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Machine Learning : Decision Tree - machinelearningtechnilesh

 Machine Learning : Part 3 : Decision Tree 



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

Actually The decision Tress is normal Tree data Structure . we they having Root  , Leaf  and Internal . You get all idea of Decision Tree By just Viewing below image . 

Image made By Garry Raut : 



As You Get Idea from This Figure that Decision tree is actually a tree Data Structure .Where if The value is true then go toward the yes i.e. Right Tree .When the condition false or the value is not stratified then go Toward the Left tree.

The Equation for Decision tree is :


Entropy = Î£ - p * log(p)


Now we are making model for Prediction of salaries .

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


Step :2 : Decision tree regressor 

Firstly import the Decision tree regressor class from Sklearn libraries of sub libraries Tree ( Sklearn.tree ) . The class which we import from this libraries is DecisionTreeRegressor . TO access the class the create the regressor object which access the class . now fit the data in regressor object using the fit inbuilt method of machine learning .

Code : Garry Raut ( Machine learner ) 

from sklearn.tree import DecisionTreeRegressor
regressor= DecisionTreeRegressor(random_state=0)
regressor.fit(X ,Y)




Step 3 : Visualiztion of the data in Graphical view 

To View data in Graphical manner we used the Matlibplot librarie.

Code : Garry Raut ( Machine learner ) 

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()

We call the method and Show the graphs ou see the below results :



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For Video Lecture on Machine Learning : Decision Tree here






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