A self-driving car, also known as an autonomous vehicle, driverless car, or robotic car, is a car incorporating vehicular automation, that is, a ground vehicle that is capable of sensing its environment and moving safely with little or no human input.
The project used VGG16 architecture to classify dog and cat images with high accuracy. It was trained for 80 epochs and evaluated on a separate set of test images. The goal was to develop an accurate model for image classification.
A Machine Learning model to predict the strength of concrete based on it's input features. Used to readily test the strength based on the composition of The Ingreadients of the Concreate
A movie recommendation system using machine learning is a system that uses algorithms to suggest movies to users based on their previous movie choices and other relevant data. It is implemented using content-based filtering It helps users discover new movies that they may enjoy based on their existing movie preference
This project involved implementing a Convolutional Neural Network (CNN) on the Freiburg Groceries Dataset, which consists of 5000 food images categorized into 25 classes. The CNN model was trained for 10 epochs and evaluated using metrics such as accuracy, precision, recall, and F1 score. The objective was to develop a model that can accurately classify food images, which could be useful for various applications such as food recognition and inventory management in grocery stores. The results of this project demonstrate the potential of using CNNs for food image classification tasks and the effectiveness of data preprocessing techniques such as data augmentation and normalization in improving model performance. Overall, the project highlights the importance of deep learning techniques in the food industry for improving efficiency and accuracy in food classification and inventory management.
The IPL score predictor is a machine learning project that utilizes historical IPL data to predict the scores of IPL matches. Trained using Random forest machine learning algorithm, and its performance is evaluated using metrics such as mean squared error and R-squared. The project demonstrates the potential of machine learning in sports analytics and provides valuable insights into the performance of IPL teams and players.
The Safety Apparel Detection project utilizes the YOLOv5 object detection model to detect safety apparel worn by workers in different industries. The model is trained on a dataset of images and fine-tuned to detect specific types of safety apparel. The project aims to improve workplace safety measures by detecting whether workers are wearing proper safety gear. The project highlights the importance of object detection in promoting safety in the workplace.