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

Model Evaluation is the process through which we quantify the quality of a system’s predictions. To do this, we measure the newly trained model performance on a new and independent dataset. This model will compare labeled data with it’s own predictions.

To import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. I will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them.

I developed a sophisticated ML model using LLMs to predict user preferences in chatbot interactions.implemented a comprehensive data preprocessing pipeline,including feature extraction and encoding,to optimize performance. conducted extensive hyperparameter tuning and evaluation, enhancing accuracy and in AI-driven conversational systems.

Label-Free Model Evaluation and Weighted Uncertainty Sample Selection for Domain Adaptive Instance Segmentation

Data Preprocessing, Data Cleaning, Fine-tuning the Hyperparameters,

BC4AI：Blockchain Used to Guarantee Credibility of AI Model Evaluations;利用区块链来保证算法模型的真实性

A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" which is usually computer-controlled wherein the user enters the payment information, and the system unlocks it.

Data Science Assignment Module 2 (K-Fold Cross Validation)

The process of computationally identifying and categorizing opinions expressed in a piece of text, especially to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral. Understanding people’s emotions is essential for businesses since customers are able to express their thoughts and fe…

This repository contains code for evaluating different machine learning models for classifying fake news. The dataset used for this evaluation consists of labeled news articles as either "REAL" or "FAKE". Three popular classifiers, Support Vector Machine (SVM), Decision Tree, and Logistic Regression, are trained and evaluated on this dataset.

This repo contains a comprehensive tutorial on machine learning with practical implementations and examples using Python.

This repository contains mini projects inData science in python with notebook files

This project implements a Convolutional Neural Network (CNN) to recognize handwritten digits from the MNIST dataset using PyTorch.

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

A machine learning project to predict loan defaults in a German bank's customer base. Using the German Credit Risk dataset, it explores key factors contributing to defaults and trains models like Random Forest, GBM, and XGBoost. Includes EDA, data processing, hyperparameter tuning, and model evaluation.

The objective of this project is to recognize hand gestures using state-of-the-art neural networks.

Building a model to predict demand of shared bikes. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels.

I developed the model to attain the predictive analysis in this task.

Binary classification and Multiclass classification with pipelining and parameter tuning with GridsearchCV and RandomizedSearchCV