机器学习：葡萄酒质量预测模型教程
stevenkwong 2018-08-27
本文介绍如何利用机器学习模型根据各种特征预测葡萄酒质量。从这里下载分析数据集。
葡萄酒数据集包含以下特征：
Input variables (based on physicochemical tests):
fixed acidity, volatile acidity, citric acid, residual sugar,
chlorides,free sulfur dioxide,total sulfur dioxide,density,
pH,sulphates, alcohol
Output variables:
quality (score between 0 and 10)
首先通过导入所需的Python库并加载白葡萄酒和红葡萄酒的csv文件来加载两个数据集。
#import the libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# load the files
df_red = pd.read_csv(“winequality-red.csv”, sep=”;”)
df_white = pd.read_csv(“winequality-white.csv”, sep=”;”)
将这两个dataframes 合并起来分析。Python代码如下：
df = pd.concat([df_red, df_white], axis=0)
检查是否有任何空列
df.isnull().sum()
fixed acidity 0
volatile acidity 0
citric acid 0
residual sugar 0
chlorides 0
free sulfur dioxide 0
total sulfur dioxide 0
density 0
pH 0
sulphates 0
alcohol 0
quality 0
找出输出(质量)变量与所有输入变量之间的相关性，Python实现如下：
# identify the correlation
plt.subplots(figsize=(20,15))
corr = df.corr()
sns.heatmap(corr,square=True, annot=True)
一些如酒精，柠檬酸，游离二氧化硫，pH值呈正相关，质量会有所改善，而密度，残糖和酸度会对质量产生负面影响。
让我们确定前6个相关特征。Python代码如下：
# pick the top 6 highly correlating columns
cols = corr.nlargest(6, ‘quality’)[‘quality’].index
corrcoef = np.corrcoef(df[cols].values.T)
# correlation plotted against the top columns
plt.subplots(figsize=(20,15))
corr = df.corr()
sns.heatmap(corrcoef,square=True, annot=True, xticklabels= cols.values, yticklabels=cols.values)
通过绘制直方图来分析数据的分布
使用机器学习中的sklearn库，将数据集拆分为测试和训练数据集，我使用了20％的数据作为测试数据集。Python代码如下：
y = df[“quality”]
X = df.drop(“quality”, axis=1)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
由于不同的列具有不同的值，因此您需要归一化值以获得准确的预测结果。我在这里使用StandardScaler库。您也可以使用MinMaxScaler方法。
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.fit_transform(X_test)
现在，我将根据各种算法拟合我的训练数据，并根据测试值确定预测输出的准确性。Python实现如下：
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
logreg.fit(X_train, y_train)
pred_logreg = logreg.predict(X_test)
accuracy = accuracy_score(pred_logreg, y_test)
print("Logreg Accuracy Score %.2f" % accuracy)
cm = confusion_matrix(pred_logreg, y_test)
knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)
pred_knn = knn.predict(X_test)
accuracy = accuracy_score(pred_knn, y_test)
print("Knn Accuracy Score %.2f" % accuracy)
from sklearn.svm import SVC
svc = SVC()
svc.fit(X_train, y_train)
pred_svc =svc.predict(X_test)
accuracy = accuracy_score(pred_svc, y_test)
print("SVC Accuracy Score %.2f" % accuracy)
dtree = DecisionTreeClassifier()
dtree.fit(X_train, y_train)
pred_tree =dtree.predict(X_test)
accuracy = accuracy_score(pred_tree, y_test)
print("DTree Accuracy Score %.2f" % accuracy)
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
pred_rf =rf.predict(X_test)
accuracy = accuracy_score(pred_rf, y_test)
print("Random Forest Accuracy Score %.2f" % accuracy)
我尝试了各种算法，包括Logistic回归，决策树，随机森林，KNN和SVC。
随机森林为我提供更好的准确性（64％）
Logreg Accuracy Score 0.53
Knn Accuracy Score 0.62
SVC Accuracy Score 0.57
DTree Accuracy Score 0.55
Random Forest Accuracy Score 0.64
将前10条记录的测试数据与预测数据进行比较，结果表明，其中有2条记录的质量预测与测试结果不同
