鸢尾花(iris)数据集分析
Iris 鸢尾花数据集是一个经典数据集，在统计学习和机器学习领域都经常被用作示例。数据集内包含 3 类共 150 条记录，每类各 50 个数据，每条记录都有 4 项特征：花萼长度、花萼宽度、花瓣长度、花瓣宽度，可以通过这4个特征预测鸢尾花卉属于（iris-setosa, iris-versicolour, iris-virginica）中的哪一品种。
据说在现实中，这三种花的基本判别依据其实是种子（因为花瓣非常容易枯萎）。
0 准备数据
下面对 iris 进行探索性分析，首先导入相关包和数据集：
# 导入相关包 import numpy as np import pandas as pd from pandas import plotting %matplotlib inline import matplotlib.pyplot as plt plt.style.use('seaborn') import seaborn as sns sns.set_style("whitegrid") from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn import metrics from sklearn.tree import DecisionTreeClassifier # 导入数据集 iris = pd.read_csv('F:\pydata\dataset\kaggle\iris.csv', usecols=[1, 2, 3, 4, 5]) 查看数据集信息：
iris.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 150 entries, 0 to 149 Data columns (total 5 columns): SepalLengthCm 150 non-null float64 SepalWidthCm 150 non-null float64 PetalLengthCm 150 non-null float64 PetalWidthCm 150 non-null float64 Species 150 non-null object dtypes: float64(4), object(1) memory usage: 5.9+ KB 查看数据集的头 5 条记录：
iris.head()
1 探索性分析
先查看数据集各特征列的摘要统计信息：
iris.describe()
通过Violinplot 和 Pointplot，分别从数据分布和斜率，观察各特征与品种之间的关系：
# 设置颜色主题 antV = ['#1890FF', '#2FC25B', '#FACC14', '#223273', '#8543E0', '#13C2C2', '#3436c7', '#F04864'] # 绘制 Violinplot f, axes = plt.subplots(2, 2, figsize=(8, 8), sharex=True) sns.despine(left=True) sns.violinplot(x='Species', y='SepalLengthCm', data=iris, palette=antV, ax=axes[0, 0]) sns.violinplot(x='Species', y='SepalWidthCm', data=iris, palette=antV, ax=axes[0, 1]) sns.violinplot(x='Species', y='PetalLengthCm', data=iris, palette=antV, ax=axes[1, 0]) sns.violinplot(x='Species', y='PetalWidthCm', data=iris, palette=antV, ax=axes[1, 1]) plt.show() 
# 绘制 pointplot f, axes = plt.subplots(2, 2, figsize=(8, 8), sharex=True) sns.despine(left=True) sns.pointplot(x='Species', y='SepalLengthCm', data=iris, color=antV[0], ax=axes[0, 0]) sns.pointplot(x='Species', y='SepalWidthCm', data=iris, color=antV[0], ax=axes[0, 1]) sns.pointplot(x='Species', y='PetalLengthCm', data=iris, color=antV[0], ax=axes[1, 0]) sns.pointplot(x='Species', y='PetalWidthCm', data=iris, color=antV[0], ax=axes[1, 1]) plt.show() 
生成各特征之间关系的矩阵图：
g = sns.pairplot(data=iris, palette=antV, hue= 'Species') 
使用 Andrews Curves 将每个多变量观测值转换为曲线并表示傅立叶级数的系数，这对于检测时间序列数据中的异常值很有用。
Andrews Curves 是一种通过将每个观察映射到函数来可视化多维数据的方法。
plt.subplots(figsize = (10,8)) plotting.andrews_curves(iris, 'Species', colormap='cool') plt.show() 
下面分别基于花萼和花瓣做线性回归的可视化：
g = sns.lmplot(data=iris, x='SepalWidthCm', y='SepalLengthCm', palette=antV, hue='Species') 
g = sns.lmplot(data=iris, x='PetalWidthCm', y='PetalLengthCm', palette=antV, hue='Species') 
最后，通过热图找出数据集中不同特征之间的相关性，高正值或负值表明特征具有高度相关性：
fig=plt.gcf()
fig.set_size_inches(12, 8) fig=sns.heatmap(iris.corr(), annot=True, cmap='GnBu', linewidths=1, linecolor='k', square=True, mask=False, vmin=-1, vmax=1, cbar_kws={"orientation": "vertical"}, cbar=True) 
从热图可看出，花萼的宽度和长度不相关，而花瓣的宽度和长度则高度相关。
2 机器学习
接下来，通过机器学习，以花萼和花瓣的尺寸为根据，预测其品种。
在进行机器学习之前，将数据集拆分为训练和测试数据集。首先，使用标签编码将 3 种鸢尾花的品种名称转换为分类值（0, 1, 2）。
# 载入特征和标签集 X = iris[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']] y = iris['Species'] # 对标签集进行编码 encoder = LabelEncoder() y = encoder.fit_transform(y) print(y) [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2] 接着，将数据集以 7: 3 的比例，拆分为训练数据和测试数据：
train_X, test_X, train_y, test_y = train_test_split(X, y, test_size = 0.3, random_state = 101) print(train_X.shape, train_y.shape, test_X.shape, test_y.shape) (105, 4) (105,) (45, 4) (45,)
检查不同模型的准确性：
