关于算法:机器学习实战系列一工业蒸汽量预测最新版本上篇含数据探索特征工程等

机器学习实战系列[一]：工业蒸汽量预测

• 背景介绍

火力发电的基本原理是：燃料在焚烧时加热水生成蒸汽，蒸汽压力推动汽轮机旋转，而后汽轮机带动发电机旋转，产生电能。在这一系列的能量转化中，影响发电效率的外围是锅炉的焚烧效率，即燃料焚烧加热水产生高温高压蒸汽。锅炉的焚烧效率的影响因素很多，包含锅炉的可调参数，如焚烧给量，一二次风，引风，返料风，给水水量；以及锅炉的工况，比方锅炉床温、床压，炉膛温度、压力，过热器的温度等。

• 相干形容

经脱敏后的锅炉传感器采集的数据（采集频率是分钟级别），依据锅炉的工况，预测产生的蒸汽量。

• 数据阐明

数据分成训练数据（train.txt）和测试数据（test.txt），其中字段”V0”-“V37”，这38个字段是作为特色变量，”target”作为指标变量。选手利用训练数据训练出模型，预测测试数据的指标变量，排名后果根据预测后果的MSE（mean square error）。

• 后果评估

预测后果以mean square error作为评判规范。

原我的项目链接：https://www.heywhale.com/home/column/64141d6b1c8c8b518ba97dcc

1.数据探索性剖析

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsfrom scipy import statsimport warningswarnings.filterwarnings("ignore") %matplotlib inline

# 下载须要用到的数据集!wget http://tianchi-media.oss-cn-beijing.aliyuncs.com/DSW/Industrial_Steam_Forecast/zhengqi_test.txt!wget http://tianchi-media.oss-cn-beijing.aliyuncs.com/DSW/Industrial_Steam_Forecast/zhengqi_train.txt

--2023-03-23 18:10:23--  http://tianchi-media.oss-cn-beijing.aliyuncs.com/DSW/Industrial_Steam_Forecast/zhengqi_test.txt正在解析主机 tianchi-media.oss-cn-beijing.aliyuncs.com (tianchi-media.oss-cn-beijing.aliyuncs.com)... 49.7.22.39正在连接 tianchi-media.oss-cn-beijing.aliyuncs.com (tianchi-media.oss-cn-beijing.aliyuncs.com)|49.7.22.39|:80... 已连贯。已收回 HTTP 申请，正在期待回应... 200 OK长度： 466959 (456K) [text/plain]正在保留至: “zhengqi_test.txt.1”zhengqi_test.txt.1  100%[===================>] 456.01K  --.-KB/s    in 0.04s   2023-03-23 18:10:23 (10.0 MB/s) - 已保留 “zhengqi_test.txt.1” [466959/466959])--2023-03-23 18:10:23--  http://tianchi-media.oss-cn-beijing.aliyuncs.com/DSW/Industrial_Steam_Forecast/zhengqi_train.txt正在解析主机 tianchi-media.oss-cn-beijing.aliyuncs.com (tianchi-media.oss-cn-beijing.aliyuncs.com)... 49.7.22.39正在连接 tianchi-media.oss-cn-beijing.aliyuncs.com (tianchi-media.oss-cn-beijing.aliyuncs.com)|49.7.22.39|:80... 已连贯。已收回 HTTP 申请，正在期待回应... 200 OK长度： 714370 (698K) [text/plain]正在保留至: “zhengqi_train.txt.1”zhengqi_train.txt.1 100%[===================>] 697.63K  --.-KB/s    in 0.04s   2023-03-23 18:10:24 (17.9 MB/s) - 已保留 “zhengqi_train.txt.1” [714370/714370])

# **读取数据文件**# 应用Pandas库`read_csv()`函数进行数据读取，宰割符为‘\t’train_data_file = "./zhengqi_train.txt"test_data_file =  "./zhengqi_test.txt"train_data = pd.read_csv(train_data_file, sep='\t', encoding='utf-8')test_data = pd.read_csv(test_data_file, sep='\t', encoding='utf-8')

1.1 查看数据信息

#查看特色变量信息train_data.info()

<class 'pandas.core.frame.DataFrame'>RangeIndex: 2888 entries, 0 to 2887Data columns (total 39 columns): #   Column  Non-Null Count  Dtype  ---  ------  --------------  -----   0   V0      2888 non-null   float64 1   V1      2888 non-null   float64 2   V2      2888 non-null   float64 3   V3      2888 non-null   float64 4   V4      2888 non-null   float64 5   V5      2888 non-null   float64 6   V6      2888 non-null   float64 7   V7      2888 non-null   float64 8   V8      2888 non-null   float64 9   V9      2888 non-null   float64 10  V10     2888 non-null   float64 11  V11     2888 non-null   float64 12  V12     2888 non-null   float64 13  V13     2888 non-null   float64 14  V14     2888 non-null   float64 15  V15     2888 non-null   float64 16  V16     2888 non-null   float64 17  V17     2888 non-null   float64 18  V18     2888 non-null   float64 19  V19     2888 non-null   float64 20  V20     2888 non-null   float64 21  V21     2888 non-null   float64 22  V22     2888 non-null   float64 23  V23     2888 non-null   float64 24  V24     2888 non-null   float64 25  V25     2888 non-null   float64 26  V26     2888 non-null   float64 27  V27     2888 non-null   float64 28  V28     2888 non-null   float64 29  V29     2888 non-null   float64 30  V30     2888 non-null   float64 31  V31     2888 non-null   float64 32  V32     2888 non-null   float64 33  V33     2888 non-null   float64 34  V34     2888 non-null   float64 35  V35     2888 non-null   float64 36  V36     2888 non-null   float64 37  V37     2888 non-null   float64 38  target  2888 non-null   float64dtypes: float64(39)memory usage: 880.1 KB

此训练集数据共有2888个样本，数据中有V0-V37共计38个特色变量，变量类型都为数值类型，所有数据特色没有缺失值数据；
数据字段因为采纳了脱敏解决，删除了特色数据的具体含意；target字段为标签变量

test_data.info()

