Tag: instagram

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Machine-Learning-Image-Searcher

Motivation

A person can take a photo of his dog and write caption hashtag #Tree instead of #Dog 😀 , I thought about how can companies and we improve the searching process such that we get only the real target we want (ex. images that contain items or objects that we need).
I don’t want to search for Tree and get Cats in results instead 🙂 and vice versa.

First thing comes to my head is Object Detection and Recognition techniques which fit perfectly in such cases.

Here is a simple example of how ML can cause amazing improvements. Think from Marketing perspective. Let’s say we want to target some type of audience based on their photos on social media(perfect example here maybe Instagram) to contact with posts authors or announce about our new sales!!!

Real example: we have animals(Cats) food shop and we want to target Instagram users who have an animal(Cat). In the example below output shows how searching using:

  • Human Hashtags + ML = 20(cats)/20(total result) -> [100% correct]
  • Human Hashtags Only = 12(cats) / 20(total result) -> [60% correct]

NodeJS Module: http://npm.im/ml-image-searcher

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Python Instagram Followers Scraper






import sys
# import instaloader
from include import *


'''
username = 'INSTAGRAM_USERNAME_HERE'
password = 'INSTAGRAM_PASSWORD_HERE'


username = ''
password = ''


L = instaloader.Instaloader()
L.login(insta_username, insta_password)

profile = instaloader.Profile.from_username(L.context, 'areyoukhaled')

print ('you have ' + str(len(profile.get_followers())))

for post in profile.get_followers():
    print(post)

# followers = set(profile.get_followers())
# print('Fetching followers of profile: {} ' + len(followers) + ' .'.format(profile.username))
#
# print(followers)

print('Storing Followers into file.')
with open( PROFILE + ' (followers).txt', 'w+') as f:
 cnt = 1
 for follower in followers:
 print(follower.username, file=f)
 sys.stdout.write("%d of %d\r" %( cnt, len(followers)) )
 sys.stdout.flush()
 cnt+=1
'''