Why is my accuracy score so poor? The database is from a JMU Sports forum. I put in pymongo. I dont understand why my accuracy is so low. I basically extracted it put it in a list. Made a new list where it takes the top 80 most prolific authors.

from pymongo import MongoClient db = MongoClient().db_name.collection_name users = [] for user in db.find(): users.append(user) print(users[1]) {'_id': 14069840, 'page': 1, 'post_id': 1465, 'post': 'I laughed too hard. Whoops.', 'username': 'Potomac', 'timestamp': '2017-02'} from collections import Counter pr = Counter([review['username'] for review in users]).most_common(80) pr[:1] [('BleedingPurple', 7619)] 

keep_ids = {pr[0] : 0 for pr in prolific_reviewers}

keep_reviews = []

for review in reviews:

uid = review['user_id']

if uid in keep_ids and keep_ids[uid] < 500:

keep_reviews.append(review)

keep_ids[uid] += 1

 authors = [review['username'] for review in keep_reviews] text = [review['post'] for review in keep_reviews] from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC from sklearn.model_selection import train_test_split vectorizer = TfidfVectorizer() vectors = vectorizer.fit_transform(texts) print(vectors.shape) (146816, 68140) X_train, X_test, y_train, y_test = train_test_split(vectors, authors, test_size=0.2, random_state=1337) print(X_train.shape, X_test.shape) (117452, 68140) (29364, 68140) svm = LinearSVC() svm.fit(X_train, y_train) predictions = svm.predict(X_test) from sklearn.metrics import accuracy_score print(accuracy_score(y_test, predictions)) 0.18