<class 'pandas.core.frame.DataFrame'>RangeIndex: 1925 entries, 0 to 1924Data columns (total 38 columns): #   Column  Non-Null Count  Dtype  ---  ------  --------------  -----   0   V0      1925 non-null   float64 1   V1      1925 non-null   float64 2   V2      1925 non-null   float64 3   V3      1925 non-null   float64 4   V4      1925 non-null   float64 5   V5      1925 non-null   float64 6   V6      1925 non-null   float64 7   V7      1925 non-null   float64 8   V8      1925 non-null   float64 9   V9      1925 non-null   float64 10  V10     1925 non-null   float64 11  V11     1925 non-null   float64 12  V12     1925 non-null   float64 13  V13     1925 non-null   float64 14  V14     1925 non-null   float64 15  V15     1925 non-null   float64 16  V16     1925 non-null   float64 17  V17     1925 non-null   float64 18  V18     1925 non-null   float64 19  V19     1925 non-null   float64 20  V20     1925 non-null   float64 21  V21     1925 non-null   float64 22  V22     1925 non-null   float64 23  V23     1925 non-null   float64 24  V24     1925 non-null   float64 25  V25     1925 non-null   float64 26  V26     1925 non-null   float64 27  V27     1925 non-null   float64 28  V28     1925 non-null   float64 29  V29     1925 non-null   float64 30  V30     1925 non-null   float64 31  V31     1925 non-null   float64 32  V32     1925 non-null   float64 33  V33     1925 non-null   float64 34  V34     1925 non-null   float64 35  V35     1925 non-null   float64 36  V36     1925 non-null   float64 37  V37     1925 non-null   float64dtypes: float64(38)memory usage: 571.6 KB

测试集数据共有1925个样本，数据中有V0-V37共计38个特色变量，变量类型都为数值类型

# 查看数据统计信息train_data.describe()

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test_data.describe()

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下面数据显示了数据的统计信息，例如样本数，数据的均值mean，标准差std，最小值，最大值等

# 查看数据字段信息train_data.head()

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下面显示训练集前5条数据的根本信息，能够看到数据都是浮点型数据，数据都是数值型连续型特色

test_data.head()

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1.2 可视化摸索数据

fig = plt.figure(figsize=(4, 6))  # 指定绘图对象宽度和高度sns.boxplot(train_data['V0'],orient="v", width=0.5)

<matplotlib.axes._subplots.AxesSubplot at 0x7faf89f46950>

# 画箱式图# column = train_data.columns.tolist()[:39]  # 列表头# fig = plt.figure(figsize=(20, 40))  # 指定绘图对象宽度和高度# for i in range(38):#     plt.subplot(13, 3, i + 1)  # 13行3列子图#     sns.boxplot(train_data[column[i]], orient="v", width=0.5)  # 箱式图#     plt.ylabel(column[i], fontsize=8)# plt.show()#箱图自行关上

查看数据分布图

• 查看特色变量‘V0’的数据分布直方图，并绘制Q-Q图查看数据是否近似于正态分布

plt.figure(figsize=(10,5))ax=plt.subplot(1,2,1)sns.distplot(train_data['V0'],fit=stats.norm)ax=plt.subplot(1,2,2)res = stats.probplot(train_data['V0'], plot=plt)

查看查看所有数据的直方图和Q-Q图，查看训练集的数据是否近似于正态分布

# train_cols = 6# train_rows = len(train_data.columns)# plt.figure(figsize=(4*train_cols,4*train_rows))# i=0# for col in train_data.columns:#     i+=1#     ax=plt.subplot(train_rows,train_cols,i)#     sns.distplot(train_data[col],fit=stats.norm)    #     i+=1#     ax=plt.subplot(train_rows,train_cols,i)#     res = stats.probplot(train_data[col], plot=plt)# plt.show()#QQ图自行关上

由下面的数据分布图信息能够看出，很多特色变量（如'V1','V9','V24','V28'等）的数据分布不是正态的，数据并不追随对角线，后续能够应用数据变换对数据进行转换。

比照同一特色变量‘V0’下，训练集数据和测试集数据的散布状况，查看数据分布是否统一

ax = sns.kdeplot(train_data['V0'], color="Red", shade=True)ax = sns.kdeplot(test_data['V0'], color="Blue", shade=True)ax.set_xlabel('V0')ax.set_ylabel("Frequency")ax = ax.legend(["train","test"])

查看所有特色变量下，训练集数据和测试集数据的散布状况，剖析并寻找出数据分布不统一的特色变量。

# dist_cols = 6# dist_rows = len(test_data.columns)# plt.figure(figsize=(4*dist_cols,4*dist_rows))# i=1# for col in test_data.columns:#     ax=plt.subplot(dist_rows,dist_cols,i)#     ax = sns.kdeplot(train_data[col], color="Red", shade=True)#     ax = sns.kdeplot(test_data[col], color="Blue", shade=True)#     ax.set_xlabel(col)#     ax.set_ylabel("Frequency")#     ax = ax.legend(["train","test"])    #     i+=1# plt.show()#自行关上

查看特色'V5', 'V17', 'V28', 'V22', 'V11', 'V9'数据的数据分布

drop_col = 6drop_row = 1plt.figure(figsize=(5*drop_col,5*drop_row))i=1for col in ["V5","V9","V11","V17","V22","V28"]:    ax =plt.subplot(drop_row,drop_col,i)    ax = sns.kdeplot(train_data[col], color="Red", shade=True)    ax = sns.kdeplot(test_data[col], color="Blue", shade=True)    ax.set_xlabel(col)    ax.set_ylabel("Frequency")    ax = ax.legend(["train","test"])        i+=1plt.show()

由上图的数据分布能够看到特色'V5','V9','V11','V17','V22','V28' 训练集数据与测试集数据分布不统一，会导致模型泛化能力差，采纳删除此类特色办法。

drop_columns = ['V5','V9','V11','V17','V22','V28']# 合并训练集和测试集数据，并可视化训练集和测试集数据特色分布图

