Problem Statement :
To build a Decision Tree model for prediction of car quality given other attributes about the car.
Data details:
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1. Title: Car Evaluation Database
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The dataset is available at “http://archive.ics.uci.edu/ml/datasets/Car+Evaluation”
2. Sources:
(a) Creator: Marko Bohanec
(b) Donors: Marko Bohanec (marko.bohanec@ijs.si)
Blaz Zupan (blaz.zupan@ijs.si)
(c) Date: June, 1997
3. Past Usage:
The hierarchical decision model, from which this dataset is derived, was first presented in M. Bohanec and V. Rajkovic: Knowledge acquisition and explanation for multi-attribute decision making. In 8th Intl Workshop on Expert Systems and their Applications, Avignon, France. pages 59-78, 1988.
Within machine-learning, this dataset was used for the evaluation of HINT (Hierarchy INduction Tool), which was proved to be able to completely reconstruct the original hierarchical model. This, together with a comparison with C4.5, is presented in B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by function decomposition. ICML-97, Nashville, TN. 1997 (to appear)
4. Relevant Information Paragraph:
Car Evaluation Database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX (M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.). The model evaluate cars according to the following concept structure:
CAR car acceptability
. PRICE overall price
. . buying buying price
. . maint price of the maintenance
. TECH technical characteristics
. . COMFORT comfort
. . . doors number of doors
. . . persons capacity in terms of persons to carry
. . . lug_boot the size of luggage boot
. . safety estimated safety of the car
Input attributes are printed in lowercase. Besides the target concept (CAR), the model includes three intermediate concepts: PRICE, TECH, COMFORT. Every concept is in the original model related to its lower level descendants by a set of examples (for these examples sets see http://www-ai.ijs.si/BlazZupan/car.html).
The Car Evaluation Database contains examples with the structural information removed, i.e., directly relates CAR to the six input attributes: buying, maint, doors, persons, lug_boot, safety.
Because of known underlying concept structure, this database may be particularly useful for testing constructive induction and structure discovery methods.
5. Number of Instances: 1728
(instances completely cover the attribute space)
6. Number of Attributes: 6
7. Attribute Values:
buying v-high, high, med, low
maint v-high, high, med, low
doors 2, 3, 4, 5-more
persons 2, 4, more
lug_boot small, med, big
safety low, med, high
8. Missing Attribute Values: none
9. Class Distribution (number of instances per class)
class N N[%]
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unacc 1210 (70.023 %)
acc 384 (22.222 %)
good 69 ( 3.993 %)
v-good 65 ( 3.762 %)
Tools to be used :
Numpy,pandas,scikit-learn
Python Implementation with code :
Import necessary libraries
Import the necessary modules from specific libraries.
import os import numpy as np import pandas as pd import numpy as np, pandas as pd import matplotlib.pyplot as plt from sklearn import tree, metrics
Load the data set
Use pandas module to read the bike data from the file system. Check few records of the dataset.
data = pd.read_csv('data/car_quality/car.data',names='buying','maint','doors','persons','lug_boot','safety','class'])
data.head()Output:
buying maint doors persons lug_boot safety class 0 vhigh vhigh 2 2 small low unacc 1 vhigh vhigh 2 2 small med unacc 2 vhigh vhigh 2 2 small high unacc 3 vhigh vhigh 2 2 med low unacc 4 vhigh vhigh 2 2 med med unacc
Check few information about the data set
data.info()
Output:
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1728 entries, 0 to 1727 Data columns (total 7 columns): buying 1728 non-null object maint 1728 non-null object doors 1728 non-null object persons 1728 non-null object lug_boot 1728 non-null object safety 1728 non-null object class 1728 non-null object dtypes: object(7) memory usage: 94.6+ KB
The train data set has 1728 rows and 7 columns.
There are no missing values in the dataset
Identify the target variable
data['class'],class_names = pd.factorize(data['class'])
The target variable is marked as class in the dataframe. The values are present in string format. However the algorithm requires the variables to be coded into its equivalent integer codes. We can convert the string categorical values into a integer code using factorize method of the pandas library.
Let’s check the encoded values now.
print(class_names) print(data['class'].unique())
Output:
Index([u'unacc', u'acc', u'vgood', u'good'], dtype='object') [0 1 2 3]
As we can see the values has been encoded into 4 different numeric labels.
Identify the predictor variables and encode any string variables to equivalent integer codes
data['buying'],_ = pd.factorize(data['buying']) data['maint'],_ = pd.factorize(data['maint']) data['doors'],_ = pd.factorize(data['doors']) data['persons'],_ = pd.factorize(data['persons']) data['lug_boot'],_ = pd.factorize(data['lug_boot']) data['safety'],_ = pd.factorize(data['safety']) data.head()
Output:
buying maint doors persons lug_boot safety class 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 2 0 0 0 0 0 2 0 3 0 0 0 0 1 0 0 4 0 0 0 0 1 1 0
Check the data types now :
data.info()
Output:
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1728 entries, 0 to 1727 Data columns (total 7 columns): buying 1728 non-null int64 maint 1728 non-null int64 doors 1728 non-null int64 persons 1728 non-null int64 lug_boot 1728 non-null int64 safety 1728 non-null int64 class 1728 non-null int64 dtypes: int64(7) memory usage: 94.6 KB
Everything is now converted in integer form.
Select the predictor feature and select the target variable
X = data.iloc[:,:-1] y = data.iloc[:,-1]
Train test split :
# split data randomly into 70% training and 30% test X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.3, random_state=0)
Training / model fitting:
# train the decision tree dtree = tree.DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0) dtree.fit(X_train, y_train)
Model parameters study :
# use the model to make predictions with the test data
y_pred = dtree.predict(X_test)
# how did our model perform?
count_misclassified = (y_test != y_pred).sum()
print('Misclassified samples: {}'.format(count_misclassified))
accuracy = metrics.accuracy_score(y_test, y_pred)
print('Accuracy: {:.2f}'.format(accuracy))
Output:
Misclassified samples: 86 Accuracy: 0.82
As you can see the algorithm was able to achieve classification accuracy of 82% on the held out set. Only 96 samples were misclassified.
Visualization of the decision graph :
import graphviz feature_names = X.columns dot_data = tree.export_graphviz(dtree, out_file=None, filled=True, rounded=True,feature_names=feature_names,class_names=class_names) graph = graphviz.Source(dot_data) graph
