Food Demand Forecasting

A supply chain use case Machine Learning project. All the resources are cummulative collection of the work done by Aditya Komaravolu.

Food Demand Forecasting

This project aims at predicting number of orders by the user based on the place of residence, region code, centre of dispatch and of course the user's past data. Completely based on Supply Chain Management.

To run this repo you will require following libraries(along with their versions):

absl-py==0.12.0
astunparse==1.6.3
backcall==0.2.0
cachetools==4.2.2
conda==4.3.16
cycler==0.10.0
decorator==5.0.9
flatbuffers==1.12
gast==0.4.0
google-auth==1.30.1
google-auth-oauthlib==0.4.4
google-pasta==0.2.0
grpcio==1.34.1
h5py==3.1.0
ipykernel==5.5.5
ipython==7.24.1
ipython-genutils==0.2.0
ipywidgets==7.6.3
jedi==0.18.0
joblib==1.0.1
jupyter==1.0.0
jupyter-client==6.1.12
jupyter-console==6.4.0
jupyter-core==4.7.1
jupyterlab-widgets==1.0.0
jupyterthemes==0.20.0
keras-nightly==2.5.0.dev2021032900
Keras-Preprocessing==1.1.2
kiwisolver==1.3.1
lesscpy==0.14.0
Markdown==3.3.4
matplotlib==3.4.2
matplotlib-inline==0.1.2
numpy==1.20.3
oauthlib==3.1.1
opt-einsum==3.3.0
pandas==1.2.4
parso==0.8.2
pexpect==4.8.0
pickleshare==0.7.5
Pillow==8.2.0
ply==3.11
prompt-toolkit==3.0.18
protobuf==3.17.2
ptyprocess==0.7.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
pycosat==0.6.3
Pygments==2.9.0
PyParse==1.1.7
python-dateutil==2.8.1
pytz==2021.1
pyzmq==22.1.0
qtconsole==5.1.0
QtPy==1.9.0
requests-oauthlib==1.3.0
rsa==4.7.2
ruamel.yaml==0.17.7
ruamel.yaml.clib==0.2.2
scikit-learn==0.24.2
scipy==1.6.3
seaborn==0.11.1
six==1.15.0
tensorboard==2.5.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.0
tensorflow==2.5.0
tensorflow-estimator==2.5.0
termcolor==1.1.0
threadpoolctl==2.1.0
tornado==6.1
traitlets==5.0.5
typing-extensions==3.7.4.3
wcwidth==0.2.5
Werkzeug==2.0.1
widgetsnbextension

[04/06/2021]

Project requires 3 models for the given three parts.

• Meal ID and Category of Food

• Fulfilment Centers for a given region

• Model to predict the demand forecast

[05/06/2021]

• Decided to go first model with DecisionTreeClassifier for predicting type of food requirement for the user based on meal_id.

• target files --> _meal_info.csv_ and meal_info.ipynb.

• Built a DecisionTreeClassifier which successfully predicts the type of category and cuisine the user is going to pick based on the user's meal_id.

[09/06/2021]

• Decided to go second model with DecisionTreeRegressor for predicting region_code, area_code, dispatch_warehouse(A,B and C) and area of operation based on center_id.

• target files --> _fulfilment_center_info.csv_ and _fulfilment_center_info.ipynb.

• Built a DecisionTreeRegressor which successfully predicts the result with above requirements.

Copyright (C) 2020-2021 by Aditya Komaravolu.

This Repo contains all my projects done in the area of Machine Learning.

Machine Learning is free open source repository for self-learning: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This repo is public in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with Machine Learning Repo. If not, see https://www.gnu.org/licenses/.