Pharmaceutical Sales Prediction

Overview

I developed a comprehensive sales forecasting system for Rossmann pharmaceutical stores, enabling accurate prediction of sales six weeks ahead across multiple locations. This solution addresses the critical business need for precise financial planning and inventory management in the pharmaceutical retail sector.

Key Features

• Multi-store Forecasting: Accurately predicts sales across various store locations, accounting for store-specific characteristics and seasonal patterns
• Dual Modeling Approach: Leverages both traditional machine learning (Random Forest Regression) and deep learning (LSTM neural networks) techniques for robust predictions
• Feature Engineering: Comprehensive transformation of temporal data, promotional information, and store characteristics into predictive signals
• MLflow Integration: End-to-end tracking of model performance, parameters, and artifacts for reproducibility and deployment
• Interactive Dashboard: Streamlit-based web interface for exploring historical sales data and generating forecasts on demand

Technical Implementation

• Data Pipeline: Automated data cleaning and feature extraction using pandas and DVC for version control
• Machine Learning: Ensemble methods including Random Forest Regression for tabular data modeling
• Deep Learning: Time series forecasting using LSTM neural networks implemented with TensorFlow/Keras
• Model Orchestration: ML experimentation and tracking with MLflow
• Containerization: Docker-based deployment for consistent environment across development and production
• Web Interface: Interactive visualization and forecasting dashboard using Streamlit

Business Impact

This forecasting system enables pharmaceutical retail management to optimize inventory levels, staff scheduling, and financial planning. By accurately predicting sales six weeks in advance, the system directly contributes to:

• Reduced inventory carrying costs through precise stock management
• Improved cash flow planning through accurate revenue forecasting
• Enhanced promotion effectiveness through quantitative impact analysis
• Data-driven decision making for store operations and resource allocation