Kishor Goraniya

The Weed Detection System is an AI-powered Django application that detects crops and weeds from images or video using computer vision. It automatically counts unique crops and weeds, displays detection results, saves processed videos, generates detailed reports, and allows users to download reports and videos for efficient agricultural monitoring.

This repository contains end-to-end implementations of key Machine Learning models. From Regression and Classification to Clustering, each folder includes the algorithm logic, dataset handling, and performance testing metrics to demonstrate real-world predictive power.

This project presents a deep learning-powered image classification system designed to identify medicinal plant species from images. The model is built using Python and implemented with TensorFlow/PyTorch, leveraging the MobileNetV3Large architecture for efficient and accurate predictions.

Animal-Breed-Analyzer is a machine learning project designed to identify animal breeds from images. It leverages computer vision and deep learning techniques to classify breeds accurately. The project includes data preprocessing, model training, evaluation, and prediction modules, making it useful for educational, research, practical applications.

SVM Grid Search with all kernels

An interactive Streamlit-based web app for effortless data analysis & visualization. Upload CSV/Excel, auto-clean data, explore insights, create interactive charts, detect outliers, and apply ML models—no coding needed. Modern dark-themed UI for students, analysts & professionals