Random_Forest.py

# Platform: `linux(Ubuntu 18.04)'
# python version: 3.6
# predicting SDN traffic using Random Forest
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
import matplotlib.pyplot as plt
import pandas as pd
from math import sqrt

# Importing the dataset
dataset = pd.read_csv(
    '/home/shadowziyus/Documents/'
    'SDN-Traffic-Prediction-Through-Machine-Learning/Dataset.csv')
x = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, 4].values

# Splitting the dataset into the Training set and Test set
x_train, x_test, y_train, y_test = train_test_split(
    x, y, test_size=0.2, random_state=0)

# Fitting Random Forest Regression to the dataset
regressor = RandomForestRegressor(n_estimators=100, random_state=0)
regressor.fit(x_train, y_train)

# predictions
y_pred = regressor.predict(x_test)
error = y_pred - y_test
error_rms = 0
sum = 0
for i in error:
    sum += (i**2)
sum /= len(error)
print(len(error))
print(sum)
error_rms = sqrt(sum)
print(error_rms)
# seprating seconds form the Dataset
sec = x_test[:, 0]


# Visualizing Random Forest for the dataset
plt.scatter(sec, y_pred, color='blue')
plt.savefig(
    '/home/shadowziyus/Documents/'
    'SDN-Traffic-Prediction-Through-Machine-Learning/random_forest_predicted.png')
plt.show()
plt.scatter(sec, y_test, color='red')
plt.savefig(
    '/home/shadowziyus/Documents/'
    'SDN-Traffic-Prediction-Through-Machine-Learning/random_forest_actual.png')
plt.show()

error = y_pred - y_test
plt.scatter(sec, error, color='black')
plt.savefig(
    '/home/shadowziyus/Documents/'
    'SDN-Traffic-Prediction-Through-Machine-Learning/random_forest_error.png')
plt.show()