【机器学习】XGB实战—O2O优惠券使用预测

比赛地址：https://tianchi.aliyun.com/competition/entrance/231593/introduction

论坛：https://tianchi.aliyun.com/competition/entrance/231593/forum

参考教程：https://tianchi.aliyun.com/notebook-ai/detail?spm=5176.12586969.1002.6.29281b48A4SkSI&postId=58107

github：https://github.com/wepe/O2O-Coupon-Usage-Forecast

数据字段需特别说明的，received_data 点击数据；used_data 消费数据；

 

XGB代码

import pandas as pd
import xgboost as xgb
from sklearn.preprocessing import MinMaxScaler

dataset1 = xgb.DMatrix(dataset1_x,label=dataset1_y)
dataset2 = xgb.DMatrix(dataset2_x,label=dataset2_y)
dataset12 = xgb.DMatrix(dataset12_x,label=dataset12_y)
dataset3 = xgb.DMatrix(dataset3_x)

params={'booster':'gbtree',
	    'objective': 'rank:pairwise',
	    'eval_metric':'auc',
	    'gamma':0.1,
	    'min_child_weight':1.1,
	    'max_depth':5,
	    'lambda':10,
	    'subsample':0.7,
	    'colsample_bytree':0.7,
	    'colsample_bylevel':0.7,
	    'eta': 0.01,
	    'tree_method':'exact',
	    'seed':0,
	    'nthread':12
	    }

watchlist = [(dataset12,'train')]
model = xgb.train(params,dataset12,num_boost_round=3500,evals=watchlist)




#predict test set
dataset3_preds['label'] = model.predict(dataset3)
dataset3_preds.label = MinMaxScaler().fit_transform(dataset3_preds.label.reshape(-1, 1))
dataset3_preds.sort_values(by=['coupon_id','label'],inplace=True)
dataset3_preds.to_csv("xgb_preds.csv",index=None,header=None)
print dataset3_preds.describe()
    
#save feature score
feature_score = model.get_fscore()
feature_score = sorted(feature_score.items(), key=lambda x:x[1],reverse=True)
fs = []
for (key,value) in feature_score:
    fs.append("{0},{1}\n".format(key,value))
    
with open('xgb_feature_score.csv','w') as f:
    f.writelines("feature,score\n")
    f.writelines(fs)

项目背景：https://tianchi.aliyun.com/competition/entrance/231593/information

本特征处理代码：https://tianchi.aliyun.com/notebook-ai/detail?spm=5176.12586969.1002.6.29281b48A4SkSI&postId=58107