Ankushr785 / Food-amenities-demand-prediction

Predicting the demand of food amenities using LSTM and MLP

Food-amenities-demand-prediction

• Predicting the demand of food amenities using LSTM and 3-layer neural network.
• To run the given codes, install Keras with tensorflow backend in your IPython shell (preferably Anaconda).
• The .py file is a looping code, while the .ipynb is a test code.
• Note - The compiled results and graphs are based on the attempt4.py code, which is not automated for lag selection. The complete_code.py file contains automation for lag selection.

Business Problem

• Predicting the demand quantity of food amenities
• No input is at disposal, hence the input variables need to be forecasted and then the target variable is regressed through the forecasted input variable

Data Definition

Data Variables and Definition

• Input variables

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)

• Target Variable - ActualDemand of SKU ( = Ordered Quantity + Missed Demand)

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

Data Understanding and Processing

Outlier Treatment

• Values below 3rd percentile of the sample and above 97th percentile of the sample are converted to their respective buffers
• Only @CustomerCount and @ActualDemand are considered for outlier treatment

Summary Statistics

• Summary Stats for SKU 1

• Summary Stats on SKU 2

• Summary Stats on SKU 3

Training and Test Datasets

• The last week of the complete dataset is considered for testing while the rest of the dataset is considered for training

Function to create Data Input to model

• AvgSP

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

• SKU 1 - rmse = Rs. 1.266

• SKU 2 - rmse = Rs. 1.9

• SKU 3 - rmse = Rs. 1.5

• CustomerCount

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

• SKU 1 - 12 Customers

• SKU 2 - rmse = 50 Customers

• SKU 3 - rmse = 52 Customers

• Onion Price is known with good accuracy due to information about the lot size.

Data Modelling

Model Name

• 3-layer Neural Network using Keras Library (tensorflow backend)
• The network is made up of 3 layers:

1. Input layer

• Takes input variables and converts them into input equation
• Parameters: no. of neurons (memory blocks) = 16, activation function = linear, weight initializer = normal distribution, kernel and activity regularizer = L1 (alpha = 0.1)

2. Hidden Layer

• The processing (optimization) takes place in this layer.
• Parameters: no. of neurons = 8, activation function = linear, weight initializer = normal distribution, kernel and activity regularizer = L1 (alpha = 0.1)

3. Output Layer

• Converts the processed results into a reverse scaled output.

Model Performance

1. SKU 1 - rmse = 180 Kg

2. SKU 2 - rmse = 250 Kg

3. SKU 3 - rmse = 353 Kg