電商用戶購買行為預測-排名48-0.23

任務：依據電子商務平平臺上真實的用戶行為記錄，利用機器學習相關技術，建立穩健的電商用戶購買行為預測模型，預測用戶下一個可能會購買的商品，

資料簡介
資料整理自一家中等化妝品在線商店公布的網上公開資料集，為該化妝品商店真實的用戶交易資訊，資料集中每一行表示一個事件，所有的事件都與商品和用戶相關，并且用戶的點擊行為之間是有時間順序的，資料集中包含了商品和用戶的多個屬性，例如商品編號、商品類別、用戶編號、事件時間等，

資料說明

主要思路

1. 對用戶id進行分組
2. 統計類別、品牌、收藏、加購物車、下單等特征，賦予合理的權重
3. 構建時間特征
4. 使用lgb的多分類模型進行訓練

主要代碼：

import gc

import pandas as pd
from sklearn.preprocessing import LabelEncoder

paths = r'E:\專案檔案\CCF\電商用戶購買行為預測'
data = pd.read_csv(f'{paths}/train.csv')
submit_example = pd.read_csv(f'{paths}/submit_example.csv')
test = pd.read_csv(f'{paths}/test.csv')

data['user_id'] = data['user_id'].astype('int32')
data['product_id'] = data['product_id'].astype('int32')
data['category_id'] = data['category_id'].astype('int32')
lbe = LabelEncoder()
data['brand'].fillna('0', inplace=True)
data['brand'] = lbe.fit_transform(data['brand'])
data['brand'] = data['brand'].astype('int32')
# data['event_time'] = pd.to_datetime(data['event_time'], format='%Y-%m-%d %H:%M:%S')
data.fillna(0, inplace=True)
gc.collect()

train_X = data
test_data = test

# 構建特征
groups = train_X.groupby('user_id')
temp = groups.size().reset_index().rename(columns={0: 'u1'})
matrix = temp
temp = groups['product_id'].agg([('u2', 'nunique')]).reset_index()
matrix = matrix.merge(temp, on='user_id', how='left')
temp = groups['category_id'].agg([('u3', 'nunique')]).reset_index()
matrix = matrix.merge(temp, on='user_id', how='left')
temp = groups['brand'].agg([('u5', 'nunique')]).reset_index()
# TODO 根據用戶購買行為去構建特征
# temp = groups['event_type'].value_counts().unstack().reset_index().rename(
#     columns={0: 'u7', 1: 'u8', 2: 'u9', 3: 'u10'})
matrix = matrix.merge(temp, on='user_id', how='left')

label_list = []
for name, group in groups:
    product_id = int(group.iloc[-1, 2])
    label_list.append([name, product_id])

train_data = matrix.merge(pd.DataFrame(label_list, columns=['user_id', 'label'], dtype=int), on='user_id', how='left')

# 構建特征
groups = test_data.groupby('user_id')
temp = groups.size().reset_index().rename(columns={0: 'u1'})
test_matrix = temp
temp = groups['product_id'].agg([('u2', 'nunique')]).reset_index()
matrix = test_matrix.merge(temp, on='user_id', how='left')
temp = groups['category_id'].agg([('u3', 'nunique')]).reset_index()
matrix = matrix.merge(temp, on='user_id', how='left')
temp = groups['brand'].agg([('u5', 'nunique')]).reset_index()
# TODO 根據用戶購買行為去構建特征
# temp = groups['event_type'].value_counts().unstack().reset_index().rename(
#     columns={0: 'u7', 1: 'u8', 2: 'u9', 3: 'u10'})
test_data = matrix.merge(temp, on='user_id', how='left')

test_data = test_data.drop(['user_id'], axis=1)

train_X, train_y = train_data.drop(['label', 'user_id'], axis=1), train_data['label']
# train_X.to_csv('train_deal.csv', index=False)
# train_y.to_csv('train_y_deal.csv', index=False)
# test_data.to_csv('test_data.csv', index=False)

# 匯入分析庫
import lightgbm as lgb

model = lgb.LGBMClassifier(
    max_depth=5,
    n_estimators=10,
)

model.fit(
    train_X,
    train_y,
    eval_metric='auc',
    eval_set=[(train_X, train_y)],
    verbose=False,
    early_stopping_rounds=5
)

prob = model.predict(test_data)

import numpy as np

np.savetxt(paths + '\\prob1.csv', prob)
submit_example['product_id'] = pd.Series(prob[:, 0])
submit_example.to_csv(paths + r'\\lgb1.csv', index=False)

耍花招湊提交的方法，直接默認買最后一條記錄，小心被封號

import pandas as pd

paths = r'E:\專案檔案\CCF\電商用戶購買行為預測'
submit_example = pd.read_csv(f'{paths}/submit_example.csv')
test = pd.read_csv(f'{paths}/test.csv')

# 構建特征
groups = test.groupby('user_id')
label_list = []
for name, group in groups:
    product_id = int(group.iloc[-1, 2])
    label_list.append([name, product_id])

submit_example = pd.DataFrame(label_list, columns=['user_id', 'product_id'])
submit_example.to_csv(paths + r'\\label_list.csv', index=False)

參考文獻，思路都差不多，主要看你怎么構造特征了，加油吧少年

天池新人實戰賽之[離線賽]_baseline_lgb

天池新人賽-Repeat Buyers Prediction-Challenge the Baseline-排名167