Rossmann Pharmaceutical Sales Prediction

Table of Contents

• Rossmann-Pharmaceuticals-Sales-Prediction

  • Introduction
  • Project Structure
    • data
    • models
    • notebooks
    • scripts
    • sql
    • tests
    • logs
    • root folder
  • Installation guide

Introduction

Pharmaceutical Sales prediction: The finance department or Rosmann Pharmaceuticals wishes to anticipate sales six weeks ahead of time in all of their stores across many cities.This project has developed an end-to-end solution that will offer the finance team's analysts with this projection.

Project Structure

images:

• images/ the folder where all snapshot for the project are stored.

logs:

• logs/ the folder where script logs are stored.

mlruns:

• mlruns/0/ the folder that contain auto generated mlflow runs.

data:

• train_store.csv.dvc the folder where the dataset versioned csv files are stored.

.dvc:

• .dvc/: the folder where dvc is configured for data version control.

.github:

• .github/: the folder where github actions and CML workflow is integrated.

.vscode:

• .vscode/: the folder where local path fix are stored.

modles:

• 28-05-2022-00-05-32-52.91%.pkl: the folder where model pickle files are stored.

notebooks:

• data_preProcessing.ipynb: a jupyter notebook for preprocessing the data.
• data_exploration.ipynb: a jupyter notebook for exploring the data.
• ml_preProcess: a jupyter notebook for preprocessing the data for ml analysis.
• ml_model: a jupyter notebook training an Regression models for prediction purpose.
• time_series_ltsm.ipynb: a jupyter notebook training an LSTM model for forecasting purpose.

scripts:

• applications/: folder where dashboard design are stored.
• web-css/: folder where style sheet are stored - index.html - styles.css
• data_cleaner.py: a python script for cleaning pandas dataframes.
• data_preProcessing.py: a python script for accessing information data from a pandas dataframe.
• ltsm_model: a python script for model manipulation.
• data_manipulator.py: a python script for manipulating dataframes.
• data_exploration.py: a python script for plotting dataframes.
• multiapp.py: a python script for creating a multipaged streamlit app.
• log_help.py: a python script that creates python based logger.

tests:

• tests/: the folder containing unit tests for the scripts.

sql:

• sql/: the folder containing database table and mysql-python manipulator script.

root folder

• train.py: holds cml report and model metrics.
• results.txt: contains cml pr reports.
• requirements.txt: a text file lsiting the projet's dependancies.
• .travis.yml: a configuration file for Travis CI for unit test.
• app.py: main file for the streamlit application.
• setup.py: a configuration file for installing the scripts as a package.
• README.md: Markdown text with a brief explanation of the project and the repository structure.
• Dockerfile: build users can create an automated build that executes several command-line instructions in a container.

Installation guide

git clone https://github.com/Maelaf/Pharmaceutical-Sales-prediction-across-multiple-stores
cd Pharmaceutical-Sales-prediction-across-mulitple-stores
sudo python3 setup.py install