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How to Prepare Text for Machine Learning pipeline with compiled Regular Expressions and/or List Comprehension?


By : Don Williamson
Date : October 14 2020, 02:22 PM
I wish this helpful for you You are treating the words as separate symbols in [word for word in nosymb if word not in STOPWORDS]. Besides, you do not strip leading/trailing spaces and do not "shrink" excess spaces that result from your previous cleanup operations.
Here is an updated method:
code :
def text_prepare(text):
    """
    text: a string
        return: modified initial string
    """
    lower = text.lower() # lowercase text
    space_replace = REPLACE_BY_SPACE_RE.sub(" ",lower) #replace REPLACE_BY_SPACE_RE symbols by space in text
    nosymb = BAD_SYMBOLS_RE.sub("",space_replace) # delete symbols which are in BAD_SYMBOLS_RE from text
    text = re.sub(r"\s*\b(?:{})\b".format("|".join(STOPWORDS)), "", nosymb) # delete STOPWORDS
    return re.sub(r" {2,}", " ", text.strip())


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Extracting data from a file using regular expressions and storing in a list to be compiled into a dictionary- python


By : user3248966
Date : March 29 2020, 07:55 AM
I hope this helps . If you want to read a file twice, you have to seek back to the beginning.
code :
InFile.seek(0)

Looking to use Machine Learning to automate customer service. How do I apply Machine Learning to generate text replies t


By : Brandon Ly
Date : March 29 2020, 07:55 AM
wish helps you I assume in this answer you have some background in machine learning. I assume also a slight simplification of the problem, where you just want to match an e-mail from customer with an existing bug/issue/category (then you could bring up all replies from support).
Here are some good heuristics / things to keep in mind when trying to apply machine learning:

Build regular expressions using previously compiled regular expressions


By : Ofir Stefanson
Date : March 29 2020, 07:55 AM
it fixes the issue You won't be able to reuse a compiled regular expression, but you can do better than plain string concatenation.
Python's regex module supports Perl's (?(DEFINE)...) syntax.
code :
import regex

common = r"""(?(DEFINE)
             (?<VALID_NAME>\b[_a-zA-Z][_a-zA-Z0-9]*)
             (?<SOMETHING_ELSE>[-+/*])
         )"""

pattern = regex.compile(common + r"((?&VALID_NAME)\.)*(?&VALID_NAME)")

result = pattern.search("42hello! foo.bar.baz")

print(result[0])
foo.bar.baz

How to prepare paneldata to machine learning in Python?


By : Danny
Date : March 29 2020, 07:55 AM
I hope this helps . Use DataFrameGroupBy.shift in loop:
code :
for i in range(1, 4):
    df[f'gcp-{i}'] = df.groupby('ID')['gcp'].shift(i)
print (df)
       ID  year  age  area  debt_ratio  gcp  gcp-1  gcp-2  gcp-3
0  654001  2013   49  East        0.14    0    NaN    NaN    NaN
1  654001  2014   50  East        0.17    0    0.0    NaN    NaN
2  654001  2015   51  East        0.23    1    0.0    0.0    NaN
3  654001  2016   52  East        0.18    0    1.0    0.0    0.0
4  112089  2013   39  West        0.13    0    NaN    NaN    NaN
5  112089  2014   40  West        0.15    0    0.0    NaN    NaN
6  112089  2015   41  West        0.18    1    0.0    0.0    NaN
7  112089  2016   42  West        0.21    1    1.0    0.0    0.0
N = df['ID'].value_counts().max()

for i in range(1, N):
    df[f'gcp-{i}'] = df.groupby('ID')['gcp'].shift(i)
print (df)
       ID  year  age  area  debt_ratio  gcp  gcp-1  gcp-2  gcp-3
0  654001  2013   49  East        0.14    0    NaN    NaN    NaN
1  654001  2014   50  East        0.17    0    0.0    NaN    NaN
2  654001  2015   51  East        0.23    1    0.0    0.0    NaN
3  654001  2016   52  East        0.18    0    1.0    0.0    0.0
4  112089  2013   39  West        0.13    0    NaN    NaN    NaN
5  112089  2014   40  West        0.15    0    0.0    NaN    NaN
6  112089  2015   41  West        0.18    1    0.0    0.0    NaN
7  112089  2016   42  West        0.21    1    1.0    0.0    0.0

What is the correct way to prepare dataset for machine learning?


By : khazelgren
Date : March 29 2020, 07:55 AM
this will help This is the most important part of any machine learning algorithm. You need to build your dataset, extract, make, scale, normalize features.
If you want to use some supervised learning algorithm, you need labeled data. There is several ways to achieve this:
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