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Sprint 3: Machine Learning Model Guide

Overview

This guide covers the third ticket of the BandersnatchStarter project, focused on building and integrating a machine learning model using Scikit-learn, as outlined in BandersnatchStarter/tickets/thirdTicket.md. The objective of Sprint 3 is to create a machine learning model that analyzes monster data from MongoDB (set up in Sprint 1) and integrates it into the Flask web application for user interaction, such as predicting monster attributes. This guide is tailored for a junior JavaScript/TypeScript developer familiar with web development and possibly TensorFlow.js, but new to Python and Scikit-learn. By relating Python and Scikit-learn to JavaScript concepts, this guide will help you build a machine learning model while learning new tools.

Ticket Objectives

The third ticket focuses on:

1. Preparing Monster Data: Query and preprocess monster data from MongoDB f

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Sprint 2: Dynamic Visualizations Guide

Overview

This guide covers the second ticket of the BandersnatchStarter project, focused on creating interactive visualizations using the Altair library, as outlined in BandersnatchStarter/tickets/secondTicket.md. The objective of Sprint 2 is to query the monster data stored in MongoDB (from Sprint 1) and create dynamic visualizations (e.g., bar charts, scatter plots) to display in a Flask-based web application. This guide is tailored for a junior JavaScript/TypeScript developer familiar with web development and visualization libraries like Chart.js or D3.js but new to Python and Altair. By relating Python and Altair to JavaScript concepts, this guide will help you build visualizations while learning new tools.

Ticket Objectives

The second ticket focuses on:

1. Querying Monster Data: Retrieve monster data from MongoDB to use for visualizations.
2. **Creating Visualiza

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Sprint 1: Database Operations Guide

Overview

This guide covers the first ticket of the BandersnatchStarter project, titled "Database Operations," as outlined in the repository: BandersnatchStarter/tickets/firstTicket.md. The objective of Sprint 1 is to set up a MongoDB database and populate it with "monster data" for use in a Flask-based web application. This guide is tailored for a junior JavaScript/TypeScript developer with about one year of experience, familiar with JavaScript concepts like Node.js, Express.js, and MongoDB (via Mongoose), but with limited Python exposure. The goal is to guide you toward a solution without providing complete code, helping you learn Python and MongoDB concepts by relating them to JavaScript equivalents.

Ticket Objectives

The first ticket focuses on:

1. Setting up a MongoDB database: Create a MongoDB cluster and configure access.
2. Connecting to MongoDB from Python: Us

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Bandersnatch Project Report

Introduction

The BandersnatchStarter project, hosted at GitHub - BloomTech-Labs/BandersnatchStarter, is a beginner-friendly data science and machine learning project centered around "monster data." Think of it as a web application where you manage, visualize, and analyze data about fictional monsters using Python. The project is broken into three sprints, each building on the previous one, guiding you through database setup, data visualization, and machine learning model creation. For a JavaScript/TypeScript developer, this is a great opportunity to learn Python, which is similar in its high-level, readable syntax but used more for data science and backend tasks compared to JavaScript’s focus on web interactivity.

Project Structure

The repository is organized into four main directories, each serving a specific purpose. Think of these like the folders in a JavaScript project (e.g., src, public, components):

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CPU Registers: The CPU's Working Memory

What Are Registers?

Registers are the fastest storage locations in a computer, located directly within the CPU. Think of them as the CPU's immediate workspace - like having tools and materials right on your workbench rather than having to walk to a storage room every time you need something.

Key Characteristics:

• Ultra-fast access: No memory latency like RAM
• Limited quantity: Only a small number available
• Temporary storage: Hold data during active processing

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Code Quality Feedback and Roadmap for slatec90

Date: July 24, 2025
Reviewer: Tom Tarpey
Repository: Bishibop/slatec90
Project Context: Enterprise Legacy Modernization Project (7-day timeline)

Overview

The slatec90 project aims to modernize a subset of the SLATEC Fortran 77 mathematical library (738 of 1,441 functions, 168,355 lines) to Fortran 90+, focusing on numerical accuracy through test-driven validation. The project aligns with the Enterprise Legacy Modernization spec, emphasizing AI-assisted development and legacy system transformation. However, it is early in development, with only 10 of 209 zero-dependency functions migrated, and lacks a clear target user segment. This report assesses code quality, progress, and provides a roadmap for the 7-day timeline.

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Observational Report on MVP Submission Video

By Tom Tarpey, Reviewer

Date: July 24, 2025
Project: Migrating an Abandoned Fortran Library to Modern Standards

Overview:
The presenter is modernizing SLATEC, a legacy Fortran 77 math library, to Fortran 90 using AI-assisted tools. The project focuses on migrating 740 math functions, with 10 semi-manually completed, aiming for automated parallelization.

Good Points:

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Grading Report

Code Quality Score: 70 / 100
Code Smell Score: 65 / 100

Concise Code Review Report: simple-c-profiler

Summary Table

Metric	Score

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Grading Report

Code Quality Score: 75 / 100
Code Smell Score: 70 / 100

Concise Code Review Report: dcat

Summary Table

Metric	Score

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Grading Report

Test Results

• Passed: 0/0

Code Quality

Code Review Report: Moodboard AI

Summary Table

| Metric | Score |