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wordcloud = WordCloud(background_color='white', mode = "RGB", width = 2000, height=1000).generate(str(postings['name'])) | |
plt.title("Craigslist Used Items Word Cloud") | |
plt.imshow(wordcloud) | |
plt.axis("off") | |
plt.show(); |
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# Tree-based estimators can be used to compute feature importances, which in turn can be used to discard irrelevant features. | |
clf = RandomForestClassifier(n_estimators=50, max_features='sqrt') | |
clf = clf.fit(train, targets) | |
# Let's have a look at the importance of each feature. | |
features = pd.DataFrame() | |
features['feature'] = train.columns | |
features['importance'] = clf.feature_importances_ | |
# Sorting values by feature importance. |
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logreg = LogisticRegression() | |
logreg_cv = LogisticRegressionCV() | |
rf = RandomForestClassifier() | |
gboost = GradientBoostingClassifier() | |
svm = SVC(probability=True) | |
knn = KNeighborsClassifier() | |
dt = DecisionTreeClassifier() | |
models = [logreg, logreg_cv, rf, gboost, svm, knn, dt] |
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# train the model on the training set | |
gboost.fit(X_train, y_train) | |
# make class predictions for the testing set | |
y_pred_class = gboost.predict(X_test) | |
# IMPORTANT: first argument is true values, second argument is predicted values | |
print(metrics.confusion_matrix(y_test, y_pred_class)) | |
binary = np.array([[125, 14], |
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