To explore how the integration of AI and Data Science can enhance Business Intelligence (BI) insights and decision-making.
To evaluate the use of Power BI as a low-code platform for visualizing and interacting with AI-driven analytics.
To demonstrate how predictive analytics, natural language processing, and machine learning can be embedded in BI dashboards.
To assess the accessibility and scalability of low-code tools for non-technical business users.
To recommend best practices for designing intelligent, real-time, and user-friendly BI systems using low-code technologies.
Conduct a literature review on Business Intelligence, AI, and low-code/no-code tools in data analytics.
Study Power BI’s capabilities for integrating Python, R, Azure ML, and cognitive services into reports and dashboards.
Design a sample BI solution that incorporates machine learning models (e.g., sales forecasting, customer segmentation) using Python/R and Power BI.
Apply low-code principles to automate data transformation, generate insights, and build interactive visualizations.
Evaluate the performance and usability of the integrated solution based on response time, prediction accuracy, and business relevance.
(If feasible) Gather feedback from business users or analysts on the effectiveness of AI-powered dashboards in real-world decision-making.
Prepare a comprehensive report outlining technical implementation, integration workflow, model impact, user experience, and recommendations for scaling BI using low-code AI solutions.
