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ai-personal-trainer

This project utilizes MediaPipe, OpenCV, and a pose estimation module to create a real-time fitness tracker focusing on counting dumbbell curls. The user stands in front of a camera, and the program calculates the angle between the shoulder, elbow, and wrist to determine the movement direction and count the number of curls performed.

ai-virtual-mouse

This project leverages OpenCV for hand tracking and gesture recognition and AutoPy for mouse control to create a real-time hand gesture mouse control application. Users can control the mouse cursor's movement and perform mouse clicks using hand gestures detected by the camera.

ai-virtual-painter

This project utilizes OpenCV and MediaPipe's hand tracking capabilities to create a real-time hand gesture drawing application. The user can draw on the screen by moving their index finger, and erase the drawing by raising their index and middle fingers simultaneously.

bigmart-sales-prediction

This web app is created to predict the sales of Big Mart based on the input features provided by the user. The model used in this web app is a XGBoost Regressor model which is trained on the Big Mart Sales dataset. The dataset used in this web app is taken from the Kaggle Datastes. The dataset contains 8523 rows and 12 columns.

blockchain

blockchain-solidity

My Solidity journey :)

breast-cancer-classification

This web app is created to classify the Breast Cancer into Benign and Malignant. The model is built using the Logistic Regression algorithm.

breast-cancer-classification-dl

This Deep Learning model utilizes Neural Networks to detect the presence of breast cancer. The dataset is imported from sklearn.datasets, containing 30 columns and 569 entries.

bulldozer-price-prediction

calories-burnt-prediction

This web app is created to predict the calories burnt based on the user inputs such as gender, age, height, weight, duration, heart rate, and body temperature. The model used in this web app is a Random Forest Regressor model trained on the dataset with 15000 samples.

car-price-prediction

This is a simple web app to predict the price of a car based on the user inputs. The model used in this web app is a Random Forest Regressor model. The model was trained on a dataset containing information about used cars. The dataset was collected from the Kaggle website.

chatbot-colab

chronic-kidney-disease-prediction

This we app is created to predict the chronic kidney disease using the data from the UCI Machine Learning Repository. The dataset is used to build a machine learning model to predict the chronic kidney disease based on the input parameters.

contribution-bot

This repository provides a detailed, step-by-step guide to setting up the GitHub Contribution bot. You’ll learn how to script in JavaScript and integrate with Windows Task Scheduler to automate your commits and maintain your GitHub streak.

credit-card-fraud-detection

This app detects credit card fraud based on the input parameters. t is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

disease-prediction-using-xgboost

dog-vs-cat-classification

This web app is a simple image classification app that uses a pre-trained model to classify images of dogs and cats. The model is trained using the MobileNet V2 architecture with ImageNet pre-trained weights. This is a SavedModel in TensorFlow 2 format. Using it requires TensorFlow 2 (or 1.15) and TensorFlow Hub 0.5.0 or newer.

emoji-prediction

face-detection

This project uses OpenCV and deep learning to detect faces in images and videos.

face-detection-mediapipe

This project employs the MediaPipe library and OpenCV for real-time face detection. Leveraging a webcam feed, it accurately identifies and annotates faces within the frame.

face-mask-detection-cnn

This web app is created to demonstrate the face mask detection model using Convolutional Neural Networks (CNN). The model is trained on a dataset containing images of people with and without masks. The model is built using TensorFlow and Keras libraries in Python.

face-mesh-detection-mediapipe

This project utilizes MediaPipe and OpenCV to perform real-time face mesh detection using a webcam feed. The program captures video frames and processes them using the MediaPipe Face Mesh module to detect facial landmarks and contours.

facial-recognition

fake-news-prediction

This web app is a fake news prediction tool. It uses a machine learning model to predict whether a given news is fake or not. The model is trained on a dataset of news articles and their labels. The model uses a logistic regression algorithm to make predictions.

fashion-mnist-gan

This script outlines the implementation of a Generative Adversarial Network (GAN) designed to generate fashion images using the Fashion MNIST dataset. The GAN consists of a generator and a discriminator, which are trained simultaneously in an adversarial manner.

fashion-mnist-image-classification

The web app uses a Convolutional Neural Network (CNN) model trained on the Fashion MNIST dataset to classify images of clothing.

finger-counter

This Python script utilizes the OpenCV library to perform real-time hand gesture recognition using a webcam. It employs a pre-trained hand detection model from the HandTrackingModule to detect and track landmarks on the hand.

gold-price-prediction

This web app is a simple Gold price prediction web app. The web app uses a Random Forest Regressor model to predict the Gold price based on the user inputs.

hand-gesture-volume-control

This project demonstrates real-time hand gesture-based volume control using a webcam feed. Leveraging the HandTrackingModule, MediaPipe, and OpenCV, the program tracks hand movements and recognizes gestures to adjust system volume.

hand-tracking

This project showcases real-time hand tracking using MediaPipe and OpenCV. Leveraging computer vision, it accurately detects and annotates hand landmarks from webcam footage, offering insights into hand poses and gestures.