二手车交易价格预测-Baseline

Baseline-v1.0 版

Tip:这是一个最初始baseline版本,抛砖引玉,为大家提供一个基本Baseline和一个竞赛流程的基本介绍，欢迎大家多多交流。

赛题：零基础入门数据挖掘 - 二手车交易价格预测

地址：https://tianchi.aliyun.com/competition/entrance/231784/introduction?spm=5176.12281957.1004.1.38b02448ausjSX

# 查看数据文件目录  list datalab files
!ls datalab/

Step 1:导入函数工具箱

## 基础工具
import numpy as np
import pandas as pd
import warnings
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.special import jn
from IPython.display import display, clear_output
import timewarnings.filterwarnings('ignore')
%matplotlib inline## 模型预测的
from sklearn import linear_model
from sklearn import preprocessing
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor## 数据降维处理的
from sklearn.decomposition import PCA,FastICA,FactorAnalysis,SparsePCAimport lightgbm as lgb
import xgboost as xgb## 参数搜索和评价的
from sklearn.model_selection import GridSearchCV,cross_val_score,StratifiedKFold,train_test_split
from sklearn.metrics import mean_squared_error, mean_absolute_error

Step 2:数据读取

## 通过Pandas对于数据进行读取 (pandas是一个很友好的数据读取函数库)
Train_data = pd.read_csv('datalab/231784/used_car_train_20200313.csv', sep=' ')
TestA_data = pd.read_csv('datalab/231784/used_car_testA_20200313.csv', sep=' ')## 输出数据的大小信息
print('Train data shape:',Train_data.shape)
print('TestA data shape:',TestA_data.shape)

Train data shape: (150000, 31)
TestA data shape: (50000, 30)

1) 数据简要浏览

## 通过.head() 简要浏览读取数据的形式
Train_data.head()

	SaleID	name	regDate	model	brand	bodyType	gearbox	power	kilometer	...	v_5	v_6	v_7	v_8	v_9	v_10	v_11	v_12	v_13	v_14
0	0	736	20040402	30.0	6	1.0	0.0	60	12.5	...	0.235676	0.101988	0.129549	0.022816	0.097462	-2.881803	2.804097	-2.420821	0.795292	0.914762
1	1	2262	20030301	40.0	1	2.0	0.0	0	15.0	...	0.264777	0.121004	0.135731	0.026597	0.020582	-4.900482	2.096338	-1.030483	-1.722674	0.245522
2	2	14874	20040403	115.0	15	1.0	0.0	163	12.5	...	0.251410	0.114912	0.165147	0.062173	0.027075	-4.846749	1.803559	1.565330	-0.832687	-0.229963
3	3	71865	19960908	109.0	10	0.0	1.0	193	15.0	...	0.274293	0.110300	0.121964	0.033395	0.000000	-4.509599	1.285940	-0.501868	-2.438353	-0.478699
4	4	111080	20120103	110.0	5	1.0	0.0	68	5.0	...	0.228036	0.073205	0.091880	0.078819	0.121534	-1.896240	0.910783	0.931110	2.834518	1.923482

5 rows × 31 columns

2) 数据信息查看

## 通过 .info() 简要可以看到对应一些数据列名，以及NAN缺失信息
Train_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150000 entries, 0 to 149999
Data columns (total 31 columns):
SaleID               150000 non-null int64
name                 150000 non-null int64
regDate              150000 non-null int64
model                149999 non-null float64
brand                150000 non-null int64
bodyType             145494 non-null float64
fuelType             141320 non-null float64
gearbox              144019 non-null float64
power                150000 non-null int64
kilometer            150000 non-null float64
notRepairedDamage    150000 non-null object
regionCode           150000 non-null int64
seller               150000 non-null int64
offerType            150000 non-null int64
creatDate            150000 non-null int64
price                150000 non-null int64
v_0                  150000 non-null float64
v_1                  150000 non-null float64
v_2                  150000 non-null float64
v_3                  150000 non-null float64
v_4                  150000 non-null float64
v_5                  150000 non-null float64
v_6                  150000 non-null float64
v_7                  150000 non-null float64
v_8                  150000 non-null float64
v_9                  150000 non-null float64
v_10                 150000 non-null float64
v_11                 150000 non-null float64
v_12                 150000 non-null float64
v_13                 150000 non-null float64
v_14                 150000 non-null float64
dtypes: float64(20), int64(10), object(1)
memory usage: 35.5+ MB

