import pandas as pd
import numpy as np
import os 
from sklearn.feature_extraction.text import CountVectorizer
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text import hashing_trick
from keras.preprocessing.text import one_hot
from keras.preprocessing.text import text_to_word_sequence
from textblob.classifiers import NaiveBayesClassifier
 
df = pd.read_csv("G:/Tasks/Task1/names_sample.csv")
 
df=df['BusinessName']
 
dff = df.fillna(method='bfill')
 
 
df1=pd.read_csv("G:/Tasks/Task1/user_raw_data.csv")
df2 =df1.iloc[:2207]
df2 =df2.fillna(method='bfill')
 
 
df4= df2.join(dff,how='inner')
 
df4['new_clm'] = df4[['address', 'BusinessName']].apply(tuple, axis=1)
 
 
train =df4['new_clm']
 
cl = NaiveBayesClassifier(train)
 
 
cl.classify("682/a, 3rd floor, adabor 12 dhaka.")