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Machine learning : Random Forest Regression machinelearningtechnilesh

 Machine Learning :Part 4: Random Forest Regression ( Last Regression model )


We were learn all the stuff regrading the Machine learning . 
For Video Lecture on Machine Learning : Decision Go at bottom or google codewithnilesh.


Random Forest tree ( regression ) technilesh - bytecode.technilesh.com



Actually The
Random Forest Regression ( Tree )  is collection of many Regression model and the collection of Decision Trees . The Random Forest Regression is part of  Ensemble Learning . The Ensemble learning is collection of many models and collection of process to get accurate and prediction.

In Forest Tree model the NO of Decision tree is directly propositional to the accuracy of prediction.




Step 1 : Data Preprocessing :

Initially Import all Libraries like Pandas , numpy , matlibplot pyplot & Sklearn
using the pandas take data and classify in to dependent and independent variable.

Code :

Garry Raut ( Machine learner
) 

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

dataset = pd.read_csv('position_salaries.csv')
X = dataset.iloc[: , 1:2].values
Y = dataset.iloc[: ,2].values

The data in Position Salaries file is :
Position                                            Level                                 Salary
BusinessAnalyst                               1                                        45232
JuniorConsultant                              2                                        52300
Senior Consultant                            3                                        62300
Manager                                         4                                        84300
Country Manager                             5                                       110540
Region Manager                               6                                       160000
Partner                                           7                                        250000
Senior Partner                                 8                                        341231
C-level                                            9                                        670000
CEO                                               10                                       3000000



After this go to the Secound Step


Step 2 : Random  Forest Regression

this is simple code for calling random forest Regressor.
from sklearn.ensemble import RandomForestRegression
regressor = RandomForestRegression(n_estimator=10)regressor.fit(X,Y)


Full Source Code 
:
import numpy as np 
import pandas as pd
import matplotlib.pyplot as plt

dataset = pd.read_csv('position_salaries.csv')
X = dataset.iloc[: , 1:2].values
Y = dataset.iloc[: ,2].values

from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor()
regressor.fit(X,Y)



def desion():
    X_grid = np.arange(min(X), max(X), 0.1)
    X_grid = X_grid.reshape((len(X_grid), 1))
    plt.scatter(X, Y, color = 'green')
    plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')
    plt.title('Truth or Bluff (Support Vector Regression Model)CodewithNilesh')
    plt.xlabel('Position levelCodewithNilesh')
    plt.ylabel('SalaryCodewithNilesh')
    plt.show()
desion()

Result :
Taken by @ Garry Raut


For Video Lecture on Machine Learning : Random Forest Regression ( Tree )




machine learning random forest regression ? ,random forest regression machine learning mastery? ,


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