## 通过 .columns 查看列名
Train_data.columns

Index(['SaleID', 'name', 'regDate', 'model', 'brand', 'bodyType', 'fuelType','gearbox', 'power', 'kilometer', 'notRepairedDamage', 'regionCode','seller', 'offerType', 'creatDate', 'price', 'v_0', 'v_1', 'v_2', 'v_3','v_4', 'v_5', 'v_6', 'v_7', 'v_8', 'v_9', 'v_10', 'v_11', 'v_12','v_13', 'v_14'],dtype='object')

TestA_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50000 entries, 0 to 49999
Data columns (total 30 columns):
SaleID               50000 non-null int64
name                 50000 non-null int64
regDate              50000 non-null int64
model                50000 non-null float64
brand                50000 non-null int64
bodyType             48587 non-null float64
fuelType             47107 non-null float64
gearbox              48090 non-null float64
power                50000 non-null int64
kilometer            50000 non-null float64
notRepairedDamage    50000 non-null object
regionCode           50000 non-null int64
seller               50000 non-null int64
offerType            50000 non-null int64
creatDate            50000 non-null int64
v_0                  50000 non-null float64
v_1                  50000 non-null float64
v_2                  50000 non-null float64
v_3                  50000 non-null float64
v_4                  50000 non-null float64
v_5                  50000 non-null float64
v_6                  50000 non-null float64
v_7                  50000 non-null float64
v_8                  50000 non-null float64
v_9                  50000 non-null float64
v_10                 50000 non-null float64
v_11                 50000 non-null float64
v_12                 50000 non-null float64
v_13                 50000 non-null float64
v_14                 50000 non-null float64
dtypes: float64(20), int64(9), object(1)
memory usage: 11.4+ MB

3) 数据统计信息浏览

## 通过 .describe() 可以查看数值特征列的一些统计信息
Train_data.describe()

	SaleID	name	regDate	model	brand	bodyType	fuelType	gearbox	power	kilometer	...	v_5	v_6	v_7	v_8	v_9	v_10	v_11	v_12	v_13	v_14
count	150000.000000	150000.000000	1.500000e+05	149999.000000	150000.000000	145494.000000	141320.000000	144019.000000	150000.000000	150000.000000	...	150000.000000	150000.000000	150000.000000	150000.000000	150000.000000	150000.000000	150000.000000	150000.000000	150000.000000	150000.000000
mean	74999.500000	68349.172873	2.003417e+07	47.129021	8.052733	1.792369	0.375842	0.224943	119.316547	12.597160	...	0.248204	0.044923	0.124692	0.058144	0.061996	-0.001000	0.009035	0.004813	0.000313	-0.000688
std	43301.414527	61103.875095	5.364988e+04	49.536040	7.864956	1.760640	0.548677	0.417546	177.168419	3.919576	...	0.045804	0.051743	0.201410	0.029186	0.035692	3.772386	3.286071	2.517478	1.288988	1.038685
min	0.000000	0.000000	1.991000e+07	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.500000	...	0.000000	0.000000	0.000000	0.000000	0.000000	-9.168192	-5.558207	-9.639552	-4.153899	-6.546556
25%	37499.750000	11156.000000	1.999091e+07	10.000000	1.000000	0.000000	0.000000	0.000000	75.000000	12.500000	...	0.243615	0.000038	0.062474	0.035334	0.033930	-3.722303	-1.951543	-1.871846	-1.057789	-0.437034
50%	74999.500000	51638.000000	2.003091e+07	30.000000	6.000000	1.000000	0.000000	0.000000	110.000000	15.000000	...	0.257798	0.000812	0.095866	0.057014	0.058484	1.624076	-0.358053	-0.130753	-0.036245	0.141246
75%	112499.250000	118841.250000	2.007111e+07	66.000000	13.000000	3.000000	1.000000	0.000000	150.000000	15.000000	...	0.265297	0.102009	0.125243	0.079382	0.087491	2.844357	1.255022	1.776933	0.942813	0.680378
max	149999.000000	196812.000000	2.015121e+07	247.000000	39.000000	7.000000	6.000000	1.000000	19312.000000	15.000000	...	0.291838	0.151420	1.404936	0.160791	0.222787	12.357011	18.819042	13.847792	11.147669	8.658418

