Classification Algorithm

Clasification is one of the Machine Learning Task that identitfies the class to which an Instance Belongs.

Here, the observed health and medical records of horses. By applying Classifier Algorithm the data was studied and compared with different two algorithms. Predict the survival of horse based on the data and accuracy was observed and found Randomforset algorithm is more accurate than Decision Tree algorithm.

Import Libraries

from sklearn import metrics
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Import Data

animals =pd.read_csv('/content/DataHorse.csv')
animals.head()

	surgery	age	hospital_number	rectal_temp	pulse	respiratory_rate	temp_of_extremities	peripheral_pulse	mucous_membrane	capillary_refill_time	pain	peristalsis	abdominal_distention	nasogastric_tube	nasogastric_reflux	nasogastric_reflux_ph	rectal_exam_feces	abdomen	packed_cell_volume	total_protein	abdomo_appearance	abdomo_protein	outcome	surgical_lesion	lesion_1	lesion_2	lesion_3	cp_data
0	no	adult	530101	38.5	66.0	28.0	cool	reduced	NaN	more_3_sec	extreme_pain	absent	severe	NaN	NaN	NaN	decreased	distend_large	45.0	8.4	NaN	NaN	died	no	11300	0	0	no
1	yes	adult	534817	39.2	88.0	20.0	NaN	NaN	pale_cyanotic	less_3_sec	mild_pain	absent	slight	NaN	NaN	NaN	absent	other	50.0	85.0	cloudy	2.0	euthanized	no	2208	0	0	no
2	no	adult	530334	38.3	40.0	24.0	normal	normal	pale_pink	less_3_sec	mild_pain	hypomotile	none	NaN	NaN	NaN	normal	normal	33.0	6.7	NaN	NaN	lived	no	0	0	0	yes
3	yes	young	5290409	39.1	164.0	84.0	cold	normal	dark_cyanotic	more_3_sec	depressed	absent	severe	none	less_1_liter	5.0	decreased	NaN	48.0	7.2	serosanguious	5.3	died	yes	2208	0	0	yes
4	no	adult	530255	37.3	104.0	35.0	NaN	NaN	dark_cyanotic	more_3_sec	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	74.0	7.4	NaN	NaN	died	no	4300	0	0	no

In this .csv, they have a Outcome column showing the target of study.

target=animals['outcome']
target.head()

0          died
1    euthanized
2         lived
3          died
4          died
Name: outcome, dtype: object

target.unique()

array(['died', 'euthanized', 'lived'], dtype=object)

Let us Drop the 'outcome' from the excel.

animals = animals.drop(['outcome'],axis=1)

animals.head()

	surgery	age	hospital_number	rectal_temp	pulse	respiratory_rate	temp_of_extremities	peripheral_pulse	mucous_membrane	capillary_refill_time	pain	peristalsis	abdominal_distention	nasogastric_tube	nasogastric_reflux	nasogastric_reflux_ph	rectal_exam_feces	abdomen	packed_cell_volume	total_protein	abdomo_appearance	abdomo_protein	surgical_lesion	lesion_1	lesion_2	lesion_3	cp_data
0	no	adult	530101	38.5	66.0	28.0	cool	reduced	NaN	more_3_sec	extreme_pain	absent	severe	NaN	NaN	NaN	decreased	distend_large	45.0	8.4	NaN	NaN	no	11300	0	0	no
1	yes	adult	534817	39.2	88.0	20.0	NaN	NaN	pale_cyanotic	less_3_sec	mild_pain	absent	slight	NaN	NaN	NaN	absent	other	50.0	85.0	cloudy	2.0	no	2208	0	0	no
2	no	adult	530334	38.3	40.0	24.0	normal	normal	pale_pink	less_3_sec	mild_pain	hypomotile	none	NaN	NaN	NaN	normal	normal	33.0	6.7	NaN	NaN	no	0	0	0	yes
3	yes	young	5290409	39.1	164.0	84.0	cold	normal	dark_cyanotic	more_3_sec	depressed	absent	severe	none	less_1_liter	5.0	decreased	NaN	48.0	7.2	serosanguious	5.3	yes	2208	0	0	yes
4	no	adult	530255	37.3	104.0	35.0	NaN	NaN	dark_cyanotic	more_3_sec	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	74.0	7.4	NaN	NaN	no	4300	0	0	no

