PharmaCrest Financial Analysis & Revenue Forecasting

Project overview

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:

Approach & tools

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.