The music generator application with LSTM and RNN neural network is a project that uses machine learning techniques to generate music. The application is designed to take in a dataset of existing music, and then use an LSTM (Long Short-Term Memory) or RNN (Recurrent Neural Network) model to learn th
This is a python-based application which provides an involuntary attendance marking system that operates without human intervention. It intends to serve as an efficient substitute for traditional manual attendance systems.
The model focuses on predicting the crop yield in advance by analyzing factors like district (assuming same weather and soil parameters in a particular district), state, season, crop type using various supervised machine learning techniques.
Smart Garbage Segregation is a project that aims to using AI/ML to efficiently and effectively sort waste into different categories such as plastic, glass, etc. using oneDNN.
This project is designed to predict the likelihood of a person developing diabetes based on a number of risk factors. The goal of tproject is to help identify individuals who are at high risk for the disease so that preventive measures can be taken early on to minimize the likelihood of complication
A comprehensive algorithm that generates a brief summary of a given research paper.Paper summarization is a crucial tool that helps researchers, students, and professionals save time and effort when working with research papers. However, summarizing research papers can be a daunting task, especially
The proposed system is a machine learning system that uses data from past matches, player and team performance metrics, and other information to generate predictions for future matches. The system will use supervised learning algorithms to identify patterns in the data and make accurate predictions.
US lenders use AI to understand mortgage delinquency risks, but models must be updated with changing data to remain accurate. Fair predictions are essential for ethical AI and building trust in AI systems impacting society.
We aim to create an intelligent system that can recommend personalized diets for individuals based on specific parameters like age,type of diet(vegan/non-vegan),weight and height. The system will use machine learning algorithms to analyze these parameters to generate a diet plan.
This NLP issue involves classifying the positive tweets from the bad tweets using machine learning models for classification, text mining, text analysis, data analysis, and data visualization.
Bengaluru House Prediction is an ML model with a user-friendly Flask interface built using Intel One API. It predicts home prices using pandas, scikit-learn, and matplotlib. The project benefits homebuyers, agents, and developers, demonstrating data science's power.
This project involves identifying edible mushrooms using various features such as cap shape, cap color, gill size, spore print color, habitat, and other characteristics.
OneDAL library project to build and optimize machine learning models for predicting the price of diamonds based on their various characteristics such as carat, cut, color, clarity, depth, and table.