The “Blood Banking System-Reforming Blood Donation and Emergency Blood Requests” presents a comprehensive digital solution designed to reshape the blood donation and emergency blood requests. Integrated with advanced technology with healthcare needs, this system offers user-centric functionalities. With this user-friendly system, people wanting to donate blood can easily sign up, provide important health information, and verify their blood test results with integrated security to maintain user data privacy.
This project is a movie recommendation system based on popularity. It recommends movies to users based on their popularity among all users. The Popularity-based Movie Recommendation system works by identifying the most popular movies based on their ratings and recommending them to users. The popularity of a movie is determined by the number of ratings it has received from users. The project utilizes a dataset that contains movie ratings provided by users. Each rating consists of the movie ID, user ID, and the rating value. The project focuses on analyzing the popularity of movies and generating recommendations based on their popularity.
Iris Classification aims to classify Iris flowers into different species based on their sepal and petal measurements. It utilizes the popular Iris dataset, which contains measurements of four features (sepal length, sepal width, petal length, and petal width) for three different Iris species (Setosa, Versicolor, and Virginica). The dataset consists of 150 samples, with each sample having four feature values and a corresponding target label. Several classification models are implemented in this project to classify the Iris flowers: Decision Tree Classifier Random Forest Classifier K-Nearest Neighbors (KNN) Classifier Support Vector Machines (SVM) Classifier Gradient Boosting Classifier The models are trained using the Iris dataset and evaluated using various evaluation metrics such as accuracy, precision, recall, and F1-score.
Handwritten digit recognition using MNIST dataset is a major project made with the help of Neural Network. It basically detects the scanned images of handwritten digits. The MNIST handwritten digit classification problem is a standard dataset used in computer vision and deep learning. Although the dataset is effectively solved, it can be used as the basis for learning and practicing how to develop, evaluate, and use convolutional deep learning neural networks for image classification from scratch.
Cooking is not only a work but also an art. It is not confined to anything it can be learnt by anyone who definitely wants to and that is only possible when you have a right guide to help you learn it. Welcome to Tasty Plate, our website provides you a platform to learn and cook popular cuisines across all countries. We provide a beginner friendly cooking processes that helps you to learn making your favourite dish.