Insta-Bot

An automated web scraper built using Selenium WebDriver to parse "food" based data from Instagram and perform a case study on it

This is an advanced bot for scraping data that uses Selenium to parse Instagram data.

The use case for this project is that a friend has strted his "food blogging Instagram account". He wants to follow a lot of individuals so that he can easily get recognized, but it is a tedious task. This bot is programmed to assist him, using Selenium by performing automation.
All specific activity functions are developed in the first section, such as following users, getting likes, getting tags, etc.
The analysis and the case study are performed in the second part.

It is divided into 3 use cases.

First, he has to evaluate the behaviours of these bloggers now that his friend has followed a lot of various food bloggers. The first 10 handles are obtained from the list of Instagram handles you obtained by searching for 'food' and the top 5 with the highest number of followers are discovered. It also finds the number of posts these handles have made in the previous 3 days and uses a graph to show this data.
The second use case is, the friend needs a list of hashtags to use in his posts. The 5 handles acquired in the last part are opened for that and the contents of each handle's first 10 posts are scraped. It prepares a list of all the terms used in all the scraped articles and measures the frequency of each word. The hashtags most common among these bloggers are contained in a csv file with two columns: the word and its frequency are created. In the context of a pie map, the top 5 hashtags collected and the number of times these bloggers have used them in the scrapped posts are plotted.
The third case of usage is forming a metric to measure the percentage of average followers. The metric is called "Follower: Likes ratio" and is measured as follows: the likes of the 5 handles are derived from the top 10 entries. In order to get the average followers, the average likes for a handle are determined by the average likes received by the number of followers of the handle: the ratio of each handle is divided. To reflect the data, a bar graph is plotted.

How to run?

Clone or download the project locally
Install Selenium
Install the compatible Chromedriver version in your local directory
Open the ipynb file in Jupyter Notebook and make sure the requirements are satisfied
Voila! Selenium magic awaits !