Import Important library.

In [3]:
import pandas as pd
import numpy as np
import nltk
from nltk.corpus import stopwords
In [5]:
df = pd.read_excel("BankReviews.xlsx")
df.head()
Out[5]:
Date Stars Reviews BankName
0 2017-04-10 5 Great job, Wyndham Capital! Each person was pr... Wyndham Capital Mortgage
1 2017-02-10 5 Matthew Richardson is professional and helpful... Wyndham Capital Mortgage
2 2017-08-21 5 We had a past experience with Wyndham Mortgage... Wyndham Capital Mortgage
3 2017-12-17 5 We have been dealing with Brad Thomka from the... Wyndham Capital Mortgage
4 2016-05-27 5 I can't express how grateful I am for the supp... Wyndham Capital Mortgage
In [6]:
# Dropping the irrelevant variables
df.drop(['Date','BankName'],axis=1,inplace=True)
In [7]:
df.head(3)
Out[7]:
Stars Reviews
0 5 Great job, Wyndham Capital! Each person was pr...
1 5 Matthew Richardson is professional and helpful...
2 5 We had a past experience with Wyndham Mortgage...
In [8]:
df.shape
Out[8]:
(505, 2)

Define X and Y

In [9]:
X,y=df.Reviews,df.Stars
In [10]:
X.head()
Out[10]:
0    Great job, Wyndham Capital! Each person was pr...
1    Matthew Richardson is professional and helpful...
2    We had a past experience with Wyndham Mortgage...
3    We have been dealing with Brad Thomka from the...
4    I can't express how grateful I am for the supp...
Name: Reviews, dtype: object
In [11]:
y.head()
Out[11]:
0    5
1    5
2    5
3    5
4    5
Name: Stars, dtype: int64
In [12]:
# Pickling the dataset

import pickle
with open('Review.pickle','wb') as f:
    pickle.dump(X,f)
    
with open('sent.pickle','wb') as f:
    pickle.dump(y,f)
In [13]:
# Unpickling dataset
X_in = open('Review.pickle','rb')
y_in = open('sent.pickle','rb')
X = pickle.load(X_in)
y = pickle.load(y_in)
In [14]:
# Creating the corpus
import re
corpus = []
for i in range(0, len(X)):
    review = re.sub(r'\W', ' ', str(X[i]))
    review = review.lower()
    review = re.sub(r'^br$', ' ', review)
    review = re.sub(r'\s+[a-z]\s+', ' ',review)
    review = re.sub(r'^[a-z]\s+', '', review)
    review = re.sub(r'\s+', ' ', review)
    corpus.append(review)
In [15]:
X[0]
Out[15]:
'Great job, Wyndham Capital! Each person was professional and helped us move through our refinance process smoothly. Thank you!'
In [16]:
corpus[1]
Out[16]:
'matthew richardson is professional and helpful he helped us find the correct product for our mortgage thank you very much for the excellent service matthew '
In [17]:
from nltk.stem import WordNetLemmatizer
# sentences = nltk.sent_tokenize(paragraph)
lemmatizer = WordNetLemmatizer()

# Lemmatization
for i in range(len(corpus)):
    words = nltk.word_tokenize(corpus[i])
    words = [lemmatizer.lemmatize(word) for word in words]
    corpus[i] = ' '.join(words)

Creating Tf - Idf model

In [18]:
# Creating the Tf-Idf model
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(max_features = 2000, min_df = 0.05, max_df = 0.8, stop_words = stopwords.words('english'))
X = vectorizer.fit_transform(corpus).toarray()
In [19]:
X
Out[19]:
array([[0.        , 0.        , 0.        , ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.        , 0.        , ..., 0.        , 0.33245169,
        0.        ],
       ...,
       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.12340384, 0.        , ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.        , 0.        , ..., 0.1137738 , 0.08430181,
        0.1272345 ]])
In [20]:
X.shape
Out[20]:
(505, 137)

Apply Logistic regression

In [22]:
from sklearn.model_selection import train_test_split

train_x,test_x,train_y,test_y=train_test_split(X,
                                              y,
                                              test_size=.3,
                                              random_state=42)
In [23]:
from sklearn.linear_model import LogisticRegression

logreg = LogisticRegression()
logreg.fit( train_x, train_y )
Out[23]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)

Model Accuracy Eveluation

In [25]:
#Evelution of train and test accuracy
from sklearn import metrics
test_accuracy=metrics.accuracy_score(test_y,logreg.predict(test_x))
print('test_accuracy: ',test_accuracy)

train_accuracy=metrics.accuracy_score(train_y,logreg.predict(train_x))
print('train_accuracy: ',train_accuracy)

test_accuracy:  0.881578947368421
train_accuracy:  0.9178470254957507
In [28]:
# Creating a confusion matrix

from sklearn import metrics

cm = metrics.confusion_matrix(test_y,
                            logreg.predict(test_x), [1,5] )
cm

import matplotlib.pyplot as plt
import seaborn as sn
%matplotlib inline

sn.heatmap(cm, annot=True,  fmt='.2f', xticklabels = ["1", "5"] , yticklabels = ["1", "5"] )
plt.ylabel('True label')
plt.xlabel('Predicted label')
Out[28]:
Text(0.5, 15.0, 'Predicted label')

As we can see from confusion matrix our model is not over fitted

Final Prediction

In [29]:
#Concatenating final prediction with original data set(all obersvations):

pred_stars=pd.DataFrame(logreg.predict(X),columns=['predicted_stars'])
  
testfile = pd.concat([df, pred_stars], axis=1)

testfile.head(10)
Out[29]:
Stars Reviews predicted_stars
0 5 Great job, Wyndham Capital! Each person was pr... 5
1 5 Matthew Richardson is professional and helpful... 5
2 5 We had a past experience with Wyndham Mortgage... 5
3 5 We have been dealing with Brad Thomka from the... 5
4 5 I can't express how grateful I am for the supp... 5
5 5 I had the pleasure of working with Wyndham Cap... 5
6 5 My experience with Mattison was beyond greatly... 5
7 5 Patrick answered all my questions by email imm... 5
8 5 I loved working with this group of people! The... 5
9 5 Great web interface for both the loan applicat... 5

Final Submission in excel file

In [30]:
#Exporting testfile to csv for final submission

testfile.to_csv('Review_submission.csv',index=False)
In [ ]:

Happy Learning....