Pharmaceutical Sales prediction across multiple stores

• Using Machine Learning and Deep Learning approaches to predict Pharmaceutical store(s) sales depending on factors such as promotions, competition, school and state holidays, seasonality, and locality as necessary for predicting the sales across the various stores

Part 01. Exploration of customer purchasing behavior

• The EDA is done in 'Exploration_of_customer_purchasing_behavior.ipynb' in notebook folder

Part 02. Prediction of Pharmaceutical store Sales

• Data Preprocessing,
• Building models with sklearn pipelines,
• Choosing a loss function,
• Post Prediction analysis,
• Serialize models,
• Building model with deep learning, and
• Using MLFlow to serve the prediction

This part is done in 'Exploration_of_customer_purchasing_behavior.ipynb' in notebook folder

Part 03. Building model with Deep Learning Using Tensorflow

• This part is done in 'Deep_Learning.ipynb' in notebook folder

Part 04. The Dashboard for ML Prediction

• This part is done in 'app.py' file in main directory

Part 05. The MLflow

• This part is done in 'scripts/app.py' file