This project focuses on forecasting future sales using historical data and machine learning techniques. The goal is to provide actionable insights to support data-driven business decisions, such as inventory planning, revenue forecasting, and marketing strategies.
Analyze historical sales data to understand trends and seasonality Build and evaluate forecasting models (Linear Regression, etc.) Visualize key metrics and predictions Provide reproducible and scalable code for forecasting
The dataset includes historical sales records across multiple time periods. Key features:
Date-wise sales data Product/category-level segmentation
Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn)
Jupyter Notebook for analysis and visualization
Git & GitHub for version control and collaboration
- Python 🐍
- Pandas
- Matplotlib
Trend & Seasonality Plots
Forecasting Charts
Error Metrics Comparison
The model achieved accurate short-term forecasts with low error margins, demonstrating its value in real-world sales planning scenarios.