A dark web analysis tool.

This Hand gesture recognition project using mediapipe is developed to recognize various hand gestures. The user can custom train any number of various hand gestures to train a model.

Web platform allows users to upload CSV files and train a machine learning model using the uploaded data

Email Spam Detection using Machine Learning

Designed and deployed a scalable machine learning pipeline on AWS to detect fraudulent transactions, leveraging SageMaker for model deployment, real-time inference, and feedback-based retraining. Ensured secure data handling with S3 and tenant isolation for a multi-tenant SaaS LMS application.

Repository for predicting house prices using the Ames Housing dataset. Implements advanced regression techniques with TensorFlow Decision Forests, including Random Forests. The project covers data exploration, feature engineering, model training, evaluation, and visualization.

A PyTorch-based Convolutional Neural Network (CNN) for image classification using the CIFAR-10 dataset, featuring advanced architecture, data augmentation, GPU support, and dynamic learning rate scheduling.

The Titanic classification problem involves predicting whether a passenger on the Titanic survived or not, based on various features available about each passenger. The sinking of the Titanic in 1912 is one of the most infamous maritime disasters in history, and this dataset has been widely used as a benchmark for predictive modeling.

A machine learning model designed to classify emails as spam or not spam (ham). This project uses natural language processing (NLP) techniques to process email text data and machine learning algorithms.

An AI Fragrance Recommendation Project basing on the user preference.

The main purpose of this repository is to build the pipeline for training of regression models and predict the compressive strength of concrete to reduce the risk and cost involved in discarding the concrete structures when the concrete cube test fails.

A simple Python script to check the strength of a password based on length, the inclusion of numbers, special characters, and upper/lower case letters.