8 rows × 30 columns

TestA_data.describe()

	SaleID	name	regDate	model	brand	bodyType	fuelType	gearbox	power	kilometer	...	v_5	v_6	v_7	v_8	v_9	v_10	v_11	v_12	v_13	v_14
count	50000.000000	50000.000000	5.000000e+04	50000.000000	50000.000000	48587.000000	47107.000000	48090.000000	50000.000000	50000.000000	...	50000.000000	50000.000000	50000.000000	50000.000000	50000.000000	50000.000000	50000.000000	50000.000000	50000.000000	50000.000000
mean	174999.500000	68542.223280	2.003393e+07	46.844520	8.056240	1.782185	0.373405	0.224350	119.883620	12.595580	...	0.248669	0.045021	0.122744	0.057997	0.062000	-0.017855	-0.013742	-0.013554	-0.003147	0.001516
std	14433.901067	61052.808133	5.368870e+04	49.469548	7.819477	1.760736	0.546442	0.417158	185.097387	3.908979	...	0.044601	0.051766	0.195972	0.029211	0.035653	3.747985	3.231258	2.515962	1.286597	1.027360
min	150000.000000	0.000000	1.991000e+07	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.500000	...	0.000000	0.000000	0.000000	0.000000	0.000000	-9.160049	-5.411964	-8.916949	-4.123333	-6.112667
25%	162499.750000	11203.500000	1.999091e+07	10.000000	1.000000	0.000000	0.000000	0.000000	75.000000	12.500000	...	0.243762	0.000044	0.062644	0.035084	0.033714	-3.700121	-1.971325	-1.876703	-1.060428	-0.437920
50%	174999.500000	52248.500000	2.003091e+07	29.000000	6.000000	1.000000	0.000000	0.000000	109.000000	15.000000	...	0.257877	0.000815	0.095828	0.057084	0.058764	1.613212	-0.355843	-0.142779	-0.035956	0.138799
75%	187499.250000	118856.500000	2.007110e+07	65.000000	13.000000	3.000000	1.000000	0.000000	150.000000	15.000000	...	0.265328	0.102025	0.125438	0.079077	0.087489	2.832708	1.262914	1.764335	0.941469	0.681163
max	199999.000000	196805.000000	2.015121e+07	246.000000	39.000000	7.000000	6.000000	1.000000	20000.000000	15.000000	...	0.291618	0.153265	1.358813	0.156355	0.214775	12.338872	18.856218	12.950498	5.913273	2.624622

8 rows × 29 columns

Step 3:特征与标签构建

1) 提取数值类型特征列名

numerical_cols = Train_data.select_dtypes(exclude = 'object').columns
print(numerical_cols)

Index(['SaleID', 'name', 'regDate', 'model', 'brand', 'bodyType', 'fuelType','gearbox', 'power', 'kilometer', 'regionCode', 'seller', 'offerType','creatDate', 'price', 'v_0', 'v_1', 'v_2', 'v_3', 'v_4', 'v_5', 'v_6','v_7', 'v_8', 'v_9', 'v_10', 'v_11', 'v_12', 'v_13', 'v_14'],dtype='object')

categorical_cols = Train_data.select_dtypes(include = 'object').columns
print(categorical_cols)

