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eslopfer
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README.md
Exercise 0: Environment and libraries
The exercise is validated if all questions of the exercise are validated
Activate the virtual environment. If you used conda run conda activate your_env.
Run python --version.
Does it print Python 3.x? x >= 8
Do import jupyter, import pandas, import nltk and import sklearn run without any error?
Exercise 1: Lower case
The exercise is validated if all questions of the exercise are validated
For question 1, is the output the following?
0 this is my first nlp exercise
1 wtf!!!!!
Name: text, dtype: object
For question 2, is the output the following?
0 THIS IS MY FIRST NLP EXERCISE
1 WTF!!!!!
Name: text, dtype: object
Exercise 2: Punctuation
For question 1, is validated if the ouptut doesn't contain punctuation !"#$%&'()*+,-./:;<=>?@[]^_`{|}~. Is the previous statement true? Do not take into account the spaces in the output. The output should be as:
Remove this from the sentence
Exercise 3: Tokenization
The exercise is validated if all questions of the exercise are validated
For question 1, is output the following?
['Bitcoin is a cryptocurrency invented in 2008 by an unknown person or group of people using the name Satoshi Nakamoto.',
'The currency began use in 2009 when its implementation was released as open-source software.']
For question 2, is the output the following?
['Bitcoin',
'is',
'a',
'cryptocurrency',
'invented',
'in',
'2008',
'by',
'an',
'unknown',
'person',
'or',
'group',
'of',
'people',
'using',
'the',
'name',
'Satoshi',
'Nakamoto',
'.',
'The',
'currency',
'began',
'use',
'in',
'2009',
'when',
'its',
'implementation',
'was',
'released',
'as',
'open-source',
'software',
'.']
Exercise 4: Stop words
For question 1, is the output the following? (using NLTK)
['The', 'goal', 'exercise', 'learn', 'remove', 'stop', 'words', 'NLTK', '.', 'Stop', 'words', 'usually', 'refers', 'common', 'words', 'language', '.']
Exercise 5: Stemming
For question 1, is the output the following? (using NLTK)
['the', 'interview', 'interview', 'the', 'presid', 'in', 'an', 'interview']
Exercise 6: Text preprocessing
For question 1, is the output the following?
['01',
'edu',
'system',
'present',
'innov',
'curriculum',
'softwar',
'engin',
'program',
'renown',
'industrylead',
'reput',
'curriculum',
'rigor',
'design',
'learn',
'skill',
'digit',
'world',
'technolog',
'industri',
'take',
'differ',
'approach',
'classic',
'teach',
'method',
'today',
'learn',
'facilit',
'collect',
'cocré',
'process',
'profession',
'environ']
Exercise 7: Bag of Word representation
The exercise is validated if all questions of the exercise are validated
For question 1, is the output of the CountVectorizer the following?
<6588x500 sparse matrix of type '<class 'numpy.int64'>'
with 79709 stored elements in Compressed Sparse Row format>
For question 2, is the output of print(df.iloc[:3,400:403].to_markdown()) the following?
| | talk | team | tell |
|---:|-------:|-------:|-------:|
| 0 | 0 | 0 | 0 |
| 1 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 |
For question 3, is the shape of the wordcount DataFrame (6588, 501) and the output of print(df.iloc[300:304,499:501].to_markdown()) the following?
| | youtube | label |
|----:|----------:|--------:|
| 300 | 0 | 0 |
| 301 | 0 | -1 |
| 302 | 1 | 0 |
| 303 | 0 | 1 |