So, I recently started with Machine Learning and coding in Python. I've been trying to figure out the partition method used in the Amazon fine food review data from kaggle and its code. What i also can't understand, is the purpose of the last 3 lines of code.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
from nltk.stem.porter import PorterStemmer
# using the SQLite Table to read data.
con = sqlite3.connect('./amazon-fine-food-reviews/database.sqlite')
#filtering only positive and negative reviews i.e.
# not taking into consideration those reviews with Score=3
filtered_data = pd.read_sql_query("""
WHERE Score != 3
# Give reviews with Score>3 a positive rating, and reviews with a
score<3 a negative rating.
if x < 3:
#changing reviews with score less than 3 to be positive vice-versa
actualScore = filtered_data['Score']
positiveNegative = actualScore.map(partition)
filtered_data['Score'] = positiveNegative
Any help would be greatly appreciated. Thanks.