Versão em português (BR)
Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied.
Main:
- Analyze the given database and create insights about the products supplied, the stores, business sales strategies, etc.
- Develop a machine learning mode capable of forecasting sales for each store in up to six weeks.
Extra:
- Create a telegram bot that reports sales forecasts.
Business Knowledge
Deep study of features...
Data Preprocessing
Dealing with missing, duplicated and bad values, fixing data types, feature engineering, data inputation...
Exploratory Data Analysis
Descriptive statistics, hypothesis mental map, univariate, bivariate and multivariate analysis.
Data Preparation
Normalization, Standardization, Encoding, Outlines.
Machine Learning Model
SARIMAX, Random Forest Regressor, XGBoost Regressor.
Feature Selection
Filter method, Embeeded methods, Wrapper method(Boruta).
Hyperparameters Tunning
Gridsearch Cross Validation.
Deploy
Rossmann API on cloud.
Project Presentation
Business Avaliation
Model Performace
Languages: Python
IDE: Visual Studio Code, Jupyter Notebook
Libraries: Pandas, Matplotlib, Seaborn, Sklearn, statsmodel
Frameworks: Flask
Deploy: Heroku
Methodology: CRISP-DM
