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πŸ“Š Financial Variance Analysis & Forecasting

Budget vs. actual expense analysis tool with variance tracking, trend visualization, and Holt-Winters exponential smoothing for 3-month expense forecasting.

Python statsmodels License: MIT


Overview

Financial planning & analysis (FP&A) tool that compares budget allocations against actual expenses over a 12-month period, calculates month-over-month variance, and uses Holt-Winters exponential smoothing to forecast the next 3 months of expenses. Built for finance teams doing monthly close and forward-looking budget projections.

Features

  • Variance Analysis β€” Calculates budget vs. actual differences per month with trend tracking
  • Variance Change Tracking β€” Month-over-month delta in variance to spot accelerating over/underspends
  • Trend Visualization β€” Budget vs. actual expense line chart for visual pattern recognition
  • Expense Forecasting β€” Holt-Winters additive trend model projecting 3 months forward
  • Configurable Parameters β€” Smoothing level (Ξ±=0.8) and trend (Ξ²=0.2) for forecast tuning

Tech Stack

Component Technology
Language Python 3.9+
Forecasting statsmodels (Holt-Winters Exponential Smoothing)
Analytics pandas, NumPy
Visualization Matplotlib

Quick Start

git clone https://github.com/RHarmit/Financial-Variance-Analysis-Forecasting-Using-Python.git
cd Financial-Variance-Analysis-Forecasting-Using-Python
pip install pandas numpy matplotlib statsmodels
python "Financial Analysis.PY"

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Budget vs. actual variance analysis with Holt-Winters exponential smoothing for 3-month expense forecasting.

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