In this project, we aim to develop a machine-learning model that can accurately predict the likelihood of depression based on social media posts. We will gather a large dataset of social media posts from individuals with and without depression and use natural language processing techniques.
In this project, we aim to develop a machine learning model that can accurately predict the likelihood of depression based on social media posts. We will gather a large dataset of social media posts from individuals with and without depression, and use natural language processing techniques.
My name is Devesh Bhandari and my teammate VS Lavan and We are student at Christ University. This challenge is focused on house price prediction in India, where the goal is to accurately predict the prices of properties using 12 influencing factors.
The pre requisite for this system are mean radius , mean perimeter ,mean area, worst radius, worst perimeter, worst radius ,worst area.
giving this parameter it detects whether it is Malignant or Benign
E-waste is one of the major concerns as of today, and them being a major concern makes it a necessity for its decomposition and recycling more important. We propose an idea to take analysis data of the current situation of the E waste being generated and all its sources and other factors.
Credit card fraud detection systems leverage AI/ML to detect patterns of fraudulent behavior in spending data. They identify anomalies, flag suspicious activity, and improve over time through machine learning. This helps prevent losses for financial institutions and consumers.
The Speech Recognition System is an artificial intelligence-based system that recognizes and transcribes spoken language into written text or other forms of output.
Basically, this model includes patient diagnoses for those with heart problems. This AI/ML model is to predict wether a person is with heart disease or not.
Here, we explore datasets with different no. of attributes required for prediction using a number of different visualization techniques.
This project aims to build a spam detection system using machine learning algorithms. The goal is to train a machine learning model that can accurately classify messages as spam or not spam. By doing so, we can save time and avoid the hassle of dealing with unwanted messages.
It Predict the white winner in a chess game on the basis of first move of white player and response of black player. In the dataset all the set of moves are given but We choose to predict the white winner the first move.Also it predicts the next move of the player using deep learning techniques
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.
In today's world mental health is a vital part of our society and us. According to a research performed by the WHO, more than 13% of the world's population suffers from mental health issues and substance use disorders. In 2019, 1 in every 8 people, or 970 million people around the world were living
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.