pratikkor Goto Github PK

cafe

A Classic Frontend Cafe Webpage with stunning graphics and working Cart system and Dynamic content alignment

crop__reco-analysis

A Crop Recomendation ML model that can idetify the best suitable crop to be planted based on the soil ph,nitrogen,etc content values

farm-fare

responsive webpage design for farm goods selling over the internet

full-stack-mern-application

A Fullstack MERN application Messaging Web Project with user authentication and Login Session management and Deployment

html-portfolio

image-maksing

A sophisticated image masking algorithm that transforms textual data into pixelated images, facilitating optimized storage and significant size reduction while ensuring lossless compression and decompression.

java_labs

List of java practicals

pratikkor

Config files for my GitHub profile.

rice_classification

This project employs CNNs to classify different varieties of rice from images. It utilizes TensorFlow and Keras to construct the model, achieving a peak validation accuracy of 98.62% after training. The dataset comprises classes like Arborio and Basmati, with training and testing datasets containing 60,000 and 15,000 files respectively.

rice_disease_classification

A CNN Model that classifies the type of disease a rice plant may (Max Accuracy 96.17 % ) have based on the 3 classes its Trained on .

snapper

Instagram Clone featuring robust user creation, a fully functional login system, and dynamic post interaction capabilities. Users can not only create accounts but also engage with posts by liking them, create their own posts, and conveniently save content tailored to their preferences.

soil_moisture

soil moisture module with webpage

text-encoder

web-scrapping-and-sentiment-analysis

Python tool for web scraping and sentiment analysis. Gather data from online sources, organize it into Excel, and analyze sentiment—all in one seamless process. Customize parameters to fit your needs and revolutionize your data analysis workflow.

ybi-project-submission

ybi-submission-machine-learning