Index(['notRepairedDamage'], dtype='object')

2) 构建训练和测试样本

## 选择特征列
feature_cols = [col for col in numerical_cols if col not in ['SaleID','name','regDate','creatDate','price','model','brand','regionCode','seller']]
feature_cols = [col for col in feature_cols if 'Type' not in col]## 提前特征列，标签列构造训练样本和测试样本
X_data = Train_data[feature_cols]
Y_data = Train_data['price']X_test  = TestA_data[feature_cols]print('X train shape:',X_data.shape)
print('X test shape:',X_test.shape)

X train shape: (150000, 18)
X test shape: (50000, 18)

## 定义了一个统计函数，方便后续信息统计
def Sta_inf(data):print('_min',np.min(data))print('_max:',np.max(data))print('_mean',np.mean(data))print('_ptp',np.ptp(data))print('_std',np.std(data))print('_var',np.var(data))

3) 统计标签的基本分布信息

print('Sta of label:')
Sta_inf(Y_data)

Sta of label:
_min 11
_max: 99999
_mean 5923.32733333
_ptp 99988
_std 7501.97346988
_var 56279605.9427

## 绘制标签的统计图，查看标签分布
plt.hist(Y_data)
plt.show()
plt.close()

4) 缺省值用-1填补

X_data = X_data.fillna(-1)
X_test = X_test.fillna(-1)

Step 4:模型训练与预测

1) 利用xgb进行五折交叉验证查看模型的参数效果

## xgb-Model
xgr = xgb.XGBRegressor(n_estimators=120, learning_rate=0.1, gamma=0, subsample=0.8,\colsample_bytree=0.9, max_depth=7) #,objective ='reg:squarederror'scores_train = []
scores = []## 5折交叉验证方式
sk=StratifiedKFold(n_splits=5,shuffle=True,random_state=0)
for train_ind,val_ind in sk.split(X_data,Y_data):train_x=X_data.iloc[train_ind].valuestrain_y=Y_data.iloc[train_ind]val_x=X_data.iloc[val_ind].valuesval_y=Y_data.iloc[val_ind]xgr.fit(train_x,train_y)pred_train_xgb=xgr.predict(train_x)pred_xgb=xgr.predict(val_x)score_train = mean_absolute_error(train_y,pred_train_xgb)scores_train.append(score_train)score = mean_absolute_error(val_y,pred_xgb)scores.append(score)print('Train mae:',np.mean(score_train))
print('Val mae',np.mean(scores))

Train mae: 628.086664863
Val mae 715.990013454

2）定义xgb和lgb模型函数

def build_model_xgb(x_train,y_train):model = xgb.XGBRegressor(n_estimators=150, learning_rate=0.1, gamma=0, subsample=0.8,\colsample_bytree=0.9, max_depth=7) #, objective ='reg:squarederror'model.fit(x_train, y_train)return modeldef build_model_lgb(x_train,y_train):estimator = lgb.LGBMRegressor(num_leaves=127,n_estimators = 150)param_grid = {'learning_rate': [0.01, 0.05, 0.1, 0.2],}gbm = GridSearchCV(estimator, param_grid)gbm.fit(x_train, y_train)return gbm

3）切分数据集（Train,Val）进行模型训练，评价和预测

## Split data with val
x_train,x_val,y_train,y_val = train_test_split(X_data,Y_data,test_size=0.3)

print('Train lgb...')
model_lgb = build_model_lgb(x_train,y_train)
val_lgb = model_lgb.predict(x_val)
MAE_lgb = mean_absolute_error(y_val,val_lgb)
print('MAE of val with lgb:',MAE_lgb)print('Predict lgb...')
model_lgb_pre = build_model_lgb(X_data,Y_data)
subA_lgb = model_lgb_pre.predict(X_test)
print('Sta of Predict lgb:')
Sta_inf(subA_lgb)

