数据挖掘或机器学习在商业智能领域的案例在Kaggle 很多，我今天分享一下红酒数据。

按照CRISP模型第一步去了解业务，也就是数据集描述的业务场景，如果不理解这一点，那么数据分析的意义又在哪儿呢，就比如你做出了一个数学题，可是更现实没什么用一样。道理就是这样的。

好了，首先看看数据集各个变量的意思：

1 - fixed acidity: most acids involved with wine or fixed or nonvolatile (do not evaporate readily)

1-固定酸性：这个概念是指大多数红酒酒类的酸性都是固定或不变化的(基本不会挥发)

2 - volatile acidity: the amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste

2-可挥发性酸性：红酒醋酸的含量，含量水平过高会导致出现口感不悦的醋酸味

3 - citric acid: found in small quantities, citric acid can add 'freshness' and flavor to wines

3 - 柠檬酸：少量的柠檬酸可以增加葡萄酒“新鲜”的口感

4 - residual sugar: the amount of sugar remaining after fermentation

stops, it's rare to find wines with less than 1 gram/liter and wines

with greater than 45 grams/liter are considered sweet

4 - 残糖：发酵停止后剩余的糖量，很少发现葡萄酒少于1克/升，葡萄酒

内大于45克/升被认为是甜的口感

5 - chlorides: the amount of salt in the wine

5 - 氯化物：酒中的盐量

6 - free sulfur dioxide: the free form of SO2 exists in equilibrium

between molecular SO2 (as a dissolved gas) and bisulfite ion; it

prevents microbial growth and the oxidation of wine

6 - 游离二氧化硫：游离态二氧化硫存在

在分子SO2(作为溶解气体)和亚硫酸氢根离子之间的平衡状态;它可以

防止微生物生长和葡萄酒的氧化

7 - total sulfur dioxide: amount of free and bound forms of S02; in

low concentrations, SO2 is mostly undetectable in wine, but at free SO2

concentrations over 50 ppm, SO2 becomes evident in the nose and taste of

wine

7 - 总二氧化硫：游离和结合形式的SO 2的量;低浓度SO2在葡萄酒中几乎检测不到，但在游离SO2中浓度超过50 ppm时，二氧化硫闻起来和品尝都比较明显了

8 - density: the density of water is close to that of water depending on the percent alcohol and sugar content

8 - 密度：水的密度接近水的密度，取决于酒精和糖的百分比

9 - pH: describes how acidic or basic a wine is on a scale from 0

(very acidic) to 14 (very basic); most wines are between 3-4 on the pH

scale

9 - ：描述葡萄酒的酸度或碱度是从0开始的

(非常酸性)到14(非常基本的);大多数葡萄酒的酸碱度pH值在3-4之间

10 - sulphates: a wine additive which can contribute to sulfur

dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant

10 - 硫酸盐：可以促成硫的葡萄酒添加剂，会增加二氧化硫气体(SO 2)的水平，即抗微生物剂和抗氧化剂

11 - alcohol: the percent alcohol content of the wine Output variable (based on sensory data):

11 - 酒精：酒的百分比酒精含量输出变量(基于感官数据)：

12 - quality (score between 0 and 10)

12 - 品质(0到10分)

该数据来源：

P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.

Modeling wine preferences by data mining from physicochemical properties.

In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.

根据该数据的特点，我们来用python 做kMeans 分析，

第一步 加载要用的5个库numpy, pandas, matplotlib.pyplot, ggplot, KMeans(sklearn.cluster)，类似R的5个包

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

plt.style.use('ggplot')

from sklearn.cluster import KMeans

导入要分析的数据集：

wine = pd.read_csv('data/wine.csv', header=0)

wine_cluster = pd.read_csv('data/wine.csv', header=0)

查看数据情况

wine.head()

Wine Alcohol Malic acid Ash Alcalinity of ash Magnesium Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins Color intensity Hue OD280/OD315 of diluted wines Proline 0 1 14.23 1.71 2.43 15.6 127 2.80 3.06 0.28 2.29 5.64 1.04 3.92 1065 1 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.40 1050 2 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03 3.17 1185 3 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86 3.45 1480 4 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.32 1.04 2.93 735

稍后再补充扩展。。。。