Leveraging TensorFlow and Machine Learning for Accurate Scholarship Portal Predictions

Abstract: The utilization of machine learning (ML) techniques, particularly Tensor Flow, for predicting scholarship eligibility has become paramount in modern educational landscapes. This study proposes a predictive model leveraging Tensor Flow algorithm to forecast scholarship eligibility based on a comprehensive set of input parameters. These parameters include crucial academic metrics such as GPA, 10th and 12th percentage, alongside qualitative assessments like extracurricular activities, essay quality, and letters of recommendation. Furthermore, the model integrates socio-economic factors such as financial need, family background, and state of residence, along with indicators of leadership, volunteerism, and work experience. Implemented through Python Flask for a user-friendly interface, this system provides a seamless experience for users to input their data and receive predictions regarding their eligibility for scholarships. By harnessing the power of ML, this framework offers educational institutions and students a robust tool to streamline scholarship allocation processes, ensuring efficient and equitable distribution of resources to deserving candidates.