In the highly competitive e-commerce landscape, pricing strategy plays a crucial role in market success. Traditional static pricing fails to adapt dynamically to changing market conditions.
This project aims to develop a machine learning model that dynamically adjusts product prices in real-time based on a data-driven approach. By leveraging market insights, the system ensures optimal pricing, maximizing competitiveness and profitability regardless of market fluctuations.
Week 1-2: Data Collection and Preprocessing
Activities: Gather historical data, clean and preprocess data, and define features.
Deliverables: Preprocessed dataset ready for modeling.
Week 3-4: Model Development
Activities: Develop and train machine learning models and perform hyperparameter tuning.
Deliverables: Trained and validated pricing model.
Week 5-6: Integration and API Development
Activities: Develop APIs to integrate the model with e-commerce systems.
Deliverables: Functional integration with a live e-commerce platform.
Week 7-8: Dashboard Design and Development
Activities: Build an interactive dashboard for pricing trends and performance.
Deliverables: Real-time analytics dashboard.
Week 9-10: Testing and Validation
Activities: Test the model to check its accuracy and robustness.
Deliverables: Detailed testing report and refined model.
Week 11-12: Deployment and Documentation
Activities: Deploy the model, conduct training sessions, and hand over the project documentation.
Deliverables: Deployed model and user manuals.
