🔹Software Requirements🔹

🔹 Python 3

🔹 PIP python get-pip.py

🔹Our Data🔹

                         For this tutorial we will need to get user's follower list which we can either get directly from the steem blockchain by preparing some separate code but, I don't want to get into the details of querying the steem blockchain with python as that will be a whole new tutorial itself, so we will directly pick the data from this site with a little bit of webscraping.

                         This site doesn't return data prior to 1^st Jan 2018 but that should be much of a problem most of the time and we will start numbering days from 1^st Jan 2018. That is to say as 1^st Jan will be Day 1, 2^nd Jan will be Day 2 and so on.. First of all we will see how our data looks on graph. In order to do so we will do a scatter plot of our data points and for that we need the Matplotlib library which you can install by executing the below line on your cmd/terminal.

#importing matplotlib library

import matplotlib.pyplot as plot

days_arr = [[5], [10], [15], [20], [25], [30], [35], [40], [45], [50], [55], [60], [65], [70], [75], [80], [85], [90], [95], [100], [105], [110], [115], [120], [125], [130], [135], [140], [145], [150], [155], [160], [165], [170], [175], [180], [185], [190], [195], [200], [205], [210], [215], [220], [225], [230], [235], [240], [245], [250]]

# followers array is an array of the cumulative sum of followers

followers = [286, 416, 709, 952, 1231, 1554, 1777, 2024, 2299, 2463, 2644, 2803, 2986, 3099, 3312, 3410, 3577, 3736, 3854, 4020, 4118, 4258, 4392, 4474, 4580, 4699, 4824, 5018, 5243, 5351, 5558, 5744, 5815, 5872, 6006, 6088, 6244, 6379, 6469, 6559, 6656, 6753, 6830, 6896, 6959, 7056, 7124, 7150, 7208, 7372]

plot.scatter(days, followers, color='red', s=3, alpha=1.0)

# X-axis label

plot.xlabel('Day')

# Y-axis label

plot.ylabel('Steemit Followers') 

🔹Algorithm🔹

🔹 Linear Regression - Since we can visualize form the scatter plot above that the data is somewhat linear, so we for the sake of this tutorial we will use Linear Regression algorithm to find a line that passes through some of the points on the plot such that the loss function is minimum or to say line that best fits all the data points such that error is minimum.

                         To apply the algorithm to our data we will use a popular machine learning python library called Scikit Learn. To install the library copy the below command onto your cmd 💻 and hit enter.

#importing the linear model from scikit learn

from sklearn import linear_model

#importing matplotlib library

import matplotlib.pyplot as plot

import numpy as np

days = [[5], [10], [15], [20], [25], [30], [35], [40], [45], [50], [55], [60], [65], [70], [75], [80], [85], [90], [95], [100], [105], [110], [115], [120], [125], [130], [135], [140], [145], [150], [155], [160], [165], [170], [175], [180], [185], [190], [195], [200], [205], [210], [215], [220], [225], [230], [235], [240], [245], [250]]

followers = [286, 416, 709, 952, 1231, 1554, 1777, 2024, 2299, 2463, 2644, 2803, 2986, 3099, 3312, 3410, 3577, 3736, 3854, 4020, 4118, 4258, 4392, 4474, 4580, 4699, 4824, 5018, 5243, 5351, 5558, 5744, 5815, 5872, 6006, 6088, 6244, 6379, 6469, 6559, 6656, 6753, 6830, 6896, 6959, 7056, 7124, 7150, 7208, 7372]

plot.scatter(days, followers, color='red', s=3, alpha=1.0)
plot.xlabel('Day')
plot.ylabel('Steemit Followers')

linear_regression_classifier = linear_model.LinearRegression()
# fit a line such that error is minimum
linear_regression_classifier = linear_regression_classifier.fit(days, followers)

# predict the no. of followers on Day 23, Day 301 and Day 323

model_prediction = linear_regression_classifier.predict([[23], [301], [323]])

#plot the line of best fit

plot.plot([[23], [301], [323]], model_prediction, color='black', linewidth=1)

# print the predicted result on terminal

print(np.floor(model_prediction))

#Output the plot

plot.show()

💻Terminal output - [ 1261. 9357. 9974.] which is nothing but predicted no. of followers on Day 23, Day 301 and Day 323 respectively.

⚠️Note - This algorithm performs best only for linear data, if your data seems to be non-linear than it won't perform good. In that case, we have to implement other algorithms.