Random Forest Regression Implementation

Random forest is a non linear regression algorithm that creates decision trees on data samples and then gets the prediction from each of them and finally selects the best solution by majority vote.
*It is a type of ensemble learning that means combining many models into one and here it combines many decision trees.

To see the decision tree model implementation click here

Here is how Random Forest looks like:

Now Let us start

For this model we will be using random forest to predict a salary of an employee with position level of 6.5

Importing the libraries

In [6]:

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

Importing the dataset / Create Own

In [9]:

df = pd.DataFrame({'Position': ['Business Analyst', 'Junior Consultant', 'Senior Consultant', 'Manager', 'Country Manager', 'Region Manager', 'Partner', 'Senior Partner', 'c-level', 'CEO'], 'Level':[1,2,3,4,5,6,7,8,9,10], 'Salary': [45000, 50000, 60000, 80000, 110000, 150000, 200000, 300000, 500000, 1000000]})
x = df.iloc[: , 1:2].values
y = df.iloc[:, 2].values
print(df)

            Position  Level   Salary
0   Business Analyst      1    45000
1  Junior Consultant      2    50000
2  Senior Consultant      3    60000
3            Manager      4    80000
4    Country Manager      5   110000
5     Region Manager      6   150000
6            Partner      7   200000
7     Senior Partner      8   300000
8            c-level      9   500000
9                CEO     10  1000000

Fitting Random Forest Regression to the dataset

In [10]:

from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators = 10, random_state = 0)
regressor.fit(x, y)

Out[10]:

RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
           max_features='auto', max_leaf_nodes=None,
           min_impurity_decrease=0.0, min_impurity_split=None,
           min_samples_leaf=1, min_samples_split=2,
           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,
           oob_score=False, random_state=0, verbose=0, warm_start=False)

Predicting a new result

In [11]:

y_pred = regressor.predict([[6.5]])

Visualising the Random Forest Regression results (higher resolution)

In [14]:

X_grid = np.arange(min(x), max(x), 0.01)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(x, y, color = 'red')
plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')
plt.title('Truth or Bluff (Random Forest Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()

In [ ]:

Learn About Data Preprocessing : Click Here