PharmaCrest Financial Analysis & Revenue Forecasting

This project presents an in-depth financial analysis of PharmaCrest, using Python to predict revenue trends. The analysis includes key financial performance indicators, including revenue, cost of production, variance, budgeting, and forecasting. This exploratory data analysis (EDA) was enhanced using ARIMA and Random Forest models to predict future revenue trends based on historical data.

Key Financial Insights:

  • Revenue Trends: Analyzed past financial data to determine trends in revenue growth and fluctuations over different periods.
  • Cost of Production vs Revenue: Examined the relationship between production costs and revenue to highlight efficiency in operations.
  • Budgeting and Forecasting: Provided detailed forecasts of future financial performance, highlighting potential opportunities and risks using ARIMA and Random Forest models.
  • Variance Analysis: Identified significant variances in budget vs actual financial data, helping pinpoint areas of improvement for PharmaCrest’s financial strategy.

Python Analysis & Techniques:

  • Data Preprocessing: Cleaned and organized the dataset for accurate analysis.
  • ARIMA Model: Applied for time series analysis to predict revenue trends, providing a linear forecast.
  • Random Forest Model: Used to predict revenue outcomes, offering more accurate results for volatile data compared to ARIMA.
  • Visualization: Created visual representations of revenue trends, cost structures, and forecast accuracy using Python's matplotlib and seaborn libraries.

Project Impact: This analysis will help PharmaCrest's management team to make data-driven decisions on budgeting, forecasting, and managing production costs. It provides actionable insights that can drive efficiency improvements, cost management strategies, and revenue optimization in the pharmaceutical business.