MediumTechnologyeducation

Decision Tree Survival Modeling App With Interactive Pruning Controls

Added Jun 2025 3 design docs

Decision trees are the most explainable models in machine learning, which makes them ideal for teaching — but only if learners can actually see the tree and watch it change. This project builds a web application around that idea using the classic Titanic passenger dataset, where every split has an interpretable story.

The intern develops a Flask backend that trains a decision tree classifier with scikit-learn to predict passenger survival, served through a frontend built with HTML, CSS, and JavaScript. Users view a rendered visualization of the fitted tree, adjust pruning parameters such as maximum depth and minimum samples per split, and watch the tree restructure and its predictions change in real time — a direct demonstration of the overfitting-versus-generalization trade-off. Jupyter notebooks accompany the app to document data preparation and experimentation, and per-passenger predictions let users test scenarios interactively.

The intern learns how tree construction, overfitting, and pruning interact — knowledge made durable by manipulating it directly — while gaining full-stack experience: serving a model with Flask, wiring a JavaScript frontend to a Python API, and packaging an educational tool others can learn from.

Related projects

BERT Fine-Tuning Command-Line Tool for Text Classification

Transformer models dominate modern text classification, but the distance between downloading a pre-trained BERT checkpoint and having a reliable classifier trained on your own labeled data trips up many practitioners. Teams in finance, healthcare, and education all share the same need: fine-tune on a private dataset, evaluate honestly, and run predictions repeatably, ideally from scripts rather than notebooks. The intern builds a Python command-line application that packages this workflow end to end using the Transformers library. Commands accept a user-provided labeled dataset, run preprocessing and subword tokenization, fine-tune a BERT-based model on the classification task, and report evaluation metrics computed with scikit-learn such as accuracy and per-class F1. Further commands save and reload fine-tuned models and run inference over new text files, writing predictions to structured output so the tool slots cleanly into larger pipelines. Sensible defaults with overridable training options make the tool approachable without hiding what matters. The project teaches the intern how transfer learning works in NLP practice, tokenization, fine-tuning dynamics, and evaluation discipline, and demonstrates the ability to wrap state-of-the-art models in a dependable command-line interface that other people can actually use.

educationfinancenlppythonscikit-learn+1

PRD · HLD · LLD includedView

TechnologyHard

Crop Disease Classification Service Using Transfer Learning

Crop diseases are often identified too late for effective intervention because farmers and field agronomists rarely have quick access to plant pathology expertise. A photograph of a diseased leaf contains enough signal for accurate diagnosis, but turning that signal into a dependable, scalable service is a genuine engineering challenge for agricultural research groups. The intern builds a full diagnostic platform around transfer learning: pre-trained ResNet and MobileNet backbones are fine-tuned in PyTorch on crop disease imagery, with OpenCV and NumPy handling preprocessing and a data augmentation pipeline improving robustness to lighting and orientation. A Streamlit interface lets users upload leaf photos and receive ranked disease predictions alongside explainable AI visualizations that highlight the image regions driving each classification, while a FastAPI service exposes a batch inference endpoint for research workloads and pandas powers summary reporting. The entire system is containerized with Docker so it can be deployed consistently across lab and cloud environments. The project demonstrates end-to-end applied computer vision: adapting pre-trained networks to a specialist domain, communicating model reasoning to non-technical users, and packaging research code as a deployable, reproducible service.

agricultureeducationcomputer-visiondockerfastapi+8

PRD · HLD · LLD includedView

TechnologyEasy

Lightweight Text Generation API Built With LangServe

The gap between a working LangChain script and a service other software can call is deployment, and LangServe exists to close it: it turns chains into REST APIs with typed endpoints, streaming, and an interactive playground almost for free. Knowing this path from notebook to network service is essential for any developer who wants their AI work used by anyone other than themselves, and this project walks it end to end at a friendly scale. The intern builds a FastAPI project in Python that exposes a text generation chain through LangServe. The intern first constructs the chain itself with LangChain, a prompt template feeding a language model with configurable parameters such as topic and style, then mounts it as a LangServe route, gaining automatically generated endpoints for invocation, batching, and streaming. Around this core the intern adds a health check, request logging, and clear API documentation, then exercises the service from the built-in playground and from an external client script to prove the full round trip works. The project teaches deployment fundamentals for LLM applications: how chains become HTTP services, how FastAPI and LangServe divide responsibilities, what streaming responses look like on the wire, and how to structure a small AI service so that adding the next endpoint is easy, foundations the intern will reuse in every production AI role.

educationfastapigenerative-ailangchain+1

PRD · HLD · LLD includedView