#datascience machinelearning decisiontree jobapplication

IMAGE

PROBLEM STATEMENT

The presented use case includes several parameters that are analyzed while selecting the prospective employee such as age, marks of 10th, 12th, graduation & post graduation, hands on tools experience, work experience, current ctc and location preference. These are considered as predictors and whether the person is selected or not is considered as target variable. 

CODE:

import pandas as pd import numpy as np import matplotlib.pylab as plt from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report import sklearn.metrics

data=pd.read_csv('Job_Application.csv')

predictors = data[['Age','SSC','HSC','Grad','PG','Python','SQL','Excel','Work_ex','Current_CTC','Mumbai','Bangalore','Delhi','Notice_period']] targets = data.Selected

pred_train,pred_test,tar_train,tar_test = train_test_split(predictors, targets,test_size=0.4)

pred_train.shape pred_test.shape tar_train.shape tar_test.shape

classifier = DecisionTreeClassifier() classifier=classifier.fit(pred_train,tar_train) predictions=classifier.predict(pred_test)

sklearn.metrics.confusion_matrix(tar_test,predictions) sklearn.metrics.accuracy_score(tar_test,predictions)

from sklearn import tree from sklearn.tree._export import plot_tree import graphviz feature_names=['Age','SSC','HSC','Grad','PG','Python','SQL','Excel','Work_ex','Current_CTC','Mumbai','Bangalore','Delhi','Notice_period'] cn=['Yes','No'] dot_data=tree.export_graphviz(classifier,out_file='tree.dot',feature_names=feature_names,class_names=cn,filled=True) graph=graphviz.Source(dot_data)

After this, we need to run a command in the command prompt to convert the dot file into an image.

dot -Tpng tree.dot -o tree.png

SUMMARY