可视化线性回归关系

• 查看特色变量‘V0’与'target'变量的线性回归关系

fcols = 2frows = 1plt.figure(figsize=(8,4))ax=plt.subplot(1,2,1)sns.regplot(x='V0', y='target', data=train_data, ax=ax,             scatter_kws={'marker':'.','s':3,'alpha':0.3},            line_kws={'color':'k'});plt.xlabel('V0')plt.ylabel('target')ax=plt.subplot(1,2,2)sns.distplot(train_data['V0'].dropna())plt.xlabel('V0')plt.show()

1.2.2 查看变量间线性回归关系

# fcols = 6# frows = len(test_data.columns)# plt.figure(figsize=(5*fcols,4*frows))# i=0# for col in test_data.columns:#     i+=1#     ax=plt.subplot(frows,fcols,i)#     sns.regplot(x=col, y='target', data=train_data, ax=ax, #                 scatter_kws={'marker':'.','s':3,'alpha':0.3},#                 line_kws={'color':'k'});#     plt.xlabel(col)#     plt.ylabel('target')    #     i+=1#     ax=plt.subplot(frows,fcols,i)#     sns.distplot(train_data[col].dropna())    # plt.xlabel(col)    #已正文图片生成，自行关上

1.2.2 查看特色变量的相关性

data_train1 = train_data.drop(['V5','V9','V11','V17','V22','V28'],axis=1)train_corr = data_train1.corr()train_corr

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# 画出相关性热力求ax = plt.subplots(figsize=(20, 16))#调整画布大小ax = sns.heatmap(train_corr, vmax=.8, square=True, annot=True)#画热力求   annot=True 显示系数

# 找出相干水平data_train1 = train_data.drop(['V5','V9','V11','V17','V22','V28'],axis=1)plt.figure(figsize=(20, 16))  # 指定绘图对象宽度和高度colnm = data_train1.columns.tolist()  # 列表头mcorr = data_train1[colnm].corr(method="spearman")  # 相关系数矩阵，即给出了任意两个变量之间的相关系数mask = np.zeros_like(mcorr, dtype=np.bool)  # 结构与mcorr同维数矩阵 为bool型mask[np.triu_indices_from(mask)] = True  # 角分线右侧为Truecmap = sns.diverging_palette(220, 10, as_cmap=True)  # 返回matplotlib colormap对象g = sns.heatmap(mcorr, mask=mask, cmap=cmap, square=True, annot=True, fmt='0.2f')  # 热力求（看两两类似度）plt.show()

上图为所有特色变量和target变量两两之间的相关系数，由此能够看出各个特色变量V0-V37之间的相关性以及特色变量V0-V37与target的相关性。

1.2.3 查找重要变量

查找出特色变量和target变量相关系数大于0.5的特色变量

#寻找K个最相干的特色信息k = 10 # number of variables for heatmapcols = train_corr.nlargest(k, 'target')['target'].indexcm = np.corrcoef(train_data[cols].values.T)hm = plt.subplots(figsize=(10, 10))#调整画布大小#hm = sns.heatmap(cm, cbar=True, annot=True, square=True)#g = sns.heatmap(train_data[cols].corr(),annot=True,square=True,cmap="RdYlGn")hm = sns.heatmap(train_data[cols].corr(),annot=True,square=True)plt.show()

threshold = 0.5corrmat = train_data.corr()top_corr_features = corrmat.index[abs(corrmat["target"])>threshold]plt.figure(figsize=(10,10))g = sns.heatmap(train_data[top_corr_features].corr(),annot=True,cmap="RdYlGn")

drop_columns.clear()drop_columns = ['V5','V9','V11','V17','V22','V28']

# Threshold for removing correlated variablesthreshold = 0.5# Absolute value correlation matrixcorr_matrix = data_train1.corr().abs()drop_col=corr_matrix[corr_matrix["target"]<threshold].index#data_all.drop(drop_col, axis=1, inplace=True)

因为'V14', 'V21', 'V25', 'V26', 'V32', 'V33', 'V34'特色的相关系数值小于0.5，故认为这些特色与最终的预测target值不相干，删除这些特色变量；

#merge train_set and test_settrain_x =  train_data.drop(['target'], axis=1)#data_all=pd.concat([train_data,test_data],axis=0,ignore_index=True)data_all = pd.concat([train_x,test_data]) data_all.drop(drop_columns,axis=1,inplace=True)#View datadata_all.head()

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# normalise numeric columnscols_numeric=list(data_all.columns)def scale_minmax(col):    return (col-col.min())/(col.max()-col.min())data_all[cols_numeric] = data_all[cols_numeric].apply(scale_minmax,axis=0)data_all[cols_numeric].describe()

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#col_data_process = cols_numeric.append('target')train_data_process = train_data[cols_numeric]train_data_process = train_data_process[cols_numeric].apply(scale_minmax,axis=0)test_data_process = test_data[cols_numeric]test_data_process = test_data_process[cols_numeric].apply(scale_minmax,axis=0)

cols_numeric_left = cols_numeric[0:13]cols_numeric_right = cols_numeric[13:]

## Check effect of Box-Cox transforms on distributions of continuous variablestrain_data_process = pd.concat([train_data_process, train_data['target']], axis=1)fcols = 6frows = len(cols_numeric_left)plt.figure(figsize=(4*fcols,4*frows))i=0for var in cols_numeric_left:    dat = train_data_process[[var, 'target']].dropna()            i+=1    plt.subplot(frows,fcols,i)    sns.distplot(dat[var] , fit=stats.norm);    plt.title(var+' Original')    plt.xlabel('')            i+=1    plt.subplot(frows,fcols,i)    _=stats.probplot(dat[var], plot=plt)    plt.title('skew='+'{:.4f}'.format(stats.skew(dat[var])))    plt.xlabel('')    plt.ylabel('')            i+=1    plt.subplot(frows,fcols,i)    plt.plot(dat[var], dat['target'],'.',alpha=0.5)    plt.title('corr='+'{:.2f}'.format(np.corrcoef(dat[var], dat['target'])[0][1]))     i+=1    plt.subplot(frows,fcols,i)    trans_var, lambda_var = stats.boxcox(dat[var].dropna()+1)    trans_var = scale_minmax(trans_var)          sns.distplot(trans_var , fit=stats.norm);    plt.title(var+' Tramsformed')    plt.xlabel('')            i+=1    plt.subplot(frows,fcols,i)    _=stats.probplot(trans_var, plot=plt)    plt.title('skew='+'{:.4f}'.format(stats.skew(trans_var)))    plt.xlabel('')    plt.ylabel('')            i+=1    plt.subplot(frows,fcols,i)    plt.plot(trans_var, dat['target'],'.',alpha=0.5)    plt.title('corr='+'{:.2f}'.format(np.corrcoef(trans_var,dat['target'])[0][1]))