Train lgb...
MAE of val with lgb: 689.084070621
Predict lgb...
Sta of Predict lgb:
_min -519.150259864
_max: 88575.1087721
_mean 5922.98242599
_ptp 89094.259032
_std 7377.29714126
_var 54424513.1104

print('Train xgb...')
model_xgb = build_model_xgb(x_train,y_train)
val_xgb = model_xgb.predict(x_val)
MAE_xgb = mean_absolute_error(y_val,val_xgb)
print('MAE of val with xgb:',MAE_xgb)print('Predict xgb...')
model_xgb_pre = build_model_xgb(X_data,Y_data)
subA_xgb = model_xgb_pre.predict(X_test)
print('Sta of Predict xgb:')
Sta_inf(subA_xgb)

Train xgb...
MAE of val with xgb: 715.37757816
Predict xgb...
Sta of Predict xgb:
_min -165.479
_max: 90051.8
_mean 5922.9
_ptp 90217.3
_std 7361.13
_var 5.41862e+07

4）进行两模型的结果加权融合

## 这里我们采取了简单的加权融合的方式
val_Weighted = (1-MAE_lgb/(MAE_xgb+MAE_lgb))*val_lgb+(1-MAE_xgb/(MAE_xgb+MAE_lgb))*val_xgb
val_Weighted[val_Weighted<0]=10 # 由于我们发现预测的最小值有负数，而真实情况下，price为负是不存在的，由此我们进行对应的后修正
print('MAE of val with Weighted ensemble:',mean_absolute_error(y_val,val_Weighted))

MAE of val with Weighted ensemble: 687.275745703

sub_Weighted = (1-MAE_lgb/(MAE_xgb+MAE_lgb))*subA_lgb+(1-MAE_xgb/(MAE_xgb+MAE_lgb))*subA_xgb## 查看预测值的统计进行
plt.hist(Y_data)
plt.show()
plt.close()

5）输出结果

sub = pd.DataFrame()
sub['SaleID'] = X_test.SaleID
sub['price'] = sub_Weighted
sub.to_csv('./sub_Weighted.csv',index=False)

sub.head()

	SaleID	price
0	0	39533.727414
1	1	386.081960
2	2	7791.974571
3	3	11835.211966
4	4	585.420407

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赛题数据数据来自某交易平台的二手车交易记录,总数据量超过40w,包含31列变量信息,其中15列为匿名变量.从中抽取15万条作为训练集,5万条作为测试集A,5万条作为测试集B,同时会对name.mod ...

【算法竞赛学习】二手车交易价格预测-Baseline

二手车交易价格预测-Baseline

Baseline-v1.0 版

Step 1:导入函数工具箱

Step 2:数据读取

1) 数据简要浏览

2) 数据信息查看

3) 数据统计信息浏览

Step 3:特征与标签构建

1) 提取数值类型特征列名

2) 构建训练和测试样本

3) 统计标签的基本分布信息

4) 缺省值用-1填补

Step 4:模型训练与预测

1) 利用xgb进行五折交叉验证查看模型的参数效果

2）定义xgb和lgb模型函数

3）切分数据集（Train,Val）进行模型训练，评价和预测

4）进行两模型的结果加权融合

5）输出结果

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最新文章

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【算法竞赛学习】二手车交易价格预测-Baseline

二手车交易价格预测-Baseline

Baseline-v1.0 版

Step 1:导入函数工具箱

Step 2:数据读取

1) 数据简要浏览

2) 数据信息查看

3) 数据统计信息浏览

Step 3:特征与标签构建

1) 提取数值类型特征列名

2) 构建训练和测试样本

3) 统计标签的基本分布信息

4) 缺省值用-1填补

Step 4:模型训练与预测

1) 利用xgb进行五折交叉验证查看模型的参数效果

2） 定义xgb和lgb模型函数

3）切分数据集（Train,Val）进行模型训练，评价和预测

4）进行两模型的结果加权融合

5）输出结果

【算法竞赛学习】二手车交易价格预测-Baseline相关推荐

最新文章

热门文章

2）定义xgb和lgb模型函数