Disk Scheduling Algorithms Visualizer

A disk scheduling algorithm visualizer made using HTML, CSS, and JavaScript would be a web application that demonstrates how disk scheduling algorithms work by animating the movement of read/write heads on a simulated disk surface. Users would be able to select the algorithm they want to visualize and provide input parameters such as the starting location of the read/write head and a list of disk requests. The visualizer would then animate the read/write head movement and display statistics such as the total head movement, average seek time, and throughput for the selected algorithm. The application would be useful for students and professionals learning about disk scheduling algorithms and would help them understand the trade-offs between different algorithms in terms of performance and efficiency.

Adult Census Income Prediction

An adult census income prediction model built using Random Forest, XAI - SHAP, Scatter, and LIME would be a machine learning application that predicts whether a person's income exceeds a certain threshold based on various demographic, social, and economic factors. The Random Forest algorithm would be used to build the predictive model by analyzing a large dataset of historical census data. The model would then be enhanced using XAI (Explainable Artificial Intelligence) techniques such as SHAP (Shapley Additive Explanations) to help users understand how the model arrived at its predictions. The Scatter and LIME (Local Interpretable Model-Agnostic Explanations) techniques would be used to visualize and explain the relationship between the input variables and the model's output. This application would be useful for policymakers, researchers, and social scientists who are interested in analyzing the factors that influence income inequality and developing evidence-based policies to address it.