This project focuses on analyzing historical sales data and forecasting future sales using time series techniques.
The objective is to identify trends and seasonality in sales behavior and generate forward-looking forecasts that support inventory planning, revenue targeting, and operational decision-making.
The project follows an end-to-end analytical workflow, starting from raw daily sales data and ending with exportable, business-ready sales forecasts.
- Analyze historical sales trends over time
- Identify seasonality and long-term growth patterns
- Apply time-series forecasting techniques
- Evaluate and compare baseline forecasting models
- Generate future sales forecasts for business planning
Language: Python
Environment: Google Colab (Cloud-based Jupyter Notebook)
Libraries Used:
- pandas
- numpy
- matplotlib
- seaborn
- scikit-learn
- statsmodels
Type: Synthetic but realistic daily sales dataset
Time Range: January 2021 – December 2023
Frequency: Daily
- Long-term upward trend
- Clear seasonal patterns
- Random noise
- Missing sales values
- Negative sales entries (returns / data errors)
The dataset was intentionally designed to resemble raw, real-world business data, requiring cleaning and preparation before analysis.
Key preparation steps included:
- Handling missing sales values using interpolation
- Addressing negative sales values using business logic
- Converting date column to a datetime index
- Enforcing daily time-series frequency
- Preparing data for time-based aggregation and modeling
These steps ensured the dataset was suitable for reliable time series analysis and forecasting.
Exploratory analysis focused on understanding sales behavior over time.
- Daily sales trend visualization
- Monthly and yearly sales aggregation
- Seasonal sales patterns by month
- Rolling averages to smooth short-term noise
- Sales show a consistent upward long-term trend
- Strong recurring seasonal patterns are present
- Year-over-year sales demonstrate steady growth
- Short-term volatility smooths out over longer windows
Sales data was decomposed into:
- Trend: Long-term growth component
- Seasonality: Recurring yearly patterns
- Residuals: Random noise
Decomposition confirmed that sales behavior is driven by both growth and seasonality, validating the use of seasonal-aware forecasting techniques.
Multiple baseline forecasting models were developed and compared.
- Naive Forecast
- Moving Average Forecast
- Linear Regression-based Time Forecasting
- Time-aware train-test split
- Root Mean Squared Error (RMSE) for comparison
Linear Regression demonstrated the best performance among baseline models by effectively capturing long-term trends.
Using the selected regression-based model, sales were forecasted for the next six months beyond the available historical data.
- Visualization combining historical and forecasted sales
- Exportable CSV file containing future sales projections
These forecasts can be directly used for:
- Inventory planning
- Revenue forecasting
- Operational and budgeting decisions
A business-ready sales forecast dataset was generated and exported containing:
- Forecasted monthly sales values
- Future time periods
- Clean, structured format for stakeholder use
This output mirrors real-world analytics deliverables used by finance and operations teams.
This project demonstrates how historical sales data can be transformed into actionable future insights using structured time series analysis and forecasting techniques.
By combining data cleaning, exploratory analysis, decomposition, and baseline forecasting models, the project highlights how businesses can leverage past sales behavior to support informed planning and decision-making.
- Seasonal regression models
- ARIMA / SARIMA forecasting
- Prophet-based forecasting
- Longer forecast horizons
- Integration with inventory optimization models
- Interactive dashboards (Power BI / Tableau)
The entire project was developed and executed using Google Colab, ensuring cloud-based accessibility, reproducibility, and ease of experimentation.