Machine Learning and Python Code

Question

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.

    %matplotlib inline
    import sqlite3
    import pandas as pd
    import numpy as np
    import nltk
    import string
    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("""
    SELECT *
    FROM Reviews
    WHERE Score != 3
    """, con) 




    # Give reviews with Score>3 a positive rating, and reviews with a 
    score<3 a negative rating.
    def partition(x):
    if x < 3:
        return 'negative'
    return 'positive'

    #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.

Answer 1

You can create an array called actualScore using the column Score from filtered_data

actualScore = filtered_data['Score']

Then create an array positiveNegative coding negative for values less than 3 and positive for values greater than 3.

positiveNegative = actualScore.map(partition)

Then you can overwrite the old column score with the new coded values

filtered_data['Score'] = positiveNegative

Hope this helps!

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Thank you!