The objective of this project is to analyze and optimize an AI-driven lead qualification model, ensuring it accurately identifies high-potential leads for sales teams. Students will evaluate different lead-scoring methodologies, refine key input parameters, and assess the AI model’s predictive capabilities. By conducting research and analyzing real-world data, students will provide data-backed recommendations to improve the model’s effectiveness.

This project is specifically designed for students in data science, artificial intelligence, or business analytics programs, allowing them to apply classroom knowledge in AI model optimization, data analysis, and performance evaluation. The focus will be on refining the model rather than integrating it into a broader sales automation system, ensuring that the project remains within a single academic discipline.

✅ Research & Literature Review: Analyze existing AI-driven lead scoring methodologies and best practices in predictive modeling.

✅ Dataset Analysis: Assess the current dataset used for lead qualification and identify key variables impacting lead conversion.

✅ Model Refinement: Test and optimize input parameters to improve the accuracy of the AI model’s lead-scoring predictions.

✅ Performance Evaluation: Compare the AI model’s accuracy against traditional lead-scoring methods and measure improvements.

✅ Final Report & Presentation: Provide a structured report summarizing key findings, methodology, insights, and recommendations for optimizing lead qualification using AI.