Kiwibp

# 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],

Kiwibp

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]

Kiwibp

# 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.

Kiwibp

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();

Kiwibp

#Removing all locations with 2 or less items.
counts = non_mv.location.value_counts()
loc_gt2 = counts[counts > 2]

popular_locations = non_mv[non_mv.location.isin(loc_gt2.keys())]

plt.figure(figsize=(10,5))
sns.violinplot(x="location", y="price", data=popular_locations, scale="width", inner="stick")
plt.show();

Kiwibp

# vehicles are skewing boxplot too much; all rows at or above 1.8k appear to be motor vehicles. 
motor_vehicles = postings.loc[postings.price >= 1800.0, :]

motor_vehicles.plot.bar('name', 'price', figsize=(9,9))
plt.ylabel("Price")
plt.xlabel("Vehicle")
plt.show();

Kiwibp

import scrapy


class CraigslistWebscrapingItem(scrapy.Item):
    name = scrapy.Field()
    price = scrapy.Field()
    location = scrapy.Field()
    date = scrapy.Field()
    

Kiwibp

#execute Summary Extractor model 
ml = MonkeyLearn('insert api key here')
data = list(nlp_df_sample.iloc[:,7])
model_id = 'ex_94WD2XxD'
summary_model_results = ml.extractors.extract(model_id, data, production_model=True)
print(summary_model_results.body)

#execute Price Extractor model 
data = list(nlp_df_sample.iloc[:,7])
model_id = 'ex_wNDME4vE'

Kiwibp

locations_ten_or_more = all_items_df.groupby(['Location']).filter(lambda g: g.Location.value_counts() >= 10) \
.loc[:,['Location','Description', 'Price', 'Title', 'Url']]

#checking the number of locations with less than 10 items
len_of_locs = len(locations_ten_or_more.groupby("Location").size())
print(f'There are {len_of_locs} cities with 10 items or more.')
print('\n')

#checking the locations with the most items in this subset
print('Locations with the most amount of items in this subset:')

Kiwibp

#facebook marketplace 
from selenium import webdriver
from time import sleep
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC
from pymongo import MongoClient

class App: