MACHINE LEARNING-BASED PREDICTIVE MODEL FOR FUTURE SAVINGS: ANALYZING MONTHLY CONSUMPTION AND EARNINGS

• Main Author: Shaiful Rahimi, Muhammad Norhazim
• Format: Final Year Project
• Language: English
• Published: 2024
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://utpedia.utp.edu.my/id/eprint/26984/1/19000445.pdf; http://utpedia.utp.edu.my/id/eprint/26984/

Artificial intelligence (AI) and machine learning (ML) technologies have revolutionized various domains, including finance. This project aims to address key challenges in the contemporary financial landscape by leveraging AI and ML techniques to provide innovative solutions for financial planning and decision-making. The project begins with an exploration of AI fundamentals, including problem-solving methodologies and machine-learning concepts. It then delves into the development of predictive models tailored to financial forecasting, focusing on savings planning, personalization of financial guidance, and data-driven decision-making. To achieve these objectives, the project utilizes the Adult Income Database, a rich dataset curated by Ronny Kohavi and Barry Becker. This dataset enables detailed analysis of socioeconomic factors influencing income levels, providing valuable insights for model training and validation. The project follows a systematic approach, encompassing data collection, preprocessing, exploratory data analysis, model development, and evaluation. Additionally, a user-friendly Flask application is built to provide individuals with access to the predictive model, facilitating seamless financial planning and decision-making. Overall, this project contributes to the advancement of AI-driven financial technologies, empowering individuals to make informed decisions and achieve long-term financial stability in an increasingly complex financial landscape