帮我用python写代码，O2O优惠券使用预测，提供用户在2016年1月1日至2016年6月30日之间真实线上线下消费行为，预测用户在2016年7月领取优惠券后15天以内的使用情况。训练集为"F:\Pycharm\期末考查题目（二选一）\题目一\data\ccf_offline_stage1_train.csv"，测试集为"F:\Pycharm\期末考查题目（二选一）\题目一\data\ccf_offline_stage1_test_revised.csv"。注意，其中存在非数字列'Date_received'，请帮我做适当处理，根据优惠券发放及核销情况，构建不同的特征：用户领取的优惠券数量；用户消费过的优惠券数量；用户与商家间的距离。

好的，这是一个比较典型的机器学习项目，需要对数据进行预处理、特征工程、模型训练和预测。我会帮你一步步实现，首先，我们需要导入一些必要的库：pandas、numpy、sklearn等。请确认你已经安装了这些库，如果没有，请使用pip install命令进行安装。以下是代码实现：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import MinMaxScaler

接着，我们需要加载训练集和测试集数据，并对数据进行预处理，包括删除空值、将非数字列转换为数字列等。以下是代码实现：

# 加载训练集和测试集
train_df = pd.read_csv('F:\Pycharm\期末考查题目（二选一）\题目一\data\ccf_offline_stage1_train.csv', header=None)
test_df = pd.read_csv('F:\Pycharm\期末考查题目（二选一）\题目一\data\ccf_offline_stage1_test_revised.csv', header=None)

# 删除空值
train_df.dropna(inplace=True)
test_df.dropna(inplace=True)

# 将非数字列转换为数字列
train_df[6] = train_df[6].apply(lambda x: str(x)[:8])
train_df[5] = train_df[5].apply(lambda x: str(x)[:8])
train_df[2] = train_df[2].apply(lambda x: str(x)[:8])
train_df[4] = train_df[4].apply(lambda x: str(x)[:8])
train_df[7] = train_df[7].apply(lambda x: str(x)[:8])
train_df[3] = train_df[3].apply(lambda x: str(x)[:8])
train_df[1] = train_df[1].apply(lambda x: str(x)[:8])

test_df[6] = test_df[6].apply(lambda x: str(x)[:8])
test_df[5] = test_df[5].apply(lambda x: str(x)[:8])
test_df[2] = test_df[2].apply(lambda x: str(x)[:8])
test_df[4] = test_df[4].apply(lambda x: str(x)[:8])
test_df[7] = test_df[7].apply(lambda x: str(x)[:8])
test_df[3] = test_df[3].apply(lambda x: str(x)[:8])
test_df[1] = test_df[1].apply(lambda x: str(x)[:8])

train_df[6] = pd.to_numeric(train_df[6], errors='coerce')
train_df[5] = pd.to_numeric(train_df[5], errors='coerce')
train_df[2] = pd.to_numeric(train_df[2], errors='coerce')
train_df[4] = pd.to_numeric(train_df[4], errors='coerce')
train_df[7] = pd.to_numeric(train_df[7], errors='coerce')
train_df[3] = pd.to_numeric(train_df[3], errors='coerce')
train_df[1] = pd.to_numeric(train_df[1], errors='coerce')

test_df[6] = pd.to_numeric(test_df[6], errors='coerce')
test_df[5] = pd.to_numeric(test_df[5], errors='coerce')
test_df[2] = pd.to_numeric(test_df[2], errors='coerce')
test_df[4] = pd.to_numeric(test_df[4], errors='coerce')
test_df[7] = pd.to_numeric(test_df[7], errors='coerce')
test_df[3] = pd.to_numeric(test_df[3], errors='coerce')
test_df[1] = pd.to_numeric(test_df[1], errors='coerce')

# 对日期进行处理
train_df[8] = train_df[6] - train_df[5]
train_df[9] = train_df[2] - train_df[5]
train_df[10] = train_df[4] - train_df[5]
train_df.drop([0, 1, 2, 3, 4, 5, 6, 7], axis=1, inplace=True)

test_df[8] = test_df[6] - test_df[5]
test_df[9] = test_df[2] - test_df[5]
test_df[10] = test_df[4] - test_df[5]
test_df.drop([0, 1, 2, 3, 4, 5, 6, 7], axis=1, inplace=True)

# 对特征进行归一化处理
scaler = MinMaxScaler()
train_df[[8, 9, 10]] = scaler.fit_transform(train_df[[8, 9, 10]])
test_df[[8, 9, 10]] = scaler.transform(test_df[[8, 9, 10]])

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(train_df.iloc[:, :-1], train_df.iloc[:, -1], test_size=0.3, random_state=0)

接下来，我们需要对特征进行处理，构建不同的特征，包括用户领取的优惠券数量、用户消费过的优惠券数量、用户与商家间的距离等。以下是代码实现：

# 构建不同的特征
def get_user_receive_count(df):
    temp = df.groupby([0]).size().reset_index(name='counts')
    return df.merge(temp, on=[0], how='left')

def get_user_consume_count(df):
    temp = df[df[5] != 'null'].groupby([0]).size().reset_index(name='counts')
    return df.merge(temp, on=[0], how='left')

def get_user_merchant_distance(df):
    temp = df.groupby([0, 3]).size().reset_index(name='counts')
    temp['counts'] = temp['counts'].apply(lambda x: 1)
    temp = temp.groupby([0])['counts'].sum().reset_index(name='distance')
    return df.merge(temp, on=[0], how='left')

X_train = get_user_receive_count(X_train)
X_train = get_user_consume_count(X_train)
X_train = get_user_merchant_distance(X_train)

X_test = get_user_receive_count(X_test)
X_test = get_user_consume_count(X_test)
X_test = get_user_merchant_distance(X_test)

test_df = get_user_receive_count(test_df)
test_df = get_user_consume_count(test_df)
test_df = get_user_merchant_distance(test_df)

X_train.drop([0, 5], axis=1, inplace=True)
X_test.drop([0, 5], axis=1, inplace=True)
test_df.drop([0, 5], axis=1, inplace=True)

最后，我们需要使用随机森林算法进行训练和预测。以下是代码实现：

# 训练和预测
rf = RandomForestClassifier(n_estimators=100, random_state=0)
rf.fit(X_train, y_train)
y_pred = rf.predict_proba(X_test)[:, 1]
score = roc_auc_score(y_test, y_pred)
print('AUC Score: {:.4f}'.format(score))

y_pred = rf.predict_proba(test_df)[:, 1]
result = pd.DataFrame({'prob': y_pred})
result.to_csv('result.csv', index=False)

这样就完成了O2O优惠券使用预测的代码实现，你可以根据自己的实际情况进行调整。