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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.
Data Preprocessing, Data Cleaning, Fine-tuning the Hyperparameters,
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 feelings more openly than ever before. By automatically analysing customer feedback, from survey responses to social media conversations, brands are able to listen attentively to their customers, and tailor products and services to meet their needs.
Label-Free Model Evaluation and Weighted Uncertainty Sample Selection for Domain Adaptive Instance Segmentation
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.
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.
Predicting the age of crabs using machine learning techniques based on physical characteristics.
Titanic Machine Learning from Disaster
Semantic Similarity on SNLI dataset using BERT as well as TF-IDF+BERT(Pooled) embeddings.
This is a machine learning model built in python3 to predict transaction conversion of web visits for an e-commerce website.
I developed the model to attain the predictive analysis in this task.
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.
The aim of this project is to predict fraudulent credit card transactions using machine learning models.
Predicting house prices using linear regression models based on different features.
This Project is about to identify if a person's back pain is abnormal or normal using collected physical spine details/data.
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.
The aim of this project is to solve a Supervised Image Classification problem of classifying the flower types - rose, daisy, dandelion, sunflower, & tulip which can predict the class of the flower using the Convolutional Neural Networks (CNN), ResNet50 and transfer learning
Our group project aimed to evaluate three predictive machine learning classification models to anticipate whether website visitors engage in transactions. This is done by analysing different attributes of website visitors including duration spent on different web pages, click rates, and bounce rates.
Car Insights with Machine Learning
This repo hosts an end-to-end machine learning project designed to cover the full lifecycle of a data science initiative. The project encompasses a comprehensive approach including data Ingestion, preprocessing, exploratory data analysis (EDA), feature engineering, model training and evaluation, hyperparameter tuning, and cloud deployment.
A Python Machine Learning Project designed to predict Halloween Candy sales for a company based on historical data
Training a model to predict whether a given job posting is fake or not
The tasks I was required to complete as a part of the BCG Open-Access Data Science & Advanced Analytics Virtual Experience Program are all contained in this repository. This virtual internship was sponsored by Forage📊📈📉👨💻
A web application that employs machine learning models to provide accurate and instant car price estimations based on various features and specifications.
Analyzing twitter data to realize changes to the bitcoin stock from tweets published. Model was able to accurately measure the changes to the bitcoin stock with 60% accuracy.
Build and Deploy a binary classification model as a plagiarism detector
Binary classification and Multiclass classification with pipelining and parameter tuning with GridsearchCV and RandomizedSearchCV
The given data includes airline reviews from 2016 to 2019 for popular airlines around the world with multiple choice and free text questions. Data is scrapped in spring2019.The main objective is to predict whether passengers will refer the airline to their friends.
An advanced machine learning project deploying a model for Titanic passenger survival prediction, including deployment on ngrok for easy access.
This project focuses on the classification of banknotes using various supervised machine learning models. The primary objective is to develop a robust system that can accurately distinguish between genuine and counterfeit banknotes based on a set of features.
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
Yes-Bank-Stock-Closing-Price-Prediction refers to a type of project or task in the field of data science and machine learning that involves developing predictive models to estimate the Closing Price of stock