科大讯飞-电信客户流失预测挑战赛-Baseline
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科大讯飞-电信客户流失预测挑战赛
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- 赛事地址：
- 赛题介绍
- 赛题任务
- 赛题baseline
- 模型结果
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赛事地址：
http://challenge.xfyun.cn/topic/info?type=telecom-customer&option=ssgy
赛题介绍
随着市场饱和度的上升，电信运营商的竞争也越来越激烈，电信运营商亟待解决减少用户流失，延长用户生命周期的问题。对于客户流失率而言，每增加5%，利润就可能随之降低25%-85%。因此，如何减少电信用户流失的分析与预测至关重要。
鉴于此，运营商会经常设有客户服务部门，该部门的职能主要是做好客户流失分析，赢回高概率流失的客户，降低客户流失率。某电信机构的客户存在大量流失情况，导致该机构的用户量急速下降。面对如此头疼的问题，该机构将部分客户数据开放，诚邀大家帮助他们建立流失预测模型来预测可能流失的客户。
赛题任务
给定某电信机构实际业务中的相关客户信息，包含69个与客户相关的字段，其中“是否流失”字段表明客户会否会在观察日期后的两个月内流失。任务目标是通过训练集训练模型，来预测客户是否会流失，以此为依据开展工作，提高用户留存。
赛题baseline
def cv_model(clf, train_x, train_y, test_x, clf_name):
folds = 5
seed = 2022
kf = KFold(n_splits=folds, shuffle=True, random_state=seed)
train = np.zeros(train_x.shape[0])
test = np.zeros(test_x.shape[0])
cv_scores = []
for i, (train_index, valid_index) in enumerate(kf.split(train_x, train_y)):
print('************************************ {} ************************************'.format(str(i+1)))
trn_x, trn_y, val_x, val_y = train_x.iloc[train_index], train_y[train_index], train_x.iloc[valid_index], train_y[valid_index]
if clf_name == "lgb":
train_matrix = clf.Dataset(trn_x, label=trn_y)
valid_matrix = clf.Dataset(val_x, label=val_y)
params = {
'boosting_type': 'gbdt',
'objective': 'binary',
'metric': 'auc',
'min_child_weight': 5,
'num_leaves': 2 ** 5,
'lambda_l2': 10,
'feature_fraction': 0.7,
'bagging_fraction': 0.7,
'bagging_freq': 10,
'learning_rate': 0.2,
'seed': 2022,
'n_jobs':-1
}
model = clf.train(params, train_matrix, 50000, valid_sets=[train_matrix, valid_matrix],
categorical_feature=[], verbose_eval=3000, early_stopping_rounds=200)
val_pred = model.predict(val_x, num_iteration=model.best_iteration)
test_pred = model.predict(test_x, num_iteration=model.best_iteration)
print(list(sorted(zip(features, model.feature_importance("gain")), key=lambda x: x[1], reverse=True))[:20])
if clf_name == "xgb":
train_matrix = clf.DMatrix(trn_x , label=trn_y)
valid_matrix = clf.DMatrix(val_x , label=val_y)
test_matrix = clf.DMatrix(test_x)
params = {'booster': 'gbtree',
'objective': 'binary:logistic',
'eval_metric': 'auc',
'gamma': 1,
'min_child_weight': 1.5,
'max_depth': 5,
'lambda': 10,
'subsample': 0.7,
'colsample_bytree': 0.7,
'colsample_bylevel': 0.7,
'eta': 0.2,
'tree_method': 'exact',
'seed': 2020,
'nthread': 36,
"silent": True,
}
watchlist = [(train_matrix, 'train'),(valid_matrix, 'eval')]
model = clf.train(params, train_matrix, num_boost_round=50000, evals=watchlist, verbose_eval=3000, early_stopping_rounds=200)
val_pred = model.predict(valid_matrix, ntree_limit=model.best_ntree_limit)
test_pred = model.predict(test_matrix , ntree_limit=model.best_ntree_limit)
if clf_name == "cat":
params = {'learning_rate': 0.2, 'depth': 5, 'l2_leaf_reg': 10, 'bootstrap_type': 'Bernoulli',
'od_type': 'Iter', 'od_wait': 50, 'random_seed': 11, 'allow_writing_files': False}
model = clf(iterations=20000, **params)
model.fit(trn_x, trn_y, eval_set=(val_x, val_y),
cat_features=[], use_best_model=True, verbose=3000)
val_pred = model.predict(val_x)
test_pred = model.predict(test_x)
train[valid_index] = val_pred
test = test_pred / kf.n_splits
cv_scores.append(roc_auc_score(val_y, val_pred))
print(cv_scores)
print("%s_scotrainre_list:" % clf_name, cv_scores)
print("%s_score_mean:" % clf_name, np.mean(cv_scores))
print("%s_score_std:" % clf_name, np.std(cv_scores))
return train, test
def lgb_model(x_train, y_train, x_test):
lgb_train, lgb_test = cv_model(lgb, x_train, y_train, x_test, "lgb")
return lgb_train, lgb_test
def xgb_model(x_train, y_train, x_test):
xgb_train, xgb_test = cv_model(xgb, x_train, y_train, x_test, "xgb")
return xgb_train, xgb_test
def cat_model(x_train, y_train, x_test):
cat_train, cat_test = cv_model(CatBoostRegressor, x_train, y_train, x_test, "cat")
return cat_train, cat_test
lgb_train, lgb_test = lgb_model(x_train, y_train, x_test)
模型结果
通过Baseline代码跑，CATBOOST、LightGBM、XGBoost，最后发现只有LightGBM的评分最高为0.8288。因为使用飞浆，经历过一次没有保存下来的情况，跑出来太花时间，对参数进行了调整，把迭代的次数减少，算力的降低也导致了模型的评分下降。
