Retail Store Sales Forecasting and Business Intelligence Analysis

Introduction

This project analyzes retail sales forecasting data across stores and departments. The report explores weekly sales trends, holiday impact, markdown effectiveness, and forecasting insights using multiple data visualization techniques.

Import Data

## Rows: 156000 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl  (4): store_id, department, weekly_sales, is_holiday
## date (1): date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 50 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): store_type, region
## dbl (2): store_id, store_size
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 7800 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (2): holiday_name, season
## dbl  (11): store_id, temperature, fuel_price, markdown_1, markdown_2, markdo...
## date  (1): date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Figure 1: Weekly Sales Trend

Figure 2: Holiday vs Non-Holiday Sales

Figure 3: Markdown Impact on Sales

Figure 4: Sales Distribution by Store Type

Figure 5: Monthly Sales Heatmap

## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by month and store_type.
## ℹ Output is grouped by month.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(month, store_type))` for per-operation grouping
##   (`?dplyr::dplyr_by`) instead.

Figure 6: Weekly Sales Distribution

Figure 7: Department-wise Sales Trends

## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by date and department.
## ℹ Output is grouped by date.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(date, department))` for per-operation grouping
##   (`?dplyr::dplyr_by`) instead.

Figure 8: Interactive Sales Trend Visualization

Conclusion

The visualizations demonstrate how retail sales are influenced by seasonal demand, holidays, markdown promotions, and store characteristics. Interactive and static visualizations together provide meaningful business intelligence insights that can support retail forecasting and merchandising decisions.