Building and AI-Powered budget management system
Financial budgeting is a crucial skill for both individuals and organizations to maintain financial health and enable long-term planning. This course introduces learners to the use of machine learning specifically K-Means clustering to analyze financial data and create intelligent, data-driven budgeting strategies. By clustering user financial behaviors, we can tailor recommendations and risk profiles. This course also incorporates GeminiAI for generating intelligent insights based on clustering outcomes and making budget suggestions.
The course aims to guide learners in building an intelligent budgeting system using unsupervised learning (K-Means clustering) to segment users based on their spending and income behaviors. Once clusters are identified, Gemini's API is utilized to:
- Interpret financial clusters (e.g., spendthrifts, savers, balanced)
- Suggest tailored budgeting strategies
- Provide smart, personalized financial advice
Data will be adapted from the Mall Customer Dataset from Kaggle.
Basic Python Knowledge
Philip Titus
Software/Machine Learning Engineer
Python Developer at CheapTrip
LinkedIn: https://www.linkedin.com/in/philiptitus/
Philip Titus is a software engineer with a strong background in software development and AI/ML. He is proficient in various programming languages and machine learning algorithms, with hands-on experience across cloud platforms like AWS and GCP. Philip is passionate about building robust, scalable solutions and staying ahead of industry trends to contribute to cutting-edge technological advancements.
