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GitHub / Ibrahim-I-Babana / Favorita_Stores_Sales_Forecasting

The primary aim of this machine learning project is to develop a precise predictive model for forecasting grocery store sales at Favorita stores in Ecuador. The objective is to accurately predict sales for numerous product families, incorporating factors such as dates, store information, product details, and promotional activities.

JSON API: https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ibrahim-I-Babana%2FFavorita_Stores_Sales_Forecasting

Stars: 0
Forks: 0
Open Issues: 0

License: mit
Language: Jupyter Notebook
Repo Size: 57.2 MB
Dependencies: 172

Created: 2 months ago
Updated: 24 days ago
Last pushed: 24 days ago
Last synced: 24 days ago

Files

Readme

Dependencies

requirements.txt pypi

Babel2.12.1 *
Cython3.0.8 *
DateTime5.4 *
ImageHash4.3.1 *
Jinja23.1.2 *
Markdown3.5 *
MarkupSafe2.1.3 *
PackageVersion *
Pillow10.0.0 *
PyWavelets1.5.0 *
PyYAML6.0.1 *
Pygments2.16.1 *
QtPy2.4.0 *
SQLAlchemy2.0.21 *
Send2Trash1.8.2 *
annotated-types0.6.0 *
anyio3.7.1 *
argon2-cffi-bindings21.2.0 *
argon2-cffi23.1.0 *
arrow1.2.3 *
asttokens2.2.1 *
async-lru2.0.4 *
attrs23.1.0 *
backcall0.2.0 *
beautifulsoup44.12.2 *
bleach6.0.0 *
catboost1.2.3 *
category-encoders2.6.3 *
certifi2023.7.22 *
cffi1.15.1 *
charset-normalizer3.2.0 *
colorama0.4.6 *
comm0.1.4 *
contourpy1.1.0 *
currency.converter0.5.5 *
cycler0.11.0 *
dacite1.8.1 *
debugpy1.6.7.post1 *
decorator5.1.1 *
defusedxml0.7.1 *
distlib0.3.8 *
et-xmlfile1.1.0 *
exceptiongroup1.1.3 *
executing1.2.0 *
fastjsonschema2.18.0 *
filelock3.13.1 *
fonttools4.42.1 *
forex-python1.8 *
fqdn1.5.1 *
git-filter-repo2.38.0 *
graphviz0.20.3 *
greenlet2.0.2 *
htmlmin0.1.12 *
idna3.4 *
imbalanced-learn0.12.0 *
iniconfig2.0.0 *
ipykernel6.25.1 *
ipython-genutils0.2.0 *
ipython8.14.0 *
ipywidgets8.1.1 *
isoduration20.11.0 *
jedi0.19.0 *
joblib1.3.2 *
json50.9.14 *
jsonpointer2.4 *
jsonschema-specifications2023.7.1 *
jsonschema4.19.0 *
jupyter-console6.6.3 *
jupyter-events0.7.0 *
jupyter-lsp2.2.0 *
jupyter1.0.0 *
jupyter_client8.3.0 *
jupyter_core5.3.1 *
jupyter_server2.7.2 *
jupyter_server_terminals0.4.4 *
jupyterlab-pygments0.2.2 *
jupyterlab-widgets3.0.9 *
jupyterlab4.0.5 *
jupyterlab_server2.24.0 *
kiwisolver1.4.5 *
llvmlite0.41.1 *
lxml4.9.3 *
matplotlib-inline0.1.6 *
matplotlib3.8.0 *
mistune3.0.1 *
multimethod1.10 *
mysql-connector-python8.1.0 *
nbclient0.8.0 *
nbconvert7.7.4 *
nbformat5.9.2 *
nest-asyncio1.5.7 *
networkx3.2.1 *
notebook7.0.4 *
notebook_shim0.2.3 *
numba0.58.1 *
numpy1.25.2 *
openpyxl3.1.2 *
overrides7.4.0 *
packaging23.1 *
pandas-profiling3.6.6 *
pandas2.1.4 *
pandocfilters1.5.0 *
parso0.8.3 *
patsy0.5.3 *
phik0.12.3 *
pickleshare0.7.5 *
pip24.0 *
platformdirs3.10.0 *
plotly5.18.0 *
pluggy1.3.0 *
pmdarima2.0.4 *
prometheus-client0.17.1 *
prompt-toolkit3.0.39 *
protobuf4.21.12 *
psutil5.9.5 *
pure-eval0.2.2 *
pycparser2.21 *
pydantic-settings2.1.0 *
pydantic2.5.2 *
pydantic_core2.14.5 *
pyodbc5.0.1 *
pyparsing3.1.1 *
pytest7.4.3 *
python-dateutil2.8.2 *
python-dotenv1.0.0 *
python-json-logger2.0.7 *
pytz2023.3.post1 *
pywin32306 *
pywinpty2.0.11 *
pyzmq25.1.1 *
qtconsole5.4.4 *
referencing0.30.2 *
requests2.31.0 *
rfc3339-validator0.1.4 *
rfc3986-validator0.1.1 *
rpds-py0.9.2 *
scikit-learn1.4.1.post1 *
scipy1.11.3 *
seaborn0.12.2 *
simplejson3.19.2 *
six1.16.0 *
sklearn0.0.post9 *
sniffio1.3.0 *
soupsieve2.4.1 *
stack-data0.6.2 *
statsmodels0.14.0 *
tangled-up-in-unicode0.2.0 *
tenacity8.2.3 *
terminado0.17.1 *
threadpoolctl3.2.0 *
tinycss21.2.1 *
tomli2.0.1 *
tornado6.3.3 *
tqdm4.66.1 *
traitlets5.9.0 *
typeguard4.1.5 *
typing_extensions4.7.1 *
tzdata2023.3 *
uri-template1.3.0 *
urllib32.0.4 *
virtualenv20.25.1 *
visions0.7.5 *
wcwidth0.2.6 *
webcolors1.13 *
webencodings0.5.1 *
websocket-client1.6.1 *
widgetsnbextension4.0.9 *
wordcloud1.9.2 *
xgboost2.0.3 *
xlrd2.0.1 *
ydata-profiling4.6.3 *
zope.interface6.1 *