Sales Forecasting Project

📌 Overview

This project focuses on forecasting future sales using historical time series data.

🎯 Objective

To analyze sales trends and build a forecasting model that predicts future sales performance.

🛠️ Tools & Technologies

• Python (Pandas, NumPy)
• Matplotlib
• Statsmodels (Exponential Smoothing)
• Jupyter Notebook

📊 Key Steps

1. Data Preparation
2. Time Series Analysis
3. Trend and Seasonality Identification
4. Model Building (Exponential Smoothing)
5. Model Evaluation (MAE, RMSE)
6. Future Forecasting

📈 Results

• MAE: ~86.6
• RMSE: ~93.8
• Model successfully captured trend and seasonal patterns

🧠 Key Insights

• Sales show a clear upward trend over time
• Seasonal patterns are present in the data
• Forecasting can support planning and decision-making

🚀 Future Improvements

• Try ARIMA or Prophet models
• Include external variables (e.g., promotions, holidays)
• Improve accuracy with feature engineering

📁 Project Structure

sales-forecasting/
├── data/
├── notebooks/
├── src/
└── requirements.txt

👤 Author

Hamzat Afe Isede

🔗 Related Projects

• Customer Churn Prediction
• Customer Segmentation
• Sales Dashboard (Power BI)