Product Sales at Store- Forecasting and Analytical Modeling Using ARIMA, SARIMA, and Prophet

Data Source Kaggle Stores Sales Data:

• Historical daily sales figures for a network of stores, including attributes like date, store ID, and sales value.

Methodology

1. Data Preprocessing Data cleaning:

   Handled missing values, detected and handled outliers.

Feature engineering: Extracted date-based features (month, day of week, holiday flags), and aggregated sales for trend and seasonality analysis.

Split data into train and test sets for validation.

2. Model Implementation :

   ARIMA (AutoRegressive Integrated Moving Average): Modeled linear trends in time series, suitable for simple seasonality and autocorrelation.

   SARIMA (Seasonal ARIMA): Extended ARIMA to capture seasonal effects inherent in retail sales data. Prophet (by Facebook): Flexible model to fit complex seasonality, trends, and event-based effects (e.g., holidays).

   Hyperparameter tuning for each model to optimize accuracy.

3. Model Evaluation Metric: Root Mean Squared Log Error (RMSLE) to evaluate prediction accuracy and penalize large, asymmetric errors common in sales data.

   Compared all models on test data for fairness.

   
   

Results

1. Performance: Each model’s RMSLE score and qualitative insights into model suitability (e.g., Prophet best for multiple seasonal patterns).

2. Insights: Identified key factors impacting sales trends (e.g., seasonality, holidays), helping inform sales strategies and forecasting practices.

   
   

Business Impact

1. Actionable Insights: Forecasts can support sales planning, resource allocation, and promotional strategies, directly improving revenue and reducing overstock/stockouts.

2. Master Data Relevance: Methodology for data cleansing, validation, and metric-based evaluation aligns with Master Data Management (MDM) best practices—ensuring data accuracy and consistency.

Deliverables

1. Code Repository: Well-documented notebooks/scripts for all models.

2. Evaluation Report: Summarized results, model comparisons, and business implications.

3. Presentation Visuals: Forecast plots, error metrics, and process flows.

4. Documentation: Step-by-step user guide for data loading, model training, and evaluation.

Extensions & Scalability

1. Can be adapted for different stores, regions, or product categories.

2. Models and pipeline can be integrated with ERP/CRM systems for real-time business decision support.

Technical Stack

1. Python Libraries: pandas, numpy, matplotlib, statsmodels, fbprophet

Tools: Jupyter Notebook

Summary:

This project showcases end-to-end data preparation, modeling, and analytics in a sales context, highly relevant for roles involving customer data migration, data governance, and analytics-driven decision support in the real world