(Model Deployment) Predict-Future-Sales
Predict total sales for every item and shop for the next month, from a time-series dataset consisting of daily sales data
Model deployed for predictions on https://predict-future-sales.herokuapp.com/
Columns:
- items columns : (‘item_name’, ‘item_id’, ‘item_category_id’)
- sales columns : (‘date’, ‘date_block_num’, ‘shop_id’, ‘item_id’, ‘item_price’, ‘item_cnt_day’)
- item_categories columns : (‘item_category_name’, ‘item_category_id’)
- shops columns : (‘shop_name’, ‘shop_id’)
- test columns : (‘ID’, ‘shop_id’, ‘item_id’)
item_cnt_month:
sales.groupby(['date_block_num', 'shop_id', 'item_id']).item_cnt_day.sum()
turns daily sales data into monthly data for every shop/item pair
sales1(oct_as_pred)/ sales0.1 notebook:
- using last month data (October) as predictions for next month data (November)
- Score, root mean squared error (RMSE) => 1.16777
‘sales modified data csv’/ first half of sales0.15 notebook:
- Create a grid of shop_ids and item_ids for different date_block_num with corresponding item_cnt_month
‘sales modified data csv(mean enc)’/ second half of sales0.15 notebook:
- Add target mean encoding feature to the data
sales0.2-meanenc:
- Create target mean encoding feature with CV loop regularization using KFold
- item_id_target_mean = all_data.iloc[rest].groupby('item_id').target.mean()
- all_data.loc[all_data.index[curr],'item_target_enc'] = all_data['item_id'].map(item_id_target_mean)
sales1-grid-itmcat-target-meanenc-lag:
- Remove Outliers
- Duplicate shops
- City name from shop names
- Types and Subtypes from categories of items
- Revenue ->
- sales['revenue'] = sales['item_price'] * sales['item_cnt_day']
- Target variables ->
- Add sum of sales in a month for shops (target_shop)
- s = sales.groupby(['date_block_num', 'shop_id'], as_index=False).item_cnt_day.sum()
- s = s.rename(columns={'item_cnt_day' : 'target_shop'})
- Delta features ->
- Chnage in particular value over a period (month)
- Average item price
- Shop revenue trend
- Mean/Target Encoding of all id variables
- Lag Features, previous values over different periods (1, 2, 3, 6, ... months before)
- division between train, val/dev sets
- and all the new features in the test set too.
sales2-exttr-xgb:
- First used XGBoost.XGBRegressor (ran out of memory)
- Switched to ExtraTreesRegressor, with Mean Squared Error as metric
- Next used Iterative Increment training or Batch training with XGBoost with rmse (to get around limited memory problem)
- Sticked to 2 batches, to keep training/convergence faster and under memory limits
- shop_id is highly important when using XGBoost, but not very important when using ExtraTreesRegressor
sales3-model_cnt:
- Script used to bypass limited memory and time
- by training, saving and re-training the models
- lead to overfitting could not increase the complexity of the models
- and could not add the features dropped earlier because limited processing memory, while preparing the data
Github Repository - Predict-Future-Sales
Feature importance for XGBoost:
Feature importance for ExtraTreesRegressor:
