Predict the flower class based on available attributes.
from IPython.display import IFrame
# Flower Classification
IFrame('http://www.spataru.at/iris-dataset-svm/iris_types.jpg', width=1280, height=300)
# Import Libaries
import pandas as pd
import numpy as np
# Import Data
data_location = "/Users/wmemorgan/Google Drive/Computer_Data_Science_Lab/iris_data_modeling/data/02_prepared_data/iris.data"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
data = pd.read_csv(data_location, names=names)
data.head()
sepal-length | sepal-width | petal-length | petal-width | class | |
---|---|---|---|---|---|
0 | 5.1 | 3.5 | 1.4 | 0.2 | Iris-setosa |
1 | 4.9 | 3.0 | 1.4 | 0.2 | Iris-setosa |
2 | 4.7 | 3.2 | 1.3 | 0.2 | Iris-setosa |
3 | 4.6 | 3.1 | 1.5 | 0.2 | Iris-setosa |
4 | 5.0 | 3.6 | 1.4 | 0.2 | Iris-setosa |
print(data.groupby('class').size())
class Iris-setosa 50 Iris-versicolor 50 Iris-virginica 50 dtype: int64
# Import Libaries
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
f, axes = plt.subplots(2, 2, figsize=(12,8)) #More than one dimension
bp1 = sns.boxplot(data=data, x="class", y="sepal-length", ax=axes[0,0])
bp2 = sns.boxplot(data=data, x="class", y="sepal-width", ax=axes[0,1])
bp3 = sns.boxplot(data=data, x="class", y="petal-length", ax=axes[1,0])
bp4 = sns.boxplot(data=data, x="class", y="petal-width", ax=axes[1,1])
# Test options and evaluation metric
seed = 7
scoring = 'accuracy'
# Import library
from sklearn.model_selection import train_test_split, KFold, cross_val_score
# Assign features and output
X = data.drop('class',axis=1)
y = data['class']
test_size = 0.2
seed = 7
# Create training and test datasets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size, random_state=seed)
# Import Algorithm Libraries
from sklearn.linear_model import LogisticRegression
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.model_selection import KFold, cross_val_score
# Spot Check Algorithms
models = []
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC()))
# evaluate each model in turn
results = []
names = []
for name, model in models:
kfold = KFold(n_splits=10, random_state=seed)
cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)
results.append(cv_results)
names.append(name)
msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
print(msg)
LR: 0.966667 (0.040825) LDA: 0.975000 (0.038188) KNN: 0.983333 (0.033333) CART: 0.975000 (0.038188) NB: 0.975000 (0.053359) SVM: 0.991667 (0.025000)
# Compare Algorithms
fig = plt.figure()
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()
# Fitting the classifier to the Training set
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
predictions = knn.predict(X_test)
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
print('Accuracy Score:')
print(accuracy_score(y_test,predictions))
print('Confusion Matrix:')
print(confusion_matrix(y_test,predictions))
print('Classification Report:')
print(classification_report(y_test,predictions))
Accuracy Score: 0.9 Confusion Matrix: [[ 7 0 0] [ 0 11 1] [ 0 2 9]] Classification Report: precision recall f1-score support Iris-setosa 1.00 1.00 1.00 7 Iris-versicolor 0.85 0.92 0.88 12 Iris-virginica 0.90 0.82 0.86 11 avg / total 0.90 0.90 0.90 30