diff --git a/Python/web_scraping_Amazon b/Python/web_scraping_Amazon
new file mode 100644
index 0000000..fd236db
--- /dev/null
+++ b/Python/web_scraping_Amazon
@@ -0,0 +1,76 @@
+#Amazon web scraping
+#website selected = Amazon
+
+#Scraps a page from Amazon website and collects all the product related information from the website and store them in a data frame.
+
+import pandas as pd
+import numpy as np
+import re
+from urllib.request import urlopen
+from bs4 import BeautifulSoup
+import requests
+
+
+no_pages = 1
+
+def get_data(pageNo):  
+    
+    r = requests.get('https://www.amazon.in/gp/bestsellers/dvd/21360334031/ref=zg_bs_pg_?'+str(pageNo)+'ie=UTF8&pg='+str(pageNo))
+    content = r.content
+    soup = BeautifulSoup(content)
+    
+    alls = []
+    for d in soup.findAll('div', attrs={'class':'a-section a-spacing-none aok-relative'}):
+ 
+        name = d.find('a', attrs={'class':'a-link-normal'})
+        n = name.find_all('img', alt=True)
+        
+        userRatings = d.find('span', attrs={'class':'zg-badge-text'})
+        
+        stars = d.find('span', attrs={'class':'a-icon-alt'})
+        
+        NoOfRatings = d.find('a', attrs={'class':'a-size-small a-link-normal'})
+        
+        all1=[]
+
+        if name is not None:
+            #print(n[0]['alt'])
+            all1.append(n[0]['alt'])
+        else:
+            all1.append("Movie name cannot be found")
+            
+        if userRatings is not None:
+            #print(rating.text)
+            all1.append(userRatings.text)
+        else:
+            all1.append('0')
+            
+        if stars is not None:
+            #print(rating.text)
+            all1.append(stars.text)
+        else:
+            all1.append('0')
+            
+        if NoOfRatings is not None:
+            all1.append(NoOfRatings.text)
+        else:
+            all1.append('0')
+            
+       
+        alls.append(all1)    
+    return alls
+
+
+
+
+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=['Movie Name', 'User Rating', 'Stars', 'No of User Ratings'])
+df.to_csv('actionMovies.csv', index=False, encoding='utf-8')
+
+df = pd.read_csv("actionMovies.csv")
+
+
+df.head(5)