Walmart, a multinational retail corporation, faces challenges in managing its inventory effectively, leading to issues aligning product supply with varying consumer demand.
Walmart struggles with inventory management, causing difficulties in matching product supply with consumer demand fluctuations.
The primary objective is to develop accurate sales prediction models using fbprophet to forecast sales for the retail store over a 12-week period. This will optimize inventory levels, ensuring alignment between anticipated demand and actual supply across all outlets.
The dataset includes historical sales data, inventory levels, and relevant variables such as promotional activities, seasonal trends, and economic indicators across multiple outlets.
| Feature Name | Description |
|---|---|
| Store | Store number |
| Date | Weekly date |
| Weekly Sales | Sales by store |
| Holiday_Flag | Holiday indicator |
| Temperature | Sale day temperature |
| Fuel_Price | Fuel cost |
| CPI | Consumer Price Index |
| Unemployment | Unemployment Rate |
Pre-processing involves handling missing values, normalizing data, and encoding categorical variables.
- Check data summary and quality
- Examine basic statistics, missing values, and data types
- Ensure data integrity and cleanliness
The prophet algorithm is chosen for its effectiveness in time-series forecasting, especially in scenarios with seasonal patterns and holidays influencing sales data.
fbprophet handles various time-series forecasting challenges like seasonality, holidays, and trend changes. It offers simplicity and ease of use, suitable for quick implementation and interpretation.
- Persistence of historical sales patterns
- Relevance of external factors (e.g., promotions, economic conditions)
- Stability of consumer behavior
Evaluation involves assessing accuracy metrics specific to fbprophet such as Mean Absolute Percentage Error (MAPE) and visual inspection of forecast plots. Cross-validation techniques and train-test splits validate model performance on unseen data.
Score: 5.43%
import matplotlib.pyplot as plt
import seaborn as sns
plt.figure(figsize=(12, 3))
sns.lineplot(x='Date', y='Weekly_Sales', data=data)
plt.title('Weekly Sales Trends')
plt.show()Evaluation results provide insights into forecasting model accuracy and reliability, guiding adjustments to the algorithm or data preprocessing steps for refinement.
- Special Offers Strategy: Targeted offers for high-performing stores; tailor promotions for underperforming stores.
- Inventory Management: Adjust levels based on store sales patterns; focus on high-demand products in low-sales stores.
- Staff Allocation: Optimize based on sales trends; ensure adequate staffing during peaks.
- Product Analysis: Identify top-selling products in underperforming stores; increase stock for popular items.
Forecast sales across all stores for the next 12 weeks, considering store-wise and overall trends. Develop a holistic holiday sales strategy to maximize year-end trends.
Initiate promotions and incentives to drive revenue at the year's start. Clear holiday inventory, offer loyalty incentives, and promote winter essentials.
Explore advanced features of fbprophet, include additional external variables, and adapt the model for real-time forecasting. Continuous improvement and adaptation will enhance inventory management accuracy and responsiveness in the long term.
credit : Intellipaat dataset code : Rakhi Tulaskar