Predictive maintenance helps companies minimize downtime, reduce repair costs, and improve operational efficiency. Developing a web application for predictive maintenance can provide users with real-time insights into equipment performance, enabling proactive maintenance, and reducing unplanned downtime.
Onsite Health Diagnostic (OHD) is the web application that allows users to predict whether the user has been infected with a menacing disease or not. These diseases can be very dangerous to health if they are not treated properly. The main objective of OHD is to help people predict the disease in case of absence of medical professionals, strikes or any related uncertainties.
The goal is to find the probability that a comment is toxic. Built a multilingual text classification model to predict the probability that a comment is toxic based on 4,35,775 text comments in 7 different languages, using the data provided by Google Jigsaw. Fine-tuned BERT-Multilingual-base and XLMRoBERTa models on the multilingual text.
Following the pandemic, the airline industry suffered a massive setback, with ICAO estimating a 371 billion dollar loss in 2020, and a 329 billion dollar loss with reduced seat capacity. As a result, in order to revitalise the industry in the face of the current recession, it is absolutely necessary to understand the customer pain points and improve their satisfaction with the services provided.
The project aims to develop a PDF querying system that leverages LangChain, a powerful language processing tool, to extract information from PDF documents. By employing LangChain's advanced natural language understanding capabilities, the system will enable users to perform complex searches and obtain specific data points from PDF files efficiently and accurately.
Designed and built an end-to-end scalable machine learning pipeline using Mlops in which i have integrated everything on a frontend so that user can get log file,artifacts,model binary file and also save the data to the database which can help for future retraining and also you can perform complete retraining of the model by just one click.The main aim is to build an entire system of a machine learning so that it can be used in realworld scenario.
The End-goal of this project is to give people an estimate of how much they need based on their individual health situation.So here I have implemented End-End Machine Learning project using MLOPS lifecycle.Built CI/CD pipeline.DVC(Data Version Control) was used here for track ml model.It was regression problem statement and used GradientBoostingRegressor to solve the problem which gave r2_score of 85% after tuning parameters.
The Automated ML WebApp project is a tool developed using Python, Pandas Profiling, PyCaret, and Streamlit frameworks. It aims to simplify and streamline the process of building machine learning models by automating various tasks involved in data preprocessing, feature engineering, model selection, and downloading the trained models.