数据挖掘之鸢尾花数据集分析

因为手上没有iris.data数据，只能通过在sklearn中加载原始数据，并将其转换为Dataframe格式

主要内容：数据分布的可视化（特征之间分布、特征内部、分类精度、热力图） 

算法：决策树 随机森林

import pandas as pd

from sklearn.datasets import load_iris

import numpy as np

iris = load_iris()

X, y = iris.data, iris.target

iris_data = pd.DataFrame(np.hstack((X, y.reshape(-1, 1))),index = range(X.shape[0]),columns=['sepal_length_cm','sepal_width_cm','petal_length_cm','petal_width_cm','class'] )

import matplotlib.pyplot as plt

print(iris_data.describe())

import seaborn as sb

sb.pairplot(iris_data.dropna(),hue = 'class')

plt.figure(figsize=(10,10))

for column_index,column in enumerate(iris_data.columns):

if column == 'class':

continue

plt.subplot(2,2,column_index+1)

sb.violinplot(x= 'class', y=column, data = iris_data)

plt.show()

输出：

sepal_length_cm sepal_width_cm petal_length_cm petal_width_cm

count 150.000000 150.000000 150.000000 150.000000

mean 5.843333 3.054000 3.758667 1.198667

std 0.828066 0.433594 1.764420 0.763161

min 4.300000 2.000000 1.000000 0.100000

25% 5.100000 2.800000 1.600000 0.300000

50% 5.800000 3.000000 4.350000