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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 |