# ## Check effect of Box-Cox transforms on distributions of continuous variables #已正文图片生成，自行关上# fcols = 6# frows = len(cols_numeric_right)# plt.figure(figsize=(4*fcols,4*frows))# i=0# for var in cols_numeric_right:#     dat = train_data_process[[var, 'target']].dropna()        #     i+=1#     plt.subplot(frows,fcols,i)#     sns.distplot(dat[var] , fit=stats.norm);#     plt.title(var+' Original')#     plt.xlabel('')        #     i+=1#     plt.subplot(frows,fcols,i)#     _=stats.probplot(dat[var], plot=plt)#     plt.title('skew='+'{:.4f}'.format(stats.skew(dat[var])))#     plt.xlabel('')#     plt.ylabel('')        #     i+=1#     plt.subplot(frows,fcols,i)#     plt.plot(dat[var], dat['target'],'.',alpha=0.5)#     plt.title('corr='+'{:.2f}'.format(np.corrcoef(dat[var], dat['target'])[0][1])) #     i+=1#     plt.subplot(frows,fcols,i)#     trans_var, lambda_var = stats.boxcox(dat[var].dropna()+1)#     trans_var = scale_minmax(trans_var)      #     sns.distplot(trans_var , fit=stats.norm);#     plt.title(var+' Tramsformed')#     plt.xlabel('')        #     i+=1#     plt.subplot(frows,fcols,i)#     _=stats.probplot(trans_var, plot=plt)#     plt.title('skew='+'{:.4f}'.format(stats.skew(trans_var)))#     plt.xlabel('')#     plt.ylabel('')        #     i+=1#     plt.subplot(frows,fcols,i)#     plt.plot(trans_var, dat['target'],'.',alpha=0.5)#     plt.title('corr='+'{:.2f}'.format(np.corrcoef(trans_var,dat['target'])[0][1]))

2.数据特色工程

2.1数据预处理和特色解决

# 导入数据分析工具包import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsfrom scipy import statsimport warningswarnings.filterwarnings("ignore") %matplotlib inline# 读取数据train_data_file = "./zhengqi_train.txt"test_data_file =  "./zhengqi_test.txt"train_data = pd.read_csv(train_data_file, sep='\t', encoding='utf-8')test_data = pd.read_csv(test_data_file, sep='\t', encoding='utf-8')

train_data.describe()#数据总览

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2.1.1 异样值剖析

#异样值剖析plt.figure(figsize=(18, 10))plt.boxplot(x=train_data.values,labels=train_data.columns)plt.hlines([-7.5, 7.5], 0, 40, colors='r')plt.show()

## 删除异常值train_data = train_data[train_data['V9']>-7.5]train_data.describe()

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test_data.describe()

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2.1.2 归一化解决

from sklearn import preprocessing features_columns = [col for col in train_data.columns if col not in ['target']]min_max_scaler = preprocessing.MinMaxScaler()min_max_scaler = min_max_scaler.fit(train_data[features_columns])train_data_scaler = min_max_scaler.transform(train_data[features_columns])test_data_scaler = min_max_scaler.transform(test_data[features_columns])train_data_scaler = pd.DataFrame(train_data_scaler)train_data_scaler.columns = features_columnstest_data_scaler = pd.DataFrame(test_data_scaler)test_data_scaler.columns = features_columnstrain_data_scaler['target'] = train_data['target']

train_data_scaler.describe()test_data_scaler.describe()

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#查看数据集状况dist_cols = 6dist_rows = len(test_data_scaler.columns)plt.figure(figsize=(4*dist_cols,4*dist_rows))for i, col in enumerate(test_data_scaler.columns):    ax=plt.subplot(dist_rows,dist_cols,i+1)    ax = sns.kdeplot(train_data_scaler[col], color="Red", shade=True)    ax = sns.kdeplot(test_data_scaler[col], color="Blue", shade=True)    ax.set_xlabel(col)    ax.set_ylabel("Frequency")    ax = ax.legend(["train","test"]) # plt.show() #已正文图片生成，自行关上

查看特色'V5', 'V17', 'V28', 'V22', 'V11', 'V9'数据的数据分布

drop_col = 6drop_row = 1plt.figure(figsize=(5*drop_col,5*drop_row))for i, col in enumerate(["V5","V9","V11","V17","V22","V28"]):    ax =plt.subplot(drop_row,drop_col,i+1)    ax = sns.kdeplot(train_data_scaler[col], color="Red", shade=True)    ax= sns.kdeplot(test_data_scaler[col], color="Blue", shade=True)    ax.set_xlabel(col)    ax.set_ylabel("Frequency")    ax = ax.legend(["train","test"])plt.show()

这几个特色下，训练集的数据和测试集的数据分布不统一，会影响模型的泛化能力，故删除这些特色

3.1.3 特色相关性

plt.figure(figsize=(20, 16))  column = train_data_scaler.columns.tolist()  mcorr = train_data_scaler[column].corr(method="spearman")  mask = np.zeros_like(mcorr, dtype=np.bool)  mask[np.triu_indices_from(mask)] = True  cmap = sns.diverging_palette(220, 10, as_cmap=True)  g = sns.heatmap(mcorr, mask=mask, cmap=cmap, square=True, annot=True, fmt='0.2f')  plt.show()

