(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

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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

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Feature importance for XGBoost:

Feature importance for ExtraTreesRegressor: