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Realtime Sign Language Detection: Deep learning model for accurate, real-time recognition of sign language gestures using Python and TensorFlow.
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.
This project aims to predict the future stock prices of various companies using machine learning and deep learning techniques. By analyzing historical stock price data and incorporating relevant features, the goal is to build accurate and robust models that can forecast stock prices over different time horizons.
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.
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.
Alphabet Soup Charity: A deep learning model to predict the success of charitable donations, enhancing decision-making for fund allocation and impact optimization.
This project detects if the card holder will default on the credit payment on the following month or not by implementation of various ML Classification Algorithms in a modular coding format
One notebook trains a vegetable classification model with InceptionV3 using TensorFlow and Keras. The second notebook showcases the pre-trained model's inference on vegetable categories, loading InceptionV3 and enhancing image features. Together, they offer a compact solution for vegetable classification through deep learning.
Data Science Project
Diabetes Prediction Web App
Model Trainig To predict the Prize Of House
The Diabetes Prediction Model is a machine learning application that predicts diabetes based on input features such as pregnancies, glucose level, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function, and age. It utilizes a logistic regression algorithm for binary classification.
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