2.2 特色降维

mcorr=mcorr.abs()numerical_corr=mcorr[mcorr['target']>0.1]['target']print(numerical_corr.sort_values(ascending=False))index0 = numerical_corr.sort_values(ascending=False).indexprint(train_data_scaler[index0].corr('spearman'))

target    1.000000V0        0.712403V31       0.711636V1        0.682909V8        0.679469V27       0.657398V2        0.585850V16       0.545793V3        0.501622V4        0.478683V12       0.460300V10       0.448682V36       0.425991V37       0.376443V24       0.305526V5        0.286076V6        0.280195V20       0.278381V11       0.234551V15       0.221290V29       0.190109V7        0.185321V19       0.180111V18       0.149741V13       0.149199V17       0.126262V22       0.112743V30       0.101378Name: target, dtype: float64          target        V0       V31        V1        V8       V27        V2  \target  1.000000  0.712403  0.711636  0.682909  0.679469  0.657398  0.585850   V0      0.712403  1.000000  0.739116  0.894116  0.832151  0.763128  0.516817   V31     0.711636  0.739116  1.000000  0.807585  0.841469  0.765750  0.589890   V1      0.682909  0.894116  0.807585  1.000000  0.849034  0.807102  0.490239   V8      0.679469  0.832151  0.841469  0.849034  1.000000  0.887119  0.676417   V27     0.657398  0.763128  0.765750  0.807102  0.887119  1.000000  0.709534   V2      0.585850  0.516817  0.589890  0.490239  0.676417  0.709534  1.000000   V16     0.545793  0.388852  0.642309  0.396122  0.642156  0.620981  0.783643   V3      0.501622  0.401150  0.420134  0.363749  0.400915  0.402468  0.417190   V4      0.478683  0.697430  0.521226  0.651615  0.455801  0.424260  0.062134   V12     0.460300  0.640696  0.471528  0.596173  0.368572  0.336190  0.055734   V10     0.448682  0.279350  0.445335  0.255763  0.351127  0.203066  0.292769   V36     0.425991  0.214930  0.390250  0.192985  0.263291  0.186131  0.259475   V37    -0.376443 -0.472200 -0.301906 -0.397080 -0.507057 -0.557098 -0.731786   V24    -0.305526 -0.336325 -0.267968 -0.289742 -0.148323 -0.153834  0.018458   V5     -0.286076 -0.356704 -0.162304 -0.242776 -0.188993 -0.222596 -0.324464   V6      0.280195  0.131507  0.340145  0.147037  0.355064  0.356526  0.546921   V20     0.278381  0.444939  0.349530  0.421987  0.408853  0.361040  0.293635   V11    -0.234551 -0.333101 -0.131425 -0.221910 -0.161792 -0.190952 -0.271868   V15     0.221290  0.334135  0.110674  0.230395  0.054701  0.007156 -0.206499   V29     0.190109  0.334603  0.121833  0.240964  0.050211  0.006048 -0.255559   V7      0.185321  0.075732  0.277283  0.082766  0.278231  0.290620  0.378984   V19    -0.180111 -0.144295 -0.183185 -0.146559 -0.170237 -0.228613 -0.179416   V18     0.149741  0.132143  0.094678  0.093688  0.079592  0.091660  0.114929   V13     0.149199  0.173861  0.071517  0.134595  0.105380  0.126831  0.180477   V17     0.126262  0.055024  0.115056  0.081446  0.102544  0.036520 -0.050935   V22    -0.112743 -0.076698 -0.106450 -0.072848 -0.078333 -0.111196 -0.241206   V30     0.101378  0.099242  0.131453  0.109216  0.165204  0.167073  0.176236                V16        V3        V4  ...       V11       V15       V29  \target  0.545793  0.501622  0.478683  ... -0.234551  0.221290  0.190109   V0      0.388852  0.401150  0.697430  ... -0.333101  0.334135  0.334603   V31     0.642309  0.420134  0.521226  ... -0.131425  0.110674  0.121833   V1      0.396122  0.363749  0.651615  ... -0.221910  0.230395  0.240964   V8      0.642156  0.400915  0.455801  ... -0.161792  0.054701  0.050211   V27     0.620981  0.402468  0.424260  ... -0.190952  0.007156  0.006048   V2      0.783643  0.417190  0.062134  ... -0.271868 -0.206499 -0.255559   V16     1.000000  0.388886  0.009749  ... -0.088716 -0.280952 -0.327558   V3      0.388886  1.000000  0.294049  ... -0.126924  0.145291  0.128079   V4      0.009749  0.294049  1.000000  ... -0.164113  0.641180  0.692626   V12    -0.024541  0.286500  0.897807  ... -0.232228  0.703861  0.732617   V10     0.473009  0.295181  0.123829  ...  0.049969 -0.014449 -0.060440   V36     0.469130  0.299063  0.099359  ... -0.017805 -0.012844 -0.051097   V37    -0.431507 -0.219751  0.040396  ...  0.455998  0.234751  0.273926   V24     0.064523 -0.237022 -0.558334  ...  0.170969 -0.687353 -0.677833   V5     -0.045495 -0.230466 -0.248061  ...  0.797583 -0.250027 -0.233233   V6      0.760362  0.181135 -0.204780  ... -0.170545 -0.443436 -0.486682   V20     0.239572  0.270647  0.257815  ... -0.138684  0.050867  0.035022   V11    -0.088716 -0.126924 -0.164113  ...  1.000000 -0.123004 -0.120982   V15    -0.280952  0.145291  0.641180  ... -0.123004  1.000000  0.947360   V29    -0.327558  0.128079  0.692626  ... -0.120982  0.947360  1.000000   V7      0.651907  0.132564 -0.150577  ... -0.097623 -0.335054 -0.360490   V19    -0.019645 -0.265940 -0.237529  ... -0.094150 -0.215364 -0.212691   V18     0.066147  0.014697  0.135792  ... -0.153625  0.109030  0.098474   V13     0.074214 -0.019453  0.061801  ... -0.436341  0.047845  0.024514   V17     0.172978  0.067720  0.060753  ...  0.192222 -0.004555 -0.006498   V22    -0.091204 -0.305218  0.021174  ...  0.079577  0.069993  0.072070   V30     0.217428  0.055660 -0.053976  ... -0.102750 -0.147541 -0.161966                 V7       V19       V18       V13       V17       V22       V30  target  0.185321 -0.180111  0.149741  0.149199  0.126262 -0.112743  0.101378  V0      0.075732 -0.144295  0.132143  0.173861  0.055024 -0.076698  0.099242  V31     0.277283 -0.183185  0.094678  0.071517  0.115056 -0.106450  0.131453  V1      0.082766 -0.146559  0.093688  0.134595  0.081446 -0.072848  0.109216  V8      0.278231 -0.170237  0.079592  0.105380  0.102544 -0.078333  0.165204  V27     0.290620 -0.228613  0.091660  0.126831  0.036520 -0.111196  0.167073  V2      0.378984 -0.179416  0.114929  0.180477 -0.050935 -0.241206  0.176236  V16     0.651907 -0.019645  0.066147  0.074214  0.172978 -0.091204  0.217428  V3      0.132564 -0.265940  0.014697 -0.019453  0.067720 -0.305218  0.055660  V4     -0.150577 -0.237529  0.135792  0.061801  0.060753  0.021174 -0.053976  V12    -0.157087 -0.174034  0.125965  0.102293  0.012429 -0.004863 -0.054432  V10     0.242818  0.089046  0.038237 -0.100776  0.258885 -0.132951  0.027257  V36     0.268044  0.099034  0.066478 -0.068582  0.298962 -0.136943  0.056802  V37    -0.284305  0.025241 -0.097699 -0.344661  0.052673  0.110455 -0.176127  V24     0.076407  0.287262 -0.221117 -0.073906  0.094367  0.081279  0.079363  V5      0.118541  0.247903 -0.191786 -0.408978  0.342555  0.143785  0.020252  V6      0.904614  0.292661  0.061109  0.088866  0.094702 -0.102842  0.201834  V20     0.064205  0.029483  0.050529  0.004600  0.061369 -0.092706  0.035036  V11    -0.097623 -0.094150 -0.153625 -0.436341  0.192222  0.079577 -0.102750  V15    -0.335054 -0.215364  0.109030  0.047845 -0.004555  0.069993 -0.147541  V29    -0.360490 -0.212691  0.098474  0.024514 -0.006498  0.072070 -0.161966  V7      1.000000  0.269472  0.032519  0.059724  0.178034  0.058178  0.196347  V19     0.269472  1.000000 -0.034215 -0.106162  0.250114  0.075582  0.120766  V18     0.032519 -0.034215  1.000000  0.242008 -0.073678  0.016819  0.133708  V13     0.059724 -0.106162  0.242008  1.000000 -0.108020  0.348432 -0.097178  V17     0.178034  0.250114 -0.073678 -0.108020  1.000000  0.363785  0.057480  V22     0.058178  0.075582  0.016819  0.348432  0.363785  1.000000 -0.054570  V30     0.196347  0.120766  0.133708 -0.097178  0.057480 -0.054570  1.000000  [28 rows x 28 columns]

