Project Review Dashboard for Streamlit app

Annotated version of Scheduler with code output

Project Review Scheduler - Main Implementation Module This module implements the core functionality for the Project Review Scheduler system, including due date calculation, reviewer assignment, notification, and reporting.

Project review scheduler V&v

Software Deployment and Software Maintenance

The Project Review Scheduler is a Python-based automation tool designed to streamline the scheduling, assignment, notification, and reporting of project reviews. It replaces error-prone manual tracking with a command-line system that processes CSV files

Project Review Scheduler: Python Automation for Review Management The Scheduler is a Python-based automation tool designed to streamline the scheduling, assignment, notification, and reporting of project reviews. It replaces error-prone manual tracking with a command-line system that processes CSV files.

Software Design Document for Project Review Scheduler

Software Requirements for the Project Review Scheduler

Connecting Reviewers with projects

# Project Review Scheduler: Automating Quality Assessment for Improved Project Management

This script loads and validates the structure of a reviewer dataset stored in an Excel file

This script uses the U.S. Census Community Survey Data

Using the MNIST data set for image extraction

Text extraction of Census data

Unit 2 assignment

The purpose of Agnostic learning is to reduce the assumptions made about the target function and discover a hypothesis that approximates it with minimal assumptions (Kearns et al., 1992). This approach utilizes dynamic programming and loss functions to find the best approximation. Agnostic learning, unlike other learning techniques which assume a specific fixed function form, allows the model to learn from data with complex relationships, such as those involving high-degree interaction among features. This means that the model can learn to predict categorical outcomes or continuous-valued outputs based on generic features. To understand the concept of agnostic learning, consider the binary classification problem of identifying ripe apples. Let the hypothesis class H contain all possible ways of distinguishing between ripe and unripe fruits, which might include features such as color, size, texture, or sweetness.

Demonstration of the Central Limit Theorem using Airline delay data

CLT Demo

Breast Cancer Analysis

Breast Cancer Data Analysys

MovieLen data analysis