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.