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Food-amenities-demand-prediction

Business Problem

Data Definition

Data Variables and Definition

  1. AvgSP - Average Selling Price of SKU
  2. OP - Average Selling Price of Onion
  3. CustomerCount - Total GT Customers for the given SKU ( = CustomerCount + Missed Customers)

Time Period considered - 17/03/2017 to 22/06/2017

Data Understanding and Processing

Outlier Treatment

Summary Statistics

input_var_summ

ouput_var_summ

sums_inp_carr

sums_out_carr

ridge_inp_summ

ridge_out_sums

Training and Test Datasets

Function to create Data Input to model

  1. @AvgSP is predicted using time series forecasting.
  2. Long Short-Term Memory (Recurrent Neural Network) method is used for forecasting. The forecasting problem is now considered as a supervised learning problem where the input is the value prior to the target day.
  3. LSTM is a special type of Neural Network which remembers information across long sequences to facilitate the forecasting.
  4. Forecasting results

avgsp_pred_cucum

avgsp_pred_carr

ridge_avgsp

  1. @CustomerCount is predicted using the same method as @AvgSP
  2. Forecasting Results

cc_pred_cucum

cc_pred_carr

ridge_cc_pred

Data Modelling

Model Name

  1. Input layer
  1. Hidden Layer
  1. Output Layer

Model Performance

  1. SKU 1 - rmse = 180 Kg

training_fit_cucum

pred_cucum

forecast_cucum

  1. SKU 2 - rmse = 250 Kg

train_dem_carr

dem_pred_carr

carr_dem_fore

  1. SKU 3 - rmse = 353 Kg

ridge_train_dem

ridge_dem_pred

ridge_dem_fore