2.2.1 相关性初筛

features_corr = numerical_corr.sort_values(ascending=False).reset_index()features_corr.columns = ['features_and_target', 'corr']features_corr_select = features_corr[features_corr['corr']>0.3] # 筛选出大于相关性大于0.3的特色print(features_corr_select)select_features = [col for col in features_corr_select['features_and_target'] if col not in ['target']]new_train_data_corr_select = train_data_scaler[select_features+['target']]new_test_data_corr_select = test_data_scaler[select_features]

   features_and_target      corr0               target  1.0000001                   V0  0.7124032                  V31  0.7116363                   V1  0.6829094                   V8  0.6794695                  V27  0.6573986                   V2  0.5858507                  V16  0.5457938                   V3  0.5016229                   V4  0.47868310                 V12  0.46030011                 V10  0.44868212                 V36  0.42599113                 V37  0.37644314                 V24  0.305526

2.2.2 多重共线性剖析

!pip install statsmodels -i https://pypi.tuna.tsinghua.edu.cn/simple

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simpleRequirement already satisfied: statsmodels in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (0.13.5)Requirement already satisfied: scipy>=1.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from statsmodels) (1.6.3)Requirement already satisfied: pandas>=0.25 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from statsmodels) (1.1.5)Requirement already satisfied: packaging>=21.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from statsmodels) (21.3)Requirement already satisfied: numpy>=1.17 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from statsmodels) (1.19.5)Requirement already satisfied: patsy>=0.5.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from statsmodels) (0.5.3)Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from packaging>=21.3->statsmodels) (3.0.9)Requirement already satisfied: pytz>=2017.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pandas>=0.25->statsmodels) (2019.3)Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pandas>=0.25->statsmodels) (2.8.2)Requirement already satisfied: six in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from patsy>=0.5.2->statsmodels) (1.16.0)[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip available: [0m[31;49m22.1.2[0m[39;49m -> [0m[32;49m23.0.1[0m[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m

from statsmodels.stats.outliers_influence import variance_inflation_factor #多重共线性方差收缩因子#多重共线性new_numerical=['V0', 'V2', 'V3', 'V4', 'V5', 'V6', 'V10','V11',                          'V13', 'V15', 'V16', 'V18', 'V19', 'V20', 'V22','V24','V30', 'V31', 'V37']X=np.matrix(train_data_scaler[new_numerical])VIF_list=[variance_inflation_factor(X, i) for i in range(X.shape[1])]VIF_list

[216.73387180903222, 114.38118723828812, 27.863778129686356, 201.96436579080174, 78.93722825798903, 151.06983667656212, 14.519604941508451, 82.69750284665385, 28.479378440614585, 27.759176471505945, 526.6483470743831, 23.50166642638334, 19.920315849901424, 24.640481765008683, 11.816055964845381, 4.958208708452915, 37.09877416736591, 298.26442986612767, 47.854002539887034]

2.2.3 PCA解决降维

from sklearn.decomposition import PCA   #主成分分析法#PCA办法降维#放弃90%的信息pca = PCA(n_components=0.9)new_train_pca_90 = pca.fit_transform(train_data_scaler.iloc[:,0:-1])new_test_pca_90 = pca.transform(test_data_scaler)new_train_pca_90 = pd.DataFrame(new_train_pca_90)new_test_pca_90 = pd.DataFrame(new_test_pca_90)new_train_pca_90['target'] = train_data_scaler['target']new_train_pca_90.describe()

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train_data_scaler.describe()

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#PCA办法降维#保留16个主成分pca = PCA(n_components=0.95)new_train_pca_16 = pca.fit_transform(train_data_scaler.iloc[:,0:-1])new_test_pca_16 = pca.transform(test_data_scaler)new_train_pca_16 = pd.DataFrame(new_train_pca_16)new_test_pca_16 = pd.DataFrame(new_test_pca_16)new_train_pca_16['target'] = train_data_scaler['target']new_train_pca_16.describe()

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<p>8 rows × 22 columns</p>
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3.模型训练

3.1 回归及相干模型

## 导入相干库from sklearn.linear_model import LinearRegression  #线性回归from sklearn.neighbors import KNeighborsRegressor  #K近邻回归from sklearn.tree import DecisionTreeRegressor     #决策树回归from sklearn.ensemble import RandomForestRegressor #随机森林回归from sklearn.svm import SVR  #反对向量回归import lightgbm as lgb #lightGbm模型from sklearn.ensemble import GradientBoostingRegressorfrom sklearn.model_selection import train_test_split # 切分数据from sklearn.metrics import mean_squared_error #评估指标from sklearn.model_selection import learning_curvefrom sklearn.model_selection import ShuffleSplit## 切分训练数据和线下验证数据#采纳 pca 保留16维特色的数据new_train_pca_16 = new_train_pca_16.fillna(0)train = new_train_pca_16[new_test_pca_16.columns]target = new_train_pca_16['target']# 切分数据 训练数据80% 验证数据20%train_data,test_data,train_target,test_target=train_test_split(train,target,test_size=0.2,random_state=0)

3.1.1 多元线性回归模型

clf = LinearRegression()clf.fit(train_data, train_target)score = mean_squared_error(test_target, clf.predict(test_data))print("LinearRegression:   ", score)train_score = []test_score = []# 给予不同的数据量，查看模型的学习效果for i in range(10, len(train_data)+1, 10):    lin_reg = LinearRegression()    lin_reg.fit(train_data[:i], train_target[:i])    # LinearRegression().fit(X_train[:i], y_train[:i])        # 查看模型的预测状况：两种，模型基于训练数据集预测的状况(能够了解为模型拟合训练数据集的状况)，模型基于测试数据集预测的状况    # 此处应用 lin_reg.predict(X_train[:i])，为训练模型的全副数据集    y_train_predict = lin_reg.predict(train_data[:i])    train_score.append(mean_squared_error(train_target[:i], y_train_predict))        y_test_predict = lin_reg.predict(test_data)    test_score.append(mean_squared_error(test_target, y_test_predict))    # np.sqrt(train_score)：将列表 train_score 中的数开平方plt.plot([i for i in range(1, len(train_score)+1)], train_score, label='train')plt.plot([i for i in range(1, len(test_score)+1)], test_score, label='test')# plt.legend()：显示图例（如图形的 label）；plt.legend()plt.show()

LinearRegression:    0.2642337917628173

定义绘制模型学习曲线函数

def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):    plt.figure()    plt.title(title)    if ylim is not None:        plt.ylim(*ylim)    plt.xlabel("Training examples")    plt.ylabel("Score")    train_sizes, train_scores, test_scores = learning_curve(        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)    train_scores_mean = np.mean(train_scores, axis=1)    train_scores_std = np.std(train_scores, axis=1)    test_scores_mean = np.mean(test_scores, axis=1)    test_scores_std = np.std(test_scores, axis=1)        print(train_scores_mean)    print(test_scores_mean)        plt.grid()     plt.fill_between(train_sizes, train_scores_mean - train_scores_std,                     train_scores_mean + train_scores_std, alpha=0.1,                     color="r")    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,                     test_scores_mean + test_scores_std, alpha=0.1, color="g")    plt.plot(train_sizes, train_scores_mean, 'o-', color="r",             label="Training score")    plt.plot(train_sizes, test_scores_mean, 'o-', color="g",             label="Cross-validation score")     plt.legend(loc="best")    return plt

def plot_learning_curve_old(algo, X_train, X_test, y_train, y_test):    """绘制学习曲线：只须要传入算法(或实例对象)、X_train、X_test、y_train、y_test"""    """当应用该函数时传入算法，该算法的变量要进行实例化，如：PolynomialRegression(degree=2)，变量 degree 要进行实例化"""    train_score = []    test_score = []    for i in range(10, len(X_train)+1, 10):        algo.fit(X_train[:i], y_train[:i])                y_train_predict = algo.predict(X_train[:i])        train_score.append(mean_squared_error(y_train[:i], y_train_predict))            y_test_predict = algo.predict(X_test)        test_score.append(mean_squared_error(y_test, y_test_predict))        plt.plot([i for i in range(1, len(train_score)+1)],            train_score, label="train")    plt.plot([i for i in range(1, len(test_score)+1)],            test_score, label="test")        plt.legend()    plt.show()

# plot_learning_curve_old(LinearRegression(), train_data, test_data, train_target, test_target)

# 线性回归模型学习曲线X = train_data.valuesy = train_target.values # 图一title = r"LinearRegression"cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)estimator = LinearRegression()    #建模plot_learning_curve(estimator, title, X, y, ylim=(0.5, 0.8), cv=cv, n_jobs=1)

[0.70183463 0.66761103 0.66101945 0.65732898 0.65360375][0.57364886 0.61882339 0.62809368 0.63012866 0.63158596]<module 'matplotlib.pyplot' from '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/pyplot.py'>

3.1.2 KNN近邻回归

for i in range(3,10):    clf = KNeighborsRegressor(n_neighbors=i) # 最近三个    clf.fit(train_data, train_target)    score = mean_squared_error(test_target, clf.predict(test_data))    print("KNeighborsRegressor:   ", score)

