Athiresh

brand_cnn

This project utilizes CNN, a powerful neural network architecture tailored for image processing tasks that leverages deep learning techniques to identify and predict the brand of a product based on its image.

cabin_entry_security

course_seats

crop_recommendation

This repository hosts the code for a Crop Prediction Machine Learning project. The project aims to assist farmers and agricultural enthusiasts in making informed decisions about crop selection by leveraging machine learning and environmental data.

face_regocnition

This project demonstrates the implementation of real-time face recognition. To ensure highest accuracy and real-time performance, this project integrates the robust LBPs technique alongside OpenCV's Haar Cascade for face detection. This combination allows for efficient feature extraction & training, making it ideal for real-time face identification

iris_pca

Iris data set prediction pandas using PCA method

IT-Customer-

This project focuses on addressing customer churn, a critical concern for businesses, using machine learning algorithms. Customer churn analysis evaluates the rate at which a company loses its customers and aims to reduce it to foster business growth. By leveraging a diverse dataset, trained and analyzed several machine learning algorithms.

pdf_reader

This project aims to simplify document processing by offering a streamlined solution for extracting, reading, and translating text from PDF files, leverages advanced OCR technology to extract text with precision. Users can not only read the extracted text in its original language but also translate it into various languages.

real_time_emotion

This project demonstrates the implementation of real-time facial emotion recognition using the deepface library and OpenCV. The objective is to capture live video from a webcam, identify faces within the video stream, and predict the corresponding emotions for each detected face and displayed in the video frames with audio message.

volume_control

This code captures video from the default camera, detects hand gestures using the MediaPipe Hands model, and adjusts the system volume based on the detected gestures while displaying relevant information on the video frame.

Athiresh/crop_recommendation

This repository hosts the code for a Crop Prediction Machine Learning project. The project aims to assist farmers and agricultural enthusiasts in making informed decisions about crop selection by leveraging machine learning and environmental data.

Athiresh/brand_cnn

This project utilizes CNN, a powerful neural network architecture tailored for image processing tasks that leverages deep learning techniques to identify and predict the brand of a product based on its image.

Athiresh/cabin_entry_security

Athiresh/course_seats

Athiresh/face_regocnition

This project demonstrates the implementation of real-time face recognition. To ensure highest accuracy and real-time performance, this project integrates the robust LBPs technique alongside OpenCV's Haar Cascade for face detection. This combination allows for efficient feature extraction & training, making it ideal for real-time face identification

Athiresh/iris_pca

Iris data set prediction pandas using PCA method

Athiresh/IT-Customer-

This project focuses on addressing customer churn, a critical concern for businesses, using machine learning algorithms. Customer churn analysis evaluates the rate at which a company loses its customers and aims to reduce it to foster business growth. By leveraging a diverse dataset, trained and analyzed several machine learning algorithms.

Athiresh/pdf_reader

This project aims to simplify document processing by offering a streamlined solution for extracting, reading, and translating text from PDF files, leverages advanced OCR technology to extract text with precision. Users can not only read the extracted text in its original language but also translate it into various languages.

Athiresh/real_time_emotion

This project demonstrates the implementation of real-time facial emotion recognition using the deepface library and OpenCV. The objective is to capture live video from a webcam, identify faces within the video stream, and predict the corresponding emotions for each detected face and displayed in the video frames with audio message.

Athiresh/volume_control

This code captures video from the default camera, detects hand gestures using the MediaPipe Hands model, and adjusts the system volume based on the detected gestures while displaying relevant information on the video frame.

Athiresh/number_plate_ocr