dermatology Data Set - امیر شکری

dermatology Data Set

github link : https://github.com/amirshnll/dermatology

dataset link : http://archive.ics.uci.edu/ml/datasets/Dermatology

# Author : Amir Shokri
# github link : https://github.com/amirshnll/dermatology
# dataset link : http://archive.ics.uci.edu/ml/datasets/Dermatology
# email : amirsh.nll@gmail.com

# Author : Amir Shokri

# github link : https://github.com/amirshnll/dermatology

# dataset link : http://archive.ics.uci.edu/ml/datasets/Dermatology

# email : amirsh.nll@gmail.com

import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
from sklearn.metrics import accuracy_score

import pandas as pd

from sklearn.preprocessing import MinMaxScaler

from sklearn.decomposition import PCA

from sklearn.model_selection import train_test_split

from sklearn.metrics import f1_score

from sklearn.metrics import accuracy_score

data = pd.read_csv('dermatology_data.csv', header=None)

1	data = pd.read_csv('dermatology_data.csv', header=None)

data = data.replace(to_replace="?", method='ffill')

1	data = data.replace(to_replace="?", method='ffill')

data.describe()

1	data.describe()

properties = data[data.columns[:34]]
target = data[data.columns[34]]
scaler = MinMaxScaler()
scaled_x = scaler.fit_transform(properties)

properties = data[data.columns[:34]]

target = data[data.columns[34]]

scaler = MinMaxScaler()

scaled_x = scaler.fit_transform(properties)

target.value_counts().plot.pie()

1	target.value_counts().plot.pie()

<matplotlib.axes._subplots.AxesSubplot at 0x23b944ca7f0>

dermatology - fig 1 — dermatology – fig 1

pca = PCA(n_components=15)
reduced_x = pca.fit_transform(scaled_x)

1 2	pca = PCA(n_components=15) reduced_x = pca.fit_transform(scaled_x)

X_train, X_test, y_train, y_test = train_test_split(reduced_x, target, test_size=0.3, random_state=0)

1	X_train, X_test, y_train, y_test = train_test_split(reduced_x, target, test_size=0.3, random_state=0)

from sklearn.naive_bayes import GaussianNB
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression

from sklearn.naive_bayes import GaussianNB

from sklearn.neural_network import MLPClassifier

from sklearn.neighbors import KNeighborsClassifier

from sklearn.tree import DecisionTreeClassifier

from sklearn.linear_model import LogisticRegression

gnb = GaussianNB()
mlp = MLPClassifier(hidden_layer_sizes=(100, 100))
knn = KNeighborsClassifier(n_neighbors=5)
dt = DecisionTreeClassifier()
regressor = LogisticRegression()

gnb = GaussianNB()

mlp = MLPClassifier(hidden_layer_sizes=(100, 100))

knn = KNeighborsClassifier(n_neighbors=5)

dt = DecisionTreeClassifier()

regressor = LogisticRegression()

gnb.fit(X_train, y_train)
y_predgnb = gnb.predict(X_test)

mlp.fit(X_train, y_train)
y_predmlp = mlp.predict(X_test)

knn.fit(X_train, y_train)
y_predknn = knn.predict(X_test)

dt.fit(X_train, y_train)
y_preddt = dt.predict(X_test)

regressor.fit(X_train, y_train)
y_predregressor = regressor.predict(X_test)

gnb.fit(X_train, y_train)

y_predgnb = gnb.predict(X_test)

mlp.fit(X_train, y_train)

y_predmlp = mlp.predict(X_test)

knn.fit(X_train, y_train)

y_predknn = knn.predict(X_test)

dt.fit(X_train, y_train)

y_preddt = dt.predict(X_test)

regressor.fit(X_train, y_train)

y_predregressor = regressor.predict(X_test)

print('gnb f1: ', f1_score(y_test, y_predgnb, average='micro'))
print('gnb accuracy: ', accuracy_score(y_test, y_predgnb))

print('mlp f1: ', f1_score(y_test, y_predmlp, average='micro'))
print('mlp accuracy: ', accuracy_score(y_test, y_predmlp))

print('knn f1: ', f1_score(y_test, y_predgnb, average='micro'))
print('knn accuracy: ', accuracy_score(y_test, y_predknn))

print('decision tree f1: ', f1_score(y_test, y_predgnb, average='micro'))
print('decision tree accuracy: ', accuracy_score(y_test, y_preddt))

print('logistic regression f1: ', f1_score(y_test, y_predgnb, average='micro'))
print('logistic regression accuracy: ', accuracy_score(y_test, y_predregressor))

print('gnb f1: ', f1_score(y_test, y_predgnb, average='micro'))

print('gnb accuracy: ', accuracy_score(y_test, y_predgnb))

print('mlp f1: ', f1_score(y_test, y_predmlp, average='micro'))

print('mlp accuracy: ', accuracy_score(y_test, y_predmlp))

print('knn f1: ', f1_score(y_test, y_predgnb, average='micro'))

print('knn accuracy: ', accuracy_score(y_test, y_predknn))

print('decision tree f1: ', f1_score(y_test, y_predgnb, average='micro'))

print('decision tree accuracy: ', accuracy_score(y_test, y_preddt))

print('logistic regression f1: ', f1_score(y_test, y_predgnb, average='micro'))

print('logistic regression accuracy: ', accuracy_score(y_test, y_predregressor))

gnb f1: 0.9727272727272728
gnb accuracy: 0.9727272727272728
mlp f1: 1.0
mlp accuracy: 1.0
knn f1: 0.9727272727272728
knn accuracy: 0.9818181818181818
decision tree f1: 0.9727272727272728
decision tree accuracy: 0.9090909090909091
logistic regression f1: 0.9727272727272728
logistic regression accuracy: 0.990909090909091

other: