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Tirth Dhandhukia’s personal portfolio showcasing frontend development skills, modern UI design, and performance focused web engineering. Built as a flagship project with extensive time spent on design, responsiveness, and clean code.

Portfolio

EVision is a data-driven system designed to forecast EV charging demand and recommend optimal locations for new charging stations across Mumbai. Using machine learning, geospatial analytics, and interactive dashboard visualizations, the project enables smarter urban planning for the growing EV ecosystem. 💡 Key Highlights •	Built an end-to-end ML pipeline covering data preprocessing, feature engineering, suitability scoring, and prediction. •	Trained and compared multiple models including Linear Regression, Ridge, Random Forest, Gradient Boosting, achieving up to 99.3% accuracy (R² = 0.9932) with Gradient Boosting. •	Designed a Suitability Scoring System (0–1) based on predicted demand, population density, accessibility, and spatial competition. •	Performed spatial clustering using K-Means + Haversine distance to detect underserved zones and avoid saturation. •	Created an interactive web dashboard using HTML, CSS, JavaScript, Leaflet.js, and Chart.js for real-time mapping and visual analytics. 🔍 What the System Delivers •	Forecasts EV charging demand at region/station level •	Identifies hotspots with high future load •	Recommends optimal charging locations with ranked suitability •	Helps policymakers, energy providers & EV operators plan efficiently 🛠 Tech Stack Python (Pandas, NumPy, Scikit-Learn), Gradient Boosting, Random Forest, GIS (GeoPandas, Folium), JavaScript, Leaflet.js, Chart.js, HTML/CSS 📌 Outcome EVision provides a scalable, city-level framework for making data-driven EV infrastructure decisions, reducing grid stress and improving accessibility—supporting India’s Smart City and green mobility initiatives.

Evision-Smart Charging Forecasting & Location

This project aims to use data analytics and machine learning to address critical inventory and supply chain challenges faced by the automotive semiconductor industry. The focus is on improving inventory forecasting, supplier performance evaluation, defect detection, and shipment delay analysis. Through advanced analytics and JIRA software integration, this project enables better decision-making and real-time tracking across the supply chain.

Using Data Analytics to Solve Inventory & Sup

Exit tutorial hell - curated official documentation &amp; learning paths for React, Next.js, TypeScript, Rust, Python &amp; 50+ technologies.