abhi227070
🤖 AI/ML Programmer | 🐍 Python Expert | 🚀 Innovator in Machine Learning, Deep Learning, NLP and fine-tuning LLM's
Rayagada, Odisha, India
Pinned Repositories
Customer-Segmentation-Using-KMeans
Utilizing KMeans clustering, this project segments customers for targeted marketing and analysis. Developed on Google Colab, it imports datasets from Kaggle, performs data analysis, preprocessing, and model building, providing actionable insights for businesses.
Data-Extraction-from-Database-using-AI
QueryGenie.AI is a Generative AI tool to extract data from a database by using simple Natural Language. This tool takes question in the form of natural language and convert that into SQL query then it shows both SQL query and exact answer as output to the user.
Face-Mask-Detection
Face Mask Detection is a deep learning project that utilizes Convolutional Neural Networks (CNNs) to classify images as either wearing a mask or not. With applications in public health and safety compliance, this project aids in enforcing mask-wearing guidelines during pandemics like COVID-19.
Fine-Tuning-BERT-for-Text-Classification
In the realm of medical research and diagnostics, the classification and analysis of cancer-related data hold immense importance. Leveraging state-of-the-art natural language processing (NLP) techniques, this project endeavors to classify textual data associated with cancer-related information using BERT - a powerful LLM developed by Google.
Image-Generation-Using-GAN-Gen-AI-Project-
Gen AI uses GANs to generate CIFAR-10-like images. The custom GAN model comprises a Generator and a Discriminator. Users can train the model and generate images using Jupyter Notebooks or Google Colab.
ML-Model-Deployment-in-AWS
This project deploys a diabetes prediction model on AWS using MLOps principles. It features a Flask-based UI for user interaction and utilizes CI/CD pipelines for automated deployment. By leveraging AWS infrastructure, the project ensures scalability, version control, and monitoring of the deployed model.
Movie-Recommendation-System
The Movie Recommendation System is an advanced machine learning project developed in Python, aimed at providing tailored movie suggestions to users based on their preferences and viewing habits. Leveraging various machine learning algorithms and data processing techniques, this system offers a personalized and enriched movie-watching experience.
Multiple-Disease-Prediction
Predictive Disease Analysis: Utilizes machine learning algorithms to forecast diabetes, heart disease, and breast cancer based on input parameters. Hyperparameter-tuned models enhance accuracy, offering valuable insights for early detection and proactive healthcare. Ideal for medical industry applications.
Next-Word-Prediction-Using-LSTM
This project predicts the next word in a sequence of text, aiding users in generating coherent sentences. Through data preprocessing, model building, training, and prediction, it suggests contextually relevant words based on input text.
Question-Answering-from-a-Given-Paragraph-using-BERT
This project is about creating a QA (Question-Answering) system using BERT, a powerful language model by Google. The goal is to train BERT to understand a given text passage and accurately respond to questions related to that passage. This system can be invaluable for tasks requiring efficient information retrieval and comprehension.
abhi227070's Repositories
abhi227070/Image-Generation-Using-GAN-Gen-AI-Project-
Gen AI uses GANs to generate CIFAR-10-like images. The custom GAN model comprises a Generator and a Discriminator. Users can train the model and generate images using Jupyter Notebooks or Google Colab.
abhi227070/Custom-Question-Answering-Chatbot-using-Langchain-and-Gemini-AI
This project builds a custom question answering chatbot using Langchain and Google Gemini Language Model (LLM). It fine-tunes industrial data for accurate responses and integrates Streamlit for user interaction, aiming to enhance user experience.
abhi227070/Data-Extraction-from-Database-using-AI
QueryGenie.AI is a Generative AI tool to extract data from a database by using simple Natural Language. This tool takes question in the form of natural language and convert that into SQL query then it shows both SQL query and exact answer as output to the user.
abhi227070/ML-Model-Deployment-in-AWS
This project deploys a diabetes prediction model on AWS using MLOps principles. It features a Flask-based UI for user interaction and utilizes CI/CD pipelines for automated deployment. By leveraging AWS infrastructure, the project ensures scalability, version control, and monitoring of the deployed model.
abhi227070/Customer-Segmentation-Using-KMeans
Utilizing KMeans clustering, this project segments customers for targeted marketing and analysis. Developed on Google Colab, it imports datasets from Kaggle, performs data analysis, preprocessing, and model building, providing actionable insights for businesses.
abhi227070/Email-Spam-Detection
Email Spam Classifier: A real-time project that utilizes natural language processing (NLP), data analytics, and machine learning (ML) techniques to classify emails as spam or legitimate based on their text content.
abhi227070/Face-Mask-Detection
Face Mask Detection is a deep learning project that utilizes Convolutional Neural Networks (CNNs) to classify images as either wearing a mask or not. With applications in public health and safety compliance, this project aids in enforcing mask-wearing guidelines during pandemics like COVID-19.
abhi227070/Fine-Tuning-BERT-for-Text-Classification
In the realm of medical research and diagnostics, the classification and analysis of cancer-related data hold immense importance. Leveraging state-of-the-art natural language processing (NLP) techniques, this project endeavors to classify textual data associated with cancer-related information using BERT - a powerful LLM developed by Google.
abhi227070/ML-Model-Deployment-in-Flask-API
This project develops a Flask API for predicting diabetes based on parameters like blood sugar level, BMI, and blood pressure. The API enables integration with healthcare systems for diagnosis and research purposes. It doesn't include a UI and can be accessed via Postman or similar tools.
abhi227070/Movie-Recommendation-System
The Movie Recommendation System is an advanced machine learning project developed in Python, aimed at providing tailored movie suggestions to users based on their preferences and viewing habits. Leveraging various machine learning algorithms and data processing techniques, this system offers a personalized and enriched movie-watching experience.
abhi227070/Multiple-Disease-Prediction
Predictive Disease Analysis: Utilizes machine learning algorithms to forecast diabetes, heart disease, and breast cancer based on input parameters. Hyperparameter-tuned models enhance accuracy, offering valuable insights for early detection and proactive healthcare. Ideal for medical industry applications.
abhi227070/Next-Word-Prediction-Using-LSTM
This project predicts the next word in a sequence of text, aiding users in generating coherent sentences. Through data preprocessing, model building, training, and prediction, it suggests contextually relevant words based on input text.
abhi227070/Question-Answering-from-a-Given-Paragraph-using-BERT
This project is about creating a QA (Question-Answering) system using BERT, a powerful language model by Google. The goal is to train BERT to understand a given text passage and accurately respond to questions related to that passage. This system can be invaluable for tasks requiring efficient information retrieval and comprehension.
abhi227070/Stock-Price-Forecasting-using-LSTM
This project uses LSTM neural networks to forecast Google Stock prices. Data is fetched from Tiingo using pandas datareader, preprocessed, and used to train the model. It's designed to run on Google Colab with GPU acceleration for faster training.
abhi227070/Whatsapp-Chat-Analyzer
The WhatsApp Chat Analyzer is a project that leverages machine learning and natural language processing techniques to analyze chat data from WhatsApp conversations. It provides insights such as message statistics, sentiment analysis, word clouds, and more.
abhi227070/abhi227070
abhi227070/car-price-prediction
This project implements a machine learning model to predict the price of cars based on various features such as mileage, manufacturing date, fuel type, and more. Users can input car information, and the model will estimate the price of the car based on the provided data. This tool can be useful for both car buyers and sellers to estimate car price.
abhi227070/Cat-VS-Dog
Cat VS Dog Classifier: Train a machine to identify whether an image contains a cat or a dog with up to 96% accuracy using deep learning techniques and the VGG16 model.
abhi227070/Customer-Churn-Prediction
Customer churn, also known as customer attrition, is a critical business concern. This project aims to predict customer churn based on a dataset's attributes. The project involves data analysis, preprocessing, feature engineering, model building, and deployment of a user interface for interaction.
abhi227070/Emotion-Detection-of-text-using-LSTM-Internship-Project
Emotion Detection of Text using LSTM: This internship project focuses on detecting the emotion of a sentence as positive, negative, or neutral using Long Short-Term Memory (LSTM) networks, a type of deep learning model. Based on these emotions, the overall sentiment of a platform or user experience is calculated from users' feedback.
abhi227070/First-flask-api
This beginner-level project introduces Flask API development. It features a simple API endpoint where users can submit two numbers in JSON format and receive their sum. The project serves as a learning tool for understanding Flask API basics, JSON data handling, and API testing with tools like Postman.
abhi227070/Git-Practice
abhi227070/IPL-2024-SOLD-PLAYER-DATA-ANALYSIS
This project analyzes IPL 2024 auctioned players' data, including name, team, cricket type, nationality, and price. Users input a player's name to access team, style, nationality, and auction price, aiding research and fantasy leagues. It offers insights into player dynamics, serving cricket enthusiasts with comprehensive data exploration.
abhi227070/Medical-Insurance-Predictor
This project implements a machine learning regression model to predict medical insurance charges based on user-provided details such as smoking status, number of children, gender, and age. The user-friendly interface allows individuals to estimate their average insurance price before purchasing medical insurance.
abhi227070/ml-project-with-ml-ops
abhi227070/Salary-Price-Prediction
Salary Prediction API using Flask predicts salaries for freshers joining organizations based on factors like past experience, company switches, courses completed, and academic marks. This Flask-based API allows users to input their details and receive a salary prediction. With no user interface, it's designed for integration into other applications
abhi227070/Sentiment-Analysis
Utilizing machine learning algorithms and natural language processing (NLP) techniques such as TF-IDF vectorization and stemming, this project analyzes the sentiment of comments. The tool provides insights into the overall sentiment of products or videos based on the comments section, helping businesses gain valuable feedback on their products.
abhi227070/Wine-Quality-Prediction
The Wine Quality Prediction project utilizes machine learning to assess wine quality based on various parameters. It offers a user-friendly interface built with Streamlit for easy interaction. Deployed on AWS, it provides scalable access to quality predictions for wine batches.