridSearchCV with the pipeline and hyperparameter space. Use 3-fold cross-validation (This is the default, so you don’t have to specify it).
  • Fit the GridSearchCV object to the training set.
  • Compute R2 and the best parameters. This has been done for you, so hit submit to see the results!

# Setup the pipeline steps: steps
steps = [('imputation', Imputer(missing_values='NaN', strategy='mean')),
         ('scaler', StandardScaler()),
         ('elasticnet', ElasticNet())]

# Create the pipeline: pipeline 
pipeline = Pipeline(steps)

# Specify the hyperparameter space
parameters = {'elasticnet__l1_ratio':np.linspace(0,1,30)}

# Create train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)

# Create the GridSearchCV object: gm_cv
gm_cv = GridSearchCV(pipeline, parameters, cv=3)

# Fit to the training set
gm_cv.fit(X_train, y_train)

# Compute and print the metrics
r2 = gm_cv.score(X_test, y_test)
print("Tuned ElasticNet Alpha: {}".format(gm_cv.best_params_))
print("Tuned ElasticNet R squared: {}".format(r2))


If you want to learn, what is linear regression? – Click here.

 If you want to learn, how to implement Simple Linear Regression with Python built-in functions – Click here.

 If you want to learn, own implementation of Simple Linear Regression – Click here

Thank you for reading this blog. If you have any query about this #Datacamp course "Supervised Learning with scikit-learn", feel free to ask by comment. Thank you again.

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