Day 39: 60 days of Data Science and Machine Learning Series
Scikit learn with a project..
Welcome back peeps. In this post we are going to understand the basics of Scikit learn with a project.
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Scikit learn, a library which is written in Python and built upon Scipy, Matplotlib and Numpy provides a set of useful and efficient tools for machine learning and statistical modeling including regression, classification, clustering, predictive data analysis and dimensionality reduction etc and known as the most robust and useful library for Machine Learning.
In this post we are going to build a logistic regression classifier to classify movie reviews as either positive or negative.
The dataset for the project can be found here
Let’s dive in!
Import necessary files
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction import TfidTransformer
import re
from nltk.stem.porter import PorterStemmer
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegressionCVLoad the data
df= pd.read_csv('Path to data file/data.csv')
df.info()Output —
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50000 entries, 0 to 49999
Data columns (total 2 columns):
review 50000 non-null object
sentiment 50000 non-null int64
dtypes: int64(1), object(1)
memory usage: 781.3+ KBTransforming Documents into Feature Vectors
count = CountVectorizer()docs = np.array (['The sun is shining',
'The weather is sweet',
'The sun is shining, the weather is sweet, and one and one is two'])bag = count.fit_transform(docs)
print(bag.toarray())Output —
[[0 1 0 1 1 0 1 0 0]
[0 1 0 0 0 1 1 0 1]
[2 3 2 1 1 1 2 1 1]]Print Vocab
print(count.vocabulary_)Output —
{'the': 6, 'sun': 4, 'is': 1, 'shining': 3, 'weather': 8, 'sweet': 5, 'and': 0, 'one': 2, 'two': 7}Term Frequency-Inverse Document Frequency
A good reference to TF-IDF can be found here :
np.set_printoptions(precision = 2)tfidf = TfidTransformer(use_idf = True, norm ='l2',smooth_idf = True)
Data Preparation
def preprocessor(text):
text = re.sub('<[^>]*>', '', text)
emoticons = re.findall('(?::|;|=)(?:-)?(?:\)|\(|D|P)', text)
text = re.sub('[\W]+', ' ', text.lower()) +\
' '.join(emoticons).replace('-', '')
return text
preprocessor(df.loc[0,'review'][-50:])
df['review'] = df['review'].apply(preprocessor)Documents Tokenization
porter = PorterStemmer()
def tokenizer(text):
return text.split()
def tokenizer_porter(text):
return [porter.stem(word) for word in text.split()]print(tokenizer('Its a wonderful day'))
print(tokenizer_porter('Beautiful day'))stop = stopwords.words('english')
[w for w in tokenizer_porter('Snow feels magical during New year')[-5:] if w not in stop]Output —
['Its', 'a', 'wonderful', 'day']
['beauti', 'day']
['feel', 'magic', 'dure', 'new', 'year']Document Classification Using Logistic Regression
tfidf = TfidfVectorizer(
strip_accents = None,
lowercase=False, preprocessor=None, tokenizer= tokenizer_porter,
use_idf = True, norm='l2',
smooth_idf= True)
y = df.sentiment.values
X = tfidf.fit_transform(df.review)
X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=1,test_size=0.5,shuffle=False)
clf = LogisticRegressionCV( cv=5, scoring = 'accuracy', random_state=0,n_jobs=-1,verbose = 3, max_iter = 300).fit(X_train,y_train)Model Evaluation
clf.score(X_test,y_test)Output —
0.89604Learnings —
How to build a logistic regression classifier using scikit-learn, clean and pre-process text data, perform feature extraction with the Natural Language Toolkit (NLTK), tune model hyper parameters and evaluate the model.
Day 40: Coming soon!
Follow and Stay tuned. Keep coding :)
For other projects, tune to —
Build Machine Learning Pipelines( With Code)
Recurrent Neural Network with Keras
Clustering Geolocation Data in Python using DBSCAN and K-Means
Facial Expression Recognition using Keras
Hyperparameter Tuning with Keras Tuner
Custom Layers in Keras
That’s it fellas. Peace out and keep coding :)
Stay Tuned and of-course let me end this post with a quote by Steve Jobs ;)
“You have to be burning with an idea, or a problem, or a wrong that you want to right. If you’re not passionate enough from the start, you’ll never stick it out.”
