The finance team of Rossmann pharmaceuticals 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 forcast sales. So this project serves an end-to-end product that delivers this prediction to analysts in the finance team.
1. Exploration of customer purchasing behaviour
Exploratory data analysis is the lifeblood of every meaningful machine learning project. It helps us unravel the nature of the data and sometimes informs how you go about modeling. A careful exploration of the data encapsulates checking all available features, checking their interactions and correlation as well as their variability with respect to the target.
In this task, We explore the behaviour of customers in the various stores. Our goal is to check how some measures such as promos and opening of new stores affect purchasing behavior.
To achieve this goal, we need to first clean the data. The data cleaning process will involve building pipelines to detect and handle outlier and missing data. This is particularly important because you don’t want to skew our analysis. So we tried to clean the data in the preProcessing.ipynb python notebook.
After cleaning the data, visualizing various features and interactions is necessary for clearly communicating our findings. It is a powerful tool in the data science toolbox. Communicate the findings below via the necessary plots. These plots are presented in the exploration.ipynb python notebook.
2. Prediction of store sales
Machine learning approach Deep Learning approach
3. Serving predictions on a web interface
- git clone https://github.com/Amanuel3065/pharmaceutical_sales_prediction
- cd pharmaceutical_sales_prediction
- pip install -r requirements.txt **