mohammedouahman

Book Name	Author	Rating	Customers_Rated	Price
0	The Power of your Subconscious Mind	Joseph Murphy	4.5 out of 5 stars	13,948	₹ 99.00
1	Think and Grow Rich	Napoleon Hill	4.5 out of 5 stars	16,670	₹ 99.00
2	Word Power Made Easy	Norman Lewis	4.4 out of 5 stars	10,708	₹ 130.00
3	Mathematics for Class 12 (Set of 2 Vol.) Exami...	R.D. Sharma	4.5 out of 5 stars	18	₹ 930.00
4	The Girl in Room 105	Chetan Bhagat	4.3 out of 5 stars	5,162	₹ 149.00
...	...	...	...	...	...
56	COMBO PACK OF Guide To JAIIB Legal Aspects Pri...	MEC MILLAN	4.5 out of 5 stars	114	₹ 1,400.00
57	Wren & Martin High School English Grammar and ...	Rao N	4.4 out of 5 stars	1,613	₹ 400.00
58	Objective General Knowledge	Sanjiv Kumar	4.2 out of 5 stars	742	₹ 254.00

mohammedouahman

df.head(61)

mohammedouahman

df = pd.read_csv("amazon_products.csv")
df.shape

mohammedouahman

results = []
for i in range(1, no_pages+1):
    results.append(get_data(i))
flatten = lambda l: [item for sublist in l for item in sublist]
df = pd.DataFrame(flatten(results),columns=['Book Name','Author','Rating','Customers_Rated', 'Price'])
df.to_csv('amazon_products.csv', index=False, encoding='utf-8')

mohammedouahman

no_pages = 2

def get_data(pageNo):  
    headers = {"User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:66.0) Gecko/20100101 Firefox/66.0", "Accept-Encoding":"gzip, deflate", "Accept":"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8", "DNT":"1","Connection":"close", "Upgrade-Insecure-Requests":"1"}

    r = requests.get('https://www.amazon.in/gp/bestsellers/books/ref=zg_bs_pg_'+str(pageNo)+'?ie=UTF8&pg='+str(pageNo), headers=headers)#, proxies=proxies)
    content = r.content
    soup = BeautifulSoup(content)
    #print(soup)

mohammedouahman

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import re
import time
from datetime import datetime
import matplotlib.dates as mdates
import matplotlib.ticker as ticker

mohammedouahman

np.argmax(predictions_single[0])

mohammedouahman

predictions_single = probability_model.predict(img)
print(predictions_single)
[[8.8914348e-05 1.3264636e-13 9.9108773e-01 1.2658383e-10 8.1463791e-03
  1.6905785e-08 6.7695131e-04 2.7492119e-17 5.1699739e-10 7.1339325e-17]]
plot_value_array(1, predictions_single[0], test_labels)
_ = plt.xticks(range(10), class_names, rotation=45)
plt.show()

mohammedouahman

# Plot the first X test images, their predicted labels, and the true labels.
# Color correct predictions in blue and incorrect predictions in red.
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
  plt.subplot(num_rows, 2*num_cols, 2*i+1)
  plot_image(i, predictions[i], test_labels, test_images)
  plt.subplot(num_rows, 2*num_cols, 2*i+2)

mohammedouahman

i = 12
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions[i],  test_labels)
plt.show()