Sales Prediction and Recommendation System

To build an intelligent and predictive sales analytics system, we implemented the following approach: 

1. Data Collection & Preprocessing:
   – Aggregated historical sales data from multiple sources and stored it in Snowflake for structured analysis. 
   
   – Cleaned and normalized datasets to ensure high-quality inputs for machine learning models. 
   
   
2. Predictive Modeling:
   –  Developed machine learning models using XGBoost and Scikit-learn to analyze customer purchase behavior and forecast future sales trends. 
   
   – Applied feature engineering techniques to enhance model accuracy in identifying upselling and down-selling opportunities. 
   
   
3. Sales Recommendations Engine:
   –  Generated data-driven recommendations for sales teams, including customer-specific product suggestions and promotional strategies. 
   
   – Identified high-value customers and suggested personalized offers based on purchase history. 
   
   
4. Visualization & Decision Support:
   – Built Power BI dashboards for real-time insights into sales performance and customer purchasing trends. 
   
   – Enabled sales managers to track key metrics, adjust strategies dynamically, and enhance decision-making.