Pheezo-SQ/PalmTrade-Financial-Forecasting

Financial Forecasting Report for PalmTrade Nigeria using Python Time Series Analysis and Power BI # 📈 PalmTrade Nigeria — Financial Forecasting Report ## 📌 Project Overview A 5-year financial analysis and revenue forecasting project for PalmTrade Nigeria Ltd, a fictional Nigerian FMCG company. The project covers revenue trends, profitability analysis, cost breakdowns and a 2024 revenue forecast using Moving Average methodology. ## 🛠️ Tools & Technologies - *Python* — Pandas, Matplotlib, Seaborn - *Jupyter Notebook* — Financial EDA and forecasting - *Power BI Desktop* — Interactive financial dashboard - *Git & GitHub* — Version control ## 📊 Key Metrics (2019-2023) - Total Revenue: NGN 5.07 Billion - Total Net Profit: NGN 796 Million - Average Gross Margin: ~40% - Best Region: Lagos - Best Quarter: Q4 (festive season effect) ## 🔍 Key Findings - Revenue grew from NGN 50M to NGN 120M monthly over 5 years - COVID-19 caused 35% revenue drop in Q2-Q3 2020 - Q4 consistently outperforms all quarters (festive season) - Lagos generates 35% of total company revenue - Company fully recovered from COVID by Q1 2021 ## 📁 Files | File | Description | |------|-------------| | financial_forecasting.ipynb | Full Python analysis notebook | | palmtrade_financial.csv | Raw dataset (300 rows) | | palmtrade_financial_clean.csv | Cleaned dataset | | palmtrade_monthly_summary.csv | Monthly aggregated data | | PalmTrade_Financial_Dashboard.pbix | Power BI dashboard | | chart1_revenue_trend.png | Revenue trend analysis | | chart2_profitability.png | Profitability breakdown | | chart3_revenue_forecast.png | 2024 revenue forecast | ## 💡 Business Recommendations 1. Invest more in Lagos — consistently highest revenue region 2. Boost Q1 and Q2 campaigns to reduce seasonal dips 3. Monitor COGS closely — currently 55-65% of revenue 4. Replicate Q4 festive strategies in Q2 and Q3 5. Target 15%+ net profit margin through cost optimisation ## 🚀 How to Run 1. Clone the repository 2. Open financial_forecasting.ipynb in Jupyter Notebook 3. Run all cells sequentially 4. Open PalmTrade_Financial_Da …