# Support Vector Machine model = svm.SVC() model.fit(train_X, train_y) prediction = model.predict(test_X) print('The accuracy of the SVM is: {0}'.format(metrics.accuracy_score(prediction,test_y))) The accuracy of the SVM is: 1.0 # Logistic Regression model = LogisticRegression() model.fit(train_X, train_y) prediction = model.predict(test_X) print('The accuracy of the Logistic Regression is: {0}'.format(metrics.accuracy_score(prediction,test_y))) The accuracy of the Logistic Regression is: 0.9555555555555556 # Decision Tree model=DecisionTreeClassifier() model.fit(train_X, train_y) prediction = model.predict(test_X) print('The accuracy of the Decision Tree is: {0}'.format(metrics.accuracy_score(prediction,test_y))) The accuracy of the Decision Tree is: 0.9555555555555556 # K-Nearest Neighbours model=KNeighborsClassifier(n_neighbors=3) model.fit(train_X, train_y) prediction = model.predict(test_X) print('The accuracy of the KNN is: {0}'.format(metrics.accuracy_score(prediction,test_y))) The accuracy of the KNN is: 1.0 上面使用了数据集的所有特征，下面将分别使用花瓣和花萼的尺寸：
petal = iris[['PetalLengthCm', 'PetalWidthCm', 'Species']] train_p,test_p=train_test_split(petal,test_size=0.3,random_state=0) train_x_p=train_p[['PetalWidthCm','PetalLengthCm']] train_y_p=train_p.Species test_x_p=test_p[['PetalWidthCm','PetalLengthCm']] test_y_p=test_p.Species sepal = iris[['SepalLengthCm', 'SepalWidthCm', 'Species']] train_s,test_s=train_test_split(sepal,test_size=0.3,random_state=0) train_x_s=train_s[['SepalWidthCm','SepalLengthCm']] train_y_s=train_s.Species test_x_s=test_s[['SepalWidthCm','SepalLengthCm']] test_y_s=test_s.Species model=svm.SVC()
model.fit(train_x_p,train_y_p)
prediction=model.predict(test_x_p)
print('The accuracy of the SVM using Petals is: {0}'.format(metrics.accuracy_score(prediction,test_y_p))) model.fit(train_x_s,train_y_s) prediction=model.predict(test_x_s) print('The accuracy of the SVM using Sepal is: {0}'.format(metrics.accuracy_score(prediction,test_y_s))) The accuracy of the SVM using Petals is: 0.9777777777777777 The accuracy of the SVM using Sepal is: 0.8 model = LogisticRegression()
model.fit(train_x_p, train_y_p)
prediction = model.predict(test_x_p)
print('The accuracy of the Logistic Regression using Petals is: {0}'.format(metrics.accuracy_score(prediction,test_y_p))) model.fit(train_x_s, train_y_s) prediction = model.predict(test_x_s) print('The accuracy of the Logistic Regression using Sepals is: {0}'.format(metrics.accuracy_score(prediction,test_y_s))) The accuracy of the Logistic Regression using Petals is: 0.6888888888888889 The accuracy of the Logistic Regression using Sepals is: 0.6444444444444445 model=DecisionTreeClassifier()
model.fit(train_x_p, train_y_p)
prediction = model.predict(test_x_p)
print('The accuracy of the Decision Tree using Petals is: {0}'.format(metrics.accuracy_score(prediction,test_y_p))) model.fit(train_x_s, train_y_s) prediction = model.predict(test_x_s) print('The accuracy of the Decision Tree using Sepals is: {0}'.format(metrics.accuracy_score(prediction,test_y_s))) The accuracy of the Decision Tree using Petals is: 0.9555555555555556 The accuracy of the Decision Tree using Sepals is: 0.6666666666666666 model=KNeighborsClassifier(n_neighbors=3) model.fit(train_x_p, train_y_p) prediction = model.predict(test_x_p) print('The accuracy of the KNN using Petals is: {0}'.format(metrics.accuracy_score(prediction,test_y_p))) model.fit(train_x_s, train_y_s) prediction = model.predict(test_x_s) print('The accuracy of the KNN using Sepals is: {0}'.format(metrics.accuracy_score(prediction,test_y_s))) The accuracy of the KNN using Petals is: 0.9777777777777777 The accuracy of the KNN using Sepals is: 0.7333333333333333 从中不难看出，使用花瓣的尺寸来训练数据较花萼更准确。正如在探索性分析的热图中所看到的那样，花萼的宽度和长度之间的相关性非常低，而花瓣的宽度和长度之间的相关性非常高。