数据挖掘-二手车价格预测Part4

赛题简介

本次新人赛是Datawhale与天池联合发起的0基础入门系列赛事第一场 —— 零基础入门数据挖掘之二手车交易价格预测大赛。

赛题以二手车市场为背景，要求选手预测二手汽车的交易价格，这是一个典型的回归问题。通过这道赛题来引导大家走进AI数据竞赛的世界，主要针对于于竞赛新人进行自我练习、自我提高。

数据获取：
https://tianchi.aliyun.com/competition/entrance/231784/information

代码

1、定义三种模型（xgb、lgb、随机森林）

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 model

def 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

def build_model_random(x_train,y_train):
	my_model=RandomForestRegressor()
	my_model.fit(x_train, y_train)
	return my_model

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

2、模型效果

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)

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)

print('Train random...')
model_random=build_model_random(x_train,y_train)
val_random=model_random.predict(x_val)
MAE_random = mean_absolute_error(y_val,val_random)
print('MAE of val with random:',MAE_random)

print('Predict random...')
model_random_pre = build_model_random(X_data,Y_data)
subA_random = model_random_pre.predict(X_test)
print('Sta of Predict random:')
Sta_inf(subA_random)

3、模型融合

## 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))

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()

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

sub.head()