This project focuses on leveraging AI technologies to build a robust, scalable solution for businesses. The goal is to provide actionable insights and improve decision-making processes by integrating data from multiple sources and applying advanced analytics. Students will engage in various stages of AI solution development, from data integration to model deployment, ensuring ethical and privacy standards are met.

Project Options:

1. AI-Powered Customer Feedback Analysis

• Description:
• Develop a tool utilizing Natural Language Processing (NLP) techniques to analyze customer feedback collected via surveys or reviews. The solution should extract key themes, sentiment trends, and actionable insights, while prioritizing user data privacy.
• Deliverables:
• Sentiment analysis engine using NLP libraries.
• Visual reports summarizing customer insights.
• Documentation outlining ethical considerations and privacy measures implemented.

1. Smart Data Source Integration and Evaluation Platform

• Description:
• Create a platform that identifies, evaluates, and integrates data sources (internal and external) to support Wouessi's operational analytics. The system should ensure data quality and relevance, enabling organizations to streamline decision-making.
• Deliverables:
• A data integration pipeline with source evaluation metrics.
• A dashboard displaying integrated datasets and their readiness for analysis.
• Recommendations for future data source optimization.

1. Machine Learning for Customer Segmentation and Targeting

• Description:
• Design and deploy a machine learning model that segments customers based on behavioral or demographic data. The solution should enable businesses to personalize marketing strategies and enhance customer engagement.
• Deliverables:
• Machine learning model for customer segmentation.
• An API or web-based application for visualizing segmentation results.
• A report on the scalability and efficiency of the deployed solution.

Key Skills and Technologies:

• Programming: Python, R
• Libraries/Frameworks: TensorFlow, PyTorch, Scikit-learn, spaCy, or NLTK
• Data Management: SQL, NoSQL, Pandas
• Tools: Tableau, Power BI, Jupyter Notebooks
• Other Considerations: Ethics in AI, Data Privacy, GDPR Compliance

Learning Outcomes for Students:

• Practical experience in developing AI solutions for real-world challenges.
• Proficiency in data analysis, integration, and machine learning techniques.
• Enhanced understanding of ethical and privacy considerations in AI projects.

Evaluation Metrics:

• Functionality and accuracy of the developed tools or platforms.
• Scalability and ease of integration with existing systems.
• Creativity and originality in addressing the project requirements.
• Clear documentation and communication of the solution’s impact.