Map-Based Property Search and Listing Analytics Frontend

Added Jun 2025 3 design docs

Property seekers decide in seconds whether a listings site is worth their time: search must be instant, maps must orient them, and photos must load beautifully. This project builds a real estate frontend engineered to win those seconds and keep users exploring. The intern implements the platform with Next.js and TypeScript, building advanced search with rich filtering across price, location, and property attributes, interactive maps that let users browse listings spatially, and media upload flows for property photos and videos. User authentication personalizes the experience with saved searches and favorites, while listing analytics views surface pricing and market context alongside each property. Redux Toolkit manages search state, map interactions, and API integration cleanly, and Tailwind CSS delivers a polished, responsive design across devices. The project combines several demanding frontend disciplines in one build, including map-driven interfaces, media-heavy pages that stay fast, and complex filter state synchronized with URLs and APIs. Finishing it gives the intern a visually impressive, technically substantial portfolio piece in one of the web's most competitive product categories.

Related projects

Property Listing Portal With Visit Scheduling and Media Tours

Buying or renting property usually means bouncing between listing sites, phone calls to schedule visits, and photos of unknown quality. This project consolidates the experience into one portal where properties are listed with rich media, visits are booked online, and agents manage their pipeline in a single place. The intern builds the portal on a Flask backend with MySQL, modeling properties, agents, users, and visit schedules, and handling image and video uploads so each listing can offer a genuine visual tour. Search and filtering let buyers narrow results by the criteria that matter, while authentication and distinct agent and user roles ensure listing management stays in the right hands: agents publish and update properties and confirm visit requests, and users browse, save, and schedule. The frontend combines Next.js, React, and TypeScript with Tailwind CSS for a clean, responsive browsing experience across devices. Delivering it teaches media-heavy backend design, relational search and filter queries, and role-aware interface construction, giving the intern a portfolio piece that mirrors how real estate marketplaces actually operate.

real-estateflaskfullstack+5

View project

Hard

Search-Driven Real Estate Listings and Market Analytics Platform

Real estate moves fast, and both agents and buyers need more than static listings: they need instant search across thousands of properties and analytics that reveal how markets are moving. This project builds a listings platform where search speed and data insight are the core product. The intern implements a Flask and Python backend with PostgreSQL as the system of record for properties, agents, buyers, and transactions, then integrates Elasticsearch so full-text and filtered property search returns results in milliseconds even at scale. JWT authentication and role-based access separate agents, buyers, and administrators; agents manage listings and upload property images, buyers search and save properties, and admins oversee the platform. Analytics views aggregate listing and pricing data into market trends. The frontend combines React and TypeScript with Tailwind CSS for a responsive, filter-rich search experience. The project teaches the intern to run a dual-store architecture, keeping a relational database and a search index in sync, a pattern used across serious marketplace products, and to layer authorization and analytics on top. It is a demanding, production-shaped build that shows mastery of fullstack engineering beyond simple CRUD.

businessreal-estateelasticsearchflask+7

View project

Medium

Decision Tree Survival Modeling App With Interactive Pruning Controls

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.

educationcssdata-science+7

View project