#!/usr/bin/env python

# coding: utf-8

# In[164]:

import pandas as pd

import sklearn

import re

import nltk

from nltk.corpus import stopwords

from nltk.stem.wordnet import WordNetLemmatizer

from nltk.tokenize import RegexpTokenizer

import string

from gensim import corpora

from gensim.models import LsiModel

from nltk.stem.porter import PorterStemmer

from sklearn.feature_extraction.text import TfidfVectorizer

from gensim.models.coherencemodel import CoherenceModel

from sklearn.metrics.pairwise import linear_kernel

from sklearn.feature_extraction.text import CountVectorizer

from sklearn.metrics.pairwise import cosine_similarity

# In[165]:

book_data = pd.read_csv("books.csv")

user_data = pd.read_csv("users.csv")

# In[168]:

book_data.keys()

# In[169]:

book_data = book_data.drop(['publication_day','isbn','isbn13','publication_month','publication_day','is_ebook','work','average_rating','num_pages','format','edition_information','ratings_count','book_links','series_works','similar_books'],axis = 1)

# In[171]:

book_data = book_data.drop(['asin','kindle_asin','marketplace_id','country_code','text_reviews_count','public_document'], axis = 1)

# In[172]:

book_data.keys()

# In[173]:

book_data = book_data.drop(['_id','id'], axis = 1)

# In[177]:

book_data.head()

# In[178]:

for i in range (book_data.shape[0]):

book_data.iloc[i,6] = re.sub(r"[^a-zA-Z]+", ' ', book_data.iloc[i,6])

# In[179]:

word = ['name','count','shelf']

for i in range(book_data.shape[0]):

word_list = book_data.iloc[i,6].split();

book_data.iloc[i,6] = ' '.join([i for i in word_list if i not in word])

# In[180]:

for i in range (book_data.shape[0]):

book_data.iloc[i,5] = re.sub(r"[^a-zA-Z]+", ' ', book_data.iloc[i,5])

# In[181]:

word = ['name','count','shelf']

for i in range(book_data.shape[0]):

word_list = book_data.iloc[i,6].split();

book_data.iloc[i,6] = ' '.join([i for i in word_list if i not in word])

# In[184]:

def pre_process(x):

return ''

# In[185]:

columns = ['description','authors','publisher','popular_shelves']

for f in columns:

book_data[f] = book_data[f].apply(pre_process)

# In[186]:

def combine(x):

return (x['description']) + ' ' + (x['authors']) + ' ' + (x['publisher']) + ' ' + (x['popular_shelves'])

# In[188]:

book_data['train'] = book_data.apply(combine,axis = 1)

# In[190]:

word_stopped = TfidfVectorizer(stop_words='english')

book_data['train'] = book_data['train'].fillna('')

matrix = word_stopped.fit_transform(book_data['train'])

# In[192]:

co_sim = linear_kernel(matrix,matrix)

# In[194]:

book_data = book_data.reset_index()

# In[195]:

indexing = pd.Series(book_data.index, index=book_data['title']).drop_duplicates()

# In[ ]:

indexing = pd.Series(book_data.index, index=book_data['title','publisher']).drop_duplicates()

# In[223]:

resultss = []

# In[228]:

def get_recommendations(title,cosine_sim1 = co_sim):

return resultss

# In[229]:

book_data['title'][323]

# In[230]:

get_recommendations('The Door Into Summer')

# In[126]:

get_recommendations("Darwin's Dangerous Idea: Evolution and the Meanings of Life")

# In[162]:

get_recommendations("Darwin's Dangerous Idea: Evolution and the Meanings of Life")

# In[200]:

get_recommendations("Darwin's Dangerous Idea: Evolution and the Meanings of Life")

# In[203]:

get_recommendations('Storage Area Network Essentials: A Complete Guide to Understanding and Implementing Sans')

# In[208]:

get_recommendations('Letterhead and Logo Design 9')

# In[213]:

get_recommendations('What to Expect the First Year (What to Expect)')

# In[217]:

get_recommendations('The Fellowship of the Ring (The Lord of the Rings, #1)')

# In[227]:

get_recommendations('eBay for Dummies')

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