Instagram Reach Analysis using Python

 One of the most widely used social media platforms right now is Instagram. Instagram is used professionally by those who want to promote their brands, develop their portfolios, blog, and produce other types of content. Instagram is a well-known programme used by millions of users in a variety of niches, and it is always evolving to better serve both users and content providers. However, when this fluctuates, it impacts the audience for our postings, which has a long-term impact on us. Therefore, a content producer must examine the analytics of their Instagram reach if they hope to succeed on the platform over the long term. The use of data science to social media addresses this issue. This post is for you if you want to understand how to use our Instagram data for the purpose of analyzing Instagram reach. I'll walk you through an Instagram Reach Analysis using Python in this post, which will assist content producers learn how to adjust to Instagram's adjustments over time.

Instagram Reach Analysis

I have spent a lot of time investigating Instagram reach. I track statistics on how well each Instagram post performs after a week after I publish it. That makes it easier to comprehend how Instagram's algorithm functions. There are some APIs, but they don't operate properly, so if you want to examine the reach of your Instagram account, you'll need to gather your data manually. Therefore, manually gathering your Instagram data is preferable.

You may use the information I've gathered from my Instagram account if you're a data science student and want to learn how to analyze Instagram reach using Python. The dataset I used to analyze Instagram reach is available for download here. I'll walk you through the challenge of analyzing and predicting Instagram reach using machine learning and Python in the part that follows.

Instagram Reach Analysis using Python

Now that the essential Python modules and the dataset have been imported, let's begin the work of assessing the reach of my Instagram account:

Let's check to see whether this dataset has any null values before proceeding with anything:

As a result, each column has a null value. Let's disregard all of these null values and continue:

To understand the data kind of each column, let's look at the insights of the columns:

Analyzing Instagram Reach

Let's now examine the audience reach of my Instagram posts. I'll start by examining the dispersion of the impressions I have from home.

The impressions I receive on Instagram's home page demonstrate how widely my posts are seen by my followers. It's difficult to reach all of my followers every day, I can tell after seeing the reactions from my house. Let's now examine the distribution of the impressions that hashtags gave me:

In order to categorise our Instagram posts and expand our audience based on the type of material we are producing, we utilise hashtags. Looking at hashtag impressions reveals that while many new users may be found with hashtags, not all postings can be found using them. Let's now examine the distribution of impressions I have collected from Instagram's explore section:

Instagram's suggestion mechanism is seen in the explore section. Using information about their preferences and interests, it suggests content to users. I can tell from my impressions of the explore area that Instagram does not seem to recommend our content to people very often. When compared to the reach I get through hashtags, some posts have received a good reach from the explore area, but overall, it's still extremely little.

Let's now examine the percentage of Instagram impressions I receive from various sources:

According to the above donut figure, my followers account for roughly 50% of the reach, followed by hashtags (38.1%), the explore section (9.14%), and other sources (3.01%).

Analyzing Content

Let's now review the information in my Instagram postings. Two variables in the dataset—caption and hashtags—will help us better identify the type of material I share on Instagram.

To examine the most often occurring terms in the captions of my Instagram pictures, let's make a word cloud of the caption column:

To view the hashtags that appeared the most frequently in my Instagram postings, let's make a verticality of the hashtags column:

Analyzing Relationships

Let's now examine relationships to see what influences our Instagram reach the most. It will also aid in our comprehension of the Instagram algorithm.

Let’s have a look at the relationship between the number of likes and the number of impressions on my Instagram posts:

There is a linear relationship between the number of likes and the reach I got on Instagram. Now let’s see the relationship between the number of comments and the number of impressions on my Instagram posts:

It looks like the number of comments we get on a post doesn’t affect its reach. Now let’s have a look at the relationship between the number of shares and the number of impressions:

A more number of shares will result in a higher reach, but shares don’t affect the reach of a post as much as likes do. Now let’s have a look at the relationship between the number of saves and the number of impressions:

There is a linear relationship between the number of times my post is saved and the reach of my Instagram post. Now let’s have a look at the correlation of all the columns with the Impressions column:

So we can say that more likes and saves will help you get more reach on Instagram. The higher number of shares will also help you get more reach, but a low number of shares will not affect your reach either.

Analyzing Conversion Rate

In Instagram, conversation rate means how many followers you are getting from the number of profile visits from a post. The formula that you can use to calculate conversion rate is (Follows/Profile Visits) * 100. Now let’s have a look at the conversation rate of my Instagram account:

So the conversation rate of my Instagram account is 31% which sounds like a very good conversation rate. Let’s have a look at the relationship between the total profile visits and the number of followers gained from all profile visits:

The relationship between profile visits and followers gained is also linear.

Instagram Reach Prediction Model

Now in this section, I will train a machine learning model to predict the reach of an Instagram post. Let’s split the data into training and test sets before training the model:

Now here’s is how we can train a machine learning model to predict the reach of an Instagram post using Python:

Now let’s predict the reach of an Instagram post by giving inputs to the machine learning model:

Summary

So this is how you can analyze and predict the reach of Instagram posts with machine learning using Python. If a content creator wants to do well on Instagram in a long run, they have to look at the data of their Instagram reach. That is where the use of Data Science in social media comes in. I hope you liked this article on the task of Instagram Reach Analysis using Python. Feel free to ask valuable questions in the comments section below.