Social Media Followers Prediction with Machine Learning

There are so many social media platforms today where you will find so many content creators in so many types of fields. As a social media consumer, the number of followers you have may not be of interest to you, but as a content creator or as a businessman, the number of followers you have is important for your content for reaching more audience. So, the task of social media followers prediction is very valuable for every content creator and every business that relies on social media. So if you want to learn how to predict your social media followers for the next month, this article is for you. In this article, I will walk you through the task of social media followers prediction with machine learning using Python.

Social Media Followers Prediction

To predict the increase in the number of followers you can expect to see, you need a dataset of your social media followers that can show you the activities of people in your social media account like:

  1. how many people have followed you every month 
  2. how many views results in how many followers 
  3. how many of your followers unfollow you every month

So it is very difficult to find such a dataset among the most common social media platforms like Facebook and Instagram as these platforms do not provide any data related to your followers. So for the task of social media followers prediction with machine learning, I collected data from my social media account on Medium, which is a social media platform for content writers, bloggers, and researchers. You can use the same process on your dataset whether you get it from Medium, Instagram, or any other social media application to predict your social media followers. For practice, you can use the same dataset that I am using.

Social Media Followers Prediction using Python

I will start the task of social media followers prediction with machine learning by importing the necessary Python libraries and the dataset that I have collected about my followers from Medium:

  period_start period_end  ...  subscribers_total    views
0     5/1/2020  5/31/2020  ...                  0    128.0
1     6/1/2020  6/30/2020  ...                  0  16130.0
2     7/1/2020  7/31/2020  ...                  0  14616.0
3     8/1/2020  8/31/2020  ...                  0   4053.0
4     9/1/2020  9/30/2020  ...                  0   5153.0

[5 rows x 11 columns]

In the 7th line of the above code, I have deleted the last row of the dataset as it contains data about this month. Now I will have a look at the number of followers that I gained every month on my account since I joined this social media platform:

Social Media Followers

Now let’s have a look at the total number of followers I end up with every month:

social media followers prediction with machine learning

Now let’s take a look at one of the most important features, which is the total number of views I get each month:

total views every month

Now I will be using the autots library in Python, which is one of the best data science libraries for time series forecasting. If you have never used this library before, you can easily install it on your system using the pip command:

  • pip install autots

Now here’s how we can predict the increase in the number of followers we can expect to see over the next four months:

               followers_gained
2021-08-31        693.465876
2021-09-30        617.750000
2021-10-31        650.000000
2021-11-30        634.750000

Summary

So this is how you can predict the increase in the number of your followers on any social media platform. As a social media consumer, the number of followers you have may not be of interest to you, but as a content creator or as a businessman, the number of followers you have is important for your content for reaching more audience. I hope you liked this article on the task of social media followers prediction with machine learning using Python. Feel free to ask your valuable questions in the comments section below.

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Aman Kharwal
Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder
Articles: 1103

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