📅 Analysis Period: May 2023 – March 2024
📍 Business: Juls Burgers & Fruit Shakes
📈 Focus: Exploratory Data Analysis (EDA) & Revenue Forecasting
This project provides an in-depth sales analysis and forecasting for Juls Burgers & Fruit Shakes, helping the business understand key trends, optimize operations, and predict future revenue.
✅ Key Objectives:
✔️ Identify sales trends, peak seasons, and customer demand patterns.
✔️ Develop an XGBoost forecasting model for 2-month revenue predictions.
✔️ Provide data-driven recommendations for business growth.
🔹 Dataset: 25,000+ daily transactions 📋
🔹 Time Frame: May 2023 – March 2024
🔹 Features: Date, product categories, quantity sold, revenue, etc.
📌 Key Insights from EDA:
📍 Peak sales occur on weekends and payday periods.
📍 Fruit shakes contribute 40% of total revenue, especially in summer.
📍 Discounts & promotions increase sales volume by 18%, but reduce profit margins.
A machine learning-based forecasting model was implemented using XGBoost.
🔹 Algorithm Used: XGBoost Regression
🔹 Evaluation Metric: Root Mean Squared Error (RMSE)
🔹 Model Performance: ₱87.54 RMSE
🔍 Business Impact:
✔️ Forecasts help in inventory planning & demand estimation.
✔️ Owners can optimize marketing strategies for peak sales periods.
| Category | Tools Used |
|---|---|
| Programming | Python (Pandas, NumPy, Matplotlib, Seaborn) |
| Modeling | XGBoost, Scikit-learn |
| Visualization | Matplotlib, Seaborn |
| Notebook | Jupyter Notebook |
| Version Control | GitHub |
1️⃣ Clone this repository:
git clone https://github.com/yourusername/sales-analysis.git