Rossmann-Pharmaceuticals-Sales-Prediction

Table of Contents

• Rossmann-Pharmaceuticals-Sales-Prediction
  • Overview
  • Scenario
  • Approach
  • Project Structure
    • data:
    • models:
    • notebooks:
    • tests:
    • logs:
    • root folder
  • Installation guide

Overview

This repository is used for week 3 challenge of 10Academy.

Scenario

You work at Rossmann Pharmaceuticals as a Machine Learning Engineer. The finance team wants to forecast sales in all their stores across several cities six weeks ahead of time. Managers in individual stores rely on their years of experience as well as their personal judgement to forecast sales.

The data team identified factors such as promotions, competition, school and state holidays, seasonality, and locality as necessary for predicting the sales across the various stores.

Your job is to build and serve an end-to-end product that delivers this prediction to analysts in the finance team.

Approach

The project is divided and implemented by the following phases

• Exploration of customer purchasing behavior
• Prediction of store sales
  • Machine learning approach
  • Deep Learning approach
• Serving predictions on a web interface

Project Structure

The repository has a number of files including python scripts, jupyter notebooks and text files. Here is their structure with a brief explanation.

data:

• the folder where the dataset csv files are stored

.github:

• the folder where github actions and CML workflow is integrated

.dvc:

• the folder where dvc is managed and configured for remote data version control

models:

• the folder where model pickle files and model reference csv files are stored

notebooks:

• data_exploration.ipynb: a jupyter notebook for exploring the data
• data_cleaning.ipynb: a jupyter notebook for preprocessing the data for ML and further analysis
• deep_learning.ipynb: a jupyter notebook training an LSTM model for forecasting purpose
• ML_modeling.ipynb: a jupyter notebook training an Regression models for prediction purpose

tests:

• the folder containing unit tests for components in the scripts

root folder

• requirements.txt: a text file lsiting the projet's dependancies
• travis.yml: a configuration file for Travis CI
• app.py: entry 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.

Installation guide

git clone  https://github.com/nahomHmichael/Rossman-Pharmaceutical-Sales-Pridiction
pip install -r requirements.txt