数据分析（业务向）技能总结

头文件

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import numpy as np  # 数学
import pandas as pd
from sklearn import datasets  #用数据库去学习，或者把数据库放到tenserflow模块练习
from sklearn.model_selection import train_test_split # 数据集测试集分离
from sklearn.neighbors import KNeighborsClassifier   # 会选择邻近几个点作为他的邻居，综合临近几个点模拟出数据的预测值
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import warnings 
warnings.filterwarnings("ignore")
import seaborn as sns
import os
%matplotlib inline

<–!more–>

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# First, we'll import pandas, a data processing and CSV file I/O library
import pandas as pd

# We'll also import seaborn, a Python graphing library
import warnings # current version of seaborn generates a bunch of warnings that we'll ignore
warnings.filterwarnings("ignore")
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white", color_codes=True)

# Next, we'll load the Iris flower dataset, which is in the "../input/" directory
iris = pd.read_csv("../input/Iris.csv") # the iris dataset is now a Pandas DataFrame

# Let's see what's in the iris data - Jupyter notebooks print the result of the last thing you do
iris.head()

# Press shift+enter to execute this cell

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