Sales Trend Analysis with Facebook Prophet and Machine Learning 

This project leverages Facebook Prophet to forecast daily retail sales by analyzing historical data from a network of over 1,100 stores. The aim is to create an accurate, scalable model that considers seasonality, holidays, and promotional impacts on sales. This predictive tool will help the sales department make data-driven decisions, optimize inventory, and anticipate future sales trends 

Historical Sales Trends Analysis

• Visual Insight: The Historical Sales Trends Plot shows the cumulative daily sales over a period. This plot reveals any overall upward or downward trends in sales, seasonality, and significant fluctuations over time.

• Interpretation: A steady upward trend in the plot suggests growing sales, indicating positive business performance. In contrast, significant dips or downward trends could signal potential issues, such as declining demand or increased competition. Identifying these trends helps in setting benchmarks for future sales projections.

2. Trend, Seasonality, and Residual Decomposition

• Visual Insight: The Decomposition Plot breaks down the sales data into three components: trend, seasonality, and residual.

  • Trend Component: Represents the underlying direction in sales over time.

  • Seasonal Component: Illustrates recurring patterns or fluctuations within the data, such as weekly or monthly peaks and troughs.

  • Residual Component: Shows what’s left after removing the trend and seasonality, highlighting random fluctuations or anomalies.

• Interpretation: The trend component confirms the general direction of sales, whether increasing or decreasing. The seasonal component provides insights into predictable patterns, such as increased sales during holidays or weekends. Any significant spikes or dips in the residual component may point to one-time events, such as sudden promotions or unexpected store closures.

3. Prophet Forecast Analysis

• Visual Insight: The Sales Forecast with Prophet Plot combines historical data with the forecasted sales. The shaded region around the forecast line represents the model’s confidence interval, indicating the range within which future sales are expected to fall.

• Interpretation: The forecast suggests expected sales over the future period, considering seasonal trends and past patterns. The confidence interval helps quantify uncertainty, with wider intervals indicating less certainty in the forecast. If the forecasted sales continue along an upward trend, it signals sustained growth potential. Conversely, flat or declining forecasts could indicate potential challenges, requiring strategic adjustments.

4. Weekly Seasonality Analysis

• Visual Insight: The Weekly Seasonality Plot captures daily sales fluctuations across a typical week. Peaks and valleys in this plot reveal which days generally experience higher or lower sales.

• Interpretation: For example, if weekends show significant sales increases, it suggests that weekend shoppers contribute heavily to sales. Conversely, dips on weekdays could indicate lower in-store traffic during workdays. Such insights help optimize marketing efforts, staffing, and promotions on specific days to enhance sales performance.

5. Yearly Seasonality Analysis

• Visual Insight: The Yearly Seasonality Plot showcases how sales patterns vary throughout the year, indicating the months or seasons that typically drive higher or lower sales volumes.

• Interpretation: For instance, spikes in sales during December could be attributed to holiday shopping, while mid-year dips may signal slower periods. Understanding these patterns aids in inventory planning and promotional strategies, ensuring the right stock levels and targeted marketing during peak seasons

The project delivers a robust predictive model that provides daily sales forecasts, complete with visualizations of expected trends, seasonal effects, and confidence intervals. This tool equips the sales team with insights for effective inventory management, promotional planning, and overall sales strategy enhancement 

Click here to visit the GitHub repository for more details: GitHub Repository