Sales-Predict-With-LSTM
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Incalos
This project involves using multi-layer LSTMs to predict the sales problem.
trafficstars
Sales Predict With LSTM
This project involves using multi-layer LSTMs to predict the sales problem.
1. Dataset Description
| Column | Type | Meaning |
|---|---|---|
| 日期 | date | time of data recording |
| 浏览量 | int | the number of times users view the page on the e-commerce platform |
| 访客数 | int | the number of users to e-commerce platform pages |
| 人均浏览量 | float | the average number of times every user views a page on an e-commerce platform in a day |
| 平均停留时间 | float | the average time spent by users on the page |
| 跳失率 | float | the proportion of visits where users enter through the corresponding portal and leave after visiting only one page to the total number of visits to that page |
| 成交客户数 | int | the number of customers who successfully paid |
| 成交单量 | int | the number of orders successfully paid |
| 成交金额 | int | the total amount of successful payments |
| 客单价 | float | the average amount of goods purchased per user |
| 成交商品件数 | int | the number of goods successfully paid for |
| 下单客户数 | int | the number of customers who have placed orders |
| 下单单量 | int | the number of orders placed |
| 下单金额 | int | the total amount of orders placed |
| 下单商品件数 | int | the number of goods ordered |
2. Prepare configuration file
(1) Dataset parameters
- feature_columns :
columns used as features in the csv dataset, with columns numbered 0, 1, 2,··· - label_columns :
columns used as labels in the csv dataset, with columns numbered 0, 1, 2,··· - predict_day :
predict how many days in the future
(2) Network parameters
- input_size :
the size of input layer, that is, the number of columns used as features - output_size :
the size of output layer, that is, the number of columns used as labels - hidden_size :
the size of hidden layer - lstm_layers :
the number of layers of lstm - dropout_rate :
dropout probability - time_step :
how many days before to predict the next day
(3) Training parameters
- do_train :
whether to train the model - do_predict :
whether the model is used for prediction - add_train :
whether to continue training on the trained weights - shuffle_train_data :
whether to randomly disrupt the training data - use_cuda :
whether to use GPU training - train_data_rate :
the ratio of training data to total data - valid_data_rate :
the ratio of validation data to training_data - batch_size :
the number of samples passed to the model for training in a epoch - learning_rate :
learning rate - epoch :
the number of times the model is trained - patience :
how many epochs to train and stop if the validation set does not improve - random_seed :
random seed, guaranteed reproducible - do_continue_train :
take the final state of the previous training as the next init state for each training
(4) Training mode
- debug_mode :
In debugging mode, it is to run through the code and pursue speed - debug_num :
debugging with only debug_num pieces of data
(5) Path Parameters
- train_data_path :
dataset save path - model_save_path :
model weights save path - figure_save_path :
prediction result save path - log_save_path :
training log save path - do_log_print_to_screen :
whether to display the log and training process on the screen - do_log_save_to_file :
whether to record the config and training process - do_figure_save :
whether to save the prediction result image - do_train_visualized :
training loss visualization
3. Display of operation results
-
Prediction of the number of items ordered
-
Prediction of transaction amount
Incalos