KNeighborsRegressor:    0.27619208861976163KNeighborsRegressor:    0.2597627823313149KNeighborsRegressor:    0.2628212724567474KNeighborsRegressor:    0.26670982271241833KNeighborsRegressor:    0.2659603905091448KNeighborsRegressor:    0.26353694644788067KNeighborsRegressor:    0.2673470579477979

# plot_learning_curve_old(KNeighborsRegressor(n_neighbors=5) , train_data, test_data, train_target, test_target)

# 绘制K近邻回归学习曲线X = train_data.valuesy = train_target.values # K近邻回归title = r"KNeighborsRegressor"cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)estimator = KNeighborsRegressor(n_neighbors=8)    #建模plot_learning_curve(estimator, title, X, y, ylim=(0.3, 0.9), cv=cv, n_jobs=1)

[0.61581146 0.68763995 0.71414969 0.73084172 0.73976273][0.50369207 0.58753672 0.61969929 0.64062459 0.6560054 ]<module 'matplotlib.pyplot' from '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/pyplot.py'>

3.1.3决策树回归

clf = DecisionTreeRegressor() clf.fit(train_data, train_target)score = mean_squared_error(test_target, clf.predict(test_data))print("DecisionTreeRegressor:   ", score)

DecisionTreeRegressor:    0.6405298823529413

# plot_learning_curve_old(DecisionTreeRegressor(), train_data, test_data, train_target, test_target)

X = train_data.valuesy = train_target.values # 决策树回归title = r"DecisionTreeRegressor"cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)estimator = DecisionTreeRegressor()    #建模plot_learning_curve(estimator, title, X, y, ylim=(0.1, 1.3), cv=cv, n_jobs=1)

[1. 1. 1. 1. 1.][0.11833987 0.22982731 0.2797608  0.30950084 0.32628853]<module 'matplotlib.pyplot' from '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/pyplot.py'>

3.1.4 随机森林回归

clf = RandomForestRegressor(n_estimators=200) # 200棵树模型clf.fit(train_data, train_target)score = mean_squared_error(test_target, clf.predict(test_data))print("RandomForestRegressor:   ", score)# plot_learning_curve_old(RandomForestRegressor(n_estimators=200), train_data, test_data, train_target, test_target)

RandomForestRegressor:    0.24087959640588236

X = train_data.valuesy = train_target.values # 随机森林title = r"RandomForestRegressor"cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)estimator = RandomForestRegressor(n_estimators=200)    #建模plot_learning_curve(estimator, title, X, y, ylim=(0.4, 1.0), cv=cv, n_jobs=1)

[0.93619796 0.94798334 0.95197393 0.95415054 0.95570763][0.53953995 0.61531165 0.64366926 0.65941678 0.67319725]<module 'matplotlib.pyplot' from '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/pyplot.py'>

3.1.5 Gradient Boosting

from sklearn.ensemble import GradientBoostingRegressormyGBR = GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,                                  learning_rate=0.03, loss='huber', max_depth=14,                                  max_features='sqrt', max_leaf_nodes=None,                                  min_impurity_decrease=0.0, min_impurity_split=None,                                  min_samples_leaf=10, min_samples_split=40,                                  min_weight_fraction_leaf=0.0, n_estimators=10,                                  warm_start=False)# 参数已删除 presort=True, random_state=10, subsample=0.8, verbose=0,myGBR.fit(train_data, train_target)score = mean_squared_error(test_target, clf.predict(test_data))print("GradientBoostingRegressor:   ", score)myGBR = GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,                                  learning_rate=0.03, loss='huber', max_depth=14,                                  max_features='sqrt', max_leaf_nodes=None,                                  min_impurity_decrease=0.0, min_impurity_split=None,                                  min_samples_leaf=10, min_samples_split=40,                                  min_weight_fraction_leaf=0.0, n_estimators=10,                                  warm_start=False)#为了疾速展现n_estimators设置较小，实战中请按需设置# plot_learning_curve_old(myGBR, train_data, test_data, train_target, test_target)

GradientBoostingRegressor:    0.906640574789251

X = train_data.valuesy = train_target.values # GradientBoostingtitle = r"GradientBoostingRegressor"cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)estimator = GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,                                  learning_rate=0.03, loss='huber', max_depth=14,                                  max_features='sqrt', max_leaf_nodes=None,                                  min_impurity_decrease=0.0, min_impurity_split=None,                                  min_samples_leaf=10, min_samples_split=40,                                  min_weight_fraction_leaf=0.0, n_estimators=10,                                  warm_start=False)  #建模plot_learning_curve(estimator, title, X, y, ylim=(0.4, 1.0), cv=cv, n_jobs=1)#为了疾速展现n_estimators设置较小，实战中请按需设置

3.1.6 lightgbm回归

# lgb回归模型clf = lgb.LGBMRegressor(        learning_rate=0.01,        max_depth=-1,        n_estimators=10,        boosting_type='gbdt',        random_state=2019,        objective='regression',    )# #为了疾速展现n_estimators设置较小，实战中请按需设置# 训练模型clf.fit(        X=train_data, y=train_target,        eval_metric='MSE',        verbose=50    )score = mean_squared_error(test_target, clf.predict(test_data))print("lightGbm:   ", score)

lightGbm:    0.906640574789251

X = train_data.valuesy = train_target.values # LGBMtitle = r"LGBMRegressor"cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)estimator = lgb.LGBMRegressor(    learning_rate=0.01,    max_depth=-1,    n_estimators=10,    boosting_type='gbdt',    random_state=2019,    objective='regression'    )    #建模plot_learning_curve(estimator, title, X, y, ylim=(0.4, 1.0), cv=cv, n_jobs=1)#为了疾速展现n_estimators设置较小，实战中请按需设置

4.篇中总结

在工业蒸汽量预测上篇中，次要解说了数据探索性剖析：查看变量间相关性以及找出要害变量；数据特色工程对数据精进：异样值解决、归一化解决以及特色降维；在进行归回模型训练波及支流ML模型：决策树、随机森林，lightgbm等。下一篇中将着重解说模型验证、特色优化、模型交融等。

原我的项目链接：https://www.heywhale.com/home/column/64141d6b1c8c8b518ba97dcc

参考链接：https://tianchi.aliyun.com/course/278/3427