Categorize the variables into the list.

category_variables =['surgery','age','temp_of_extremities','peripheral_pulse',
                     'mucous_membrane','capillary_refill_time','pain',
                     'peristalsis','abdominal_distention','nasogastric_tube',
                     'nasogastric_reflux','rectal_exam_feces','abdomen',
                     'abdomo_appearance','abdomo_protein','surgical_lesion',
                     'lesion_1','lesion_2','lesion_3','cp_data']

for category in category_variables:
    animals[category]=pd.get_dummies(animals[category])

Seggregate animals and target seperately. Now, lets split the data

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder

X,y = animals.values, target.values

label_encoder = LabelEncoder()
y = label_encoder.fit_transform(y)
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1)

Check the data status and shape

from sklearn.tree import DecisionTreeClassifier
print(X_train.shape)

(239, 27)

from sklearn.impute import SimpleImputer
imp = SimpleImputer(missing_values=np.NaN, strategy = 'most_frequent')
imp = imp.fit(animals.iloc[:,1:])

animals.head()

	surgery	age	hospital_number	rectal_temp	pulse	respiratory_rate	temp_of_extremities	peripheral_pulse	mucous_membrane	capillary_refill_time	pain	peristalsis	abdominal_distention	nasogastric_tube	nasogastric_reflux	nasogastric_reflux_ph	rectal_exam_feces	abdomen	packed_cell_volume	total_protein	abdomo_appearance	abdomo_protein	surgical_lesion	lesion_1	lesion_2	lesion_3	cp_data
0	1	1	530101	38.5	66.0	28.0	0	0	0	0	0	1	0	0	0	NaN	0	1	45.0	8.4	0	0	1	0	1	1	1
1	0	1	534817	39.2	88.0	20.0	0	0	0	0	0	1	0	0	0	NaN	1	0	50.0	85.0	0	0	1	0	1	1	1
2	1	1	530334	38.3	40.0	24.0	0	0	0	0	0	0	0	0	0	NaN	0	0	33.0	6.7	0	0	1	1	1	1	0
3	0	0	5290409	39.1	164.0	84.0	1	0	0	0	0	1	0	1	1	5.0	0	0	48.0	7.2	0	0	0	0	1	1	0
4	1	1	530255	37.3	104.0	35.0	0	0	0	0	0	0	0	0	0	NaN	0	0	74.0	7.4	0	0	1	0	1	1	1

X_train =imp.fit_transform(X_train)
print(X_train.shape)

(239, 27)

X_test=imp.fit_transform(X_test)
print(X_test.shape)

(60, 27)

Data has been completely preprocessed and let us apply classifier alorithms. At first Decision Tree Classifier algorithm.

classifier = DecisionTreeClassifier()

classifier.fit(X_train,y_train)

DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=None, max_features=None, 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, presort='deprecated',
                       random_state=None, splitter='best')

Over the training data & testing data Predict

y_predict = classifier.predict(X_test)
print(y_predict.shape)

(60,)

Lets Check accuracy of Algorithm

from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_predict,y_test)
print(accuracy)

0.65

Lets Perform another algorithm..

from sklearn.ensemble import RandomForestClassifier
Classifier = RandomForestClassifier()

classifier.fit(X_train,y_train)
y_predict = classifier.predict(X_test)
accuracy = accuracy_score(y_predict,y_test)
print(accuracy)

0.7

From the above Random Forest Classifer is more accurate than Desicion Tree Classifier for the above model.