SantoshRM-1/cropsense-karnataka

AI-based crop price prediction system using Machine Learning, Flask, React, and MongoDB. Helps farmers predict mandi prices. # KrishiPredict – Crop Price Prediction System ## Live Demo cropsense-karnataka.onrende… A web application that helps Karnataka farmers analyze crop prices and predict future prices using machine learning. ## Tech Stack This project is built with: - **Vite** + **React** + **TypeScript** - **Tailwind CSS** with **shadcn/ui** components - **Recharts** for data visualization - **React Router** for navigation ## Getting Started ### Prerequisites - Node.js 18+ and npm ### Installation Clone the repository and install dependencies: ```sh npm install ``` ### Running Locally Start the development server: ```sh npm run dev ``` Open localhost in your browser. ### Building for Production ```sh npm run build ``` ## Sample Dataset A sample dataset is available at `public/data/sample_dataset.csv`. It contains crop price records for Karnataka districts with the following columns: | Column | Description | |--------|-------------| | `date` | Date of price record (YYYY-MM-DD) | | `state` | State (Karnataka) | | `district` | District name | | `market` | APMC market name | | `commodity` | Crop name (Jowar, Ragi, Maize, Rice, Groundnut) | | `min_price` | Minimum price (₹ per quintal) | | `max_price` | Maximum price (₹ per quintal) | | `modal_price` | Modal (most common) price – used for analysis | ## Features - **Multi-language support** (English + Kannada) - **Crop selection** (Jowar, Ragi, Maize, Rice, Groundnut) - **Historical price analysis** with trend and district comparison charts - **Price prediction** for future dates using trend-based models - **CSV upload** – bring your own APMC dataset