Realtime Sign Language Detection: Deep learning model for accurate, real-time recognition of sign language gestures using Python and TensorFlow.

This repository contains a basic fraud detection system utilising supervised learning techniques to identify potentially fraudulent credit card transactions. The project establishes a baseline model that addresses the challenges of credit card fraud in financial institutions.

Welcome to the Machine Learning Repository! This repository is a collection of notebooks showcasing various machine learning projects and implementations. It incluedes Decision tree algorithm, Random forest , Support vector machine etc.

This project fine-tunes the LLaMA-3.1-8B model using LoRA adapters for parameter-efficient training. It leverages chat templates for conversation structuring, utilizes 4-bit quantization for memory efficiency, and saves the fine-tuned model for deployment on the Hugging Face Hub.

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.

AlmaBetter Capstone Project -Classification model to predict the sentiment of COVID-19 tweets. The tweets have been pulled from Twitter and manual tagging has been done then.

A Deep Learning application designed to detect plant 🌱diseases using images of plant leaves🌿, powered by TensorFlow technology.

Machine Learning model capable of accurately predicting the Rate of Interest (ROI) from bureau data

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

An advanced machine learning project deploying a model for Titanic passenger survival prediction, including deployment on ngrok for easy access.

This is a Data Science task related to kaggle challenge of Titanic Spaceship

This repository serves as a comprehensive resource for understanding and implementing various feature selection techniques, gaining familiarity with Jupyter Notebook, and mastering the process of model training and evaluation

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.

MBTI Personality Prediction from Text Data This project leverages machine learning to predict Myers-Briggs Type Indicator (MBTI) personality types based on textual data, specifically from social media posts.

This project utilizes a machine learning model where consumer brand data is employed. Initially, a preliminary model is developed, followed by a refined model using a process called 'fine-tuning' to improve results. Additionally, a comprehensive testing suite has been created to validate accuracy and reliability of the model's predictions.

This project aims to predict the prices of cars based on various features such as year of manufacture, brand, mileage, and other relevant factors. Leveraging machine learning algorithms, this project explores different regression techniques to create an accurate model for car price prediction.

Predicting heart failure using Decision Tree algorithm with a dataset sourced from Kaggle. Achieved 99% accuracy, demonstrating robust performance as a binary classifier.