Accurate sales forecasting enables a business organization to make decisions related to inventory management, production planning, and marketing strategies. The traditional sales forecasting technique usually fails to capture the complexities of trends in the market and consumer behavior.
Therefore, this project proposes a predictive sales analytics model that will take historical sales data as input to forecast future sales, helping a business improve its accuracy of sales forecasting, optimize resources, and enhance overall sales performance.
Week 1-2: Initial Planning and Requirement Analysis
Define the objectives of the project, its scope, and high-level requirements
Gather necessary data and resources Week 3-4: Data Collection and Preprocessing Phase
Gathering and preprocessing historical sales data
Extracting features from this data and cleaning it Week 5-6: Model Development Phase
Develop and train machine learning models for sales prediction.
Test accuracy, performance on test data Week 7-8: System Implementation Phase
Develop a predictive sales analytics solution and implement it
Integrate the user-friendly interface with the model
Test and improve the system according to the performance metrics and feedback obtained Week 9-10: System Integration and Testing Phase
Extensive testing for an accurate forecast of sales
o System performance monitoring and user interactions o Integrate continuous improvement through analytics and user feedback Week 11-12: Final Evaluation and Reporting Phase
Final evaluation and validation of the predictive sale’s analytical solution
Compilation of the final project report and documentation
Presentations of the individual reports by students
