Pinned Repositories
agency_swarm
The only reliable agent framework built on top of the latest OpenAI Assistants API.
aws-deployment
This repository provides a comprehensive guide to deploying ML projects on AWS Cloud using Docker, ECR, EC2, and GitHub Actions. It offers step-by-step instructions for automation, CI/CD, and containerization, enabling efficient and scalable ML deployments in production environments.
Bike_Sharing_Project
This Repo uses data analysis to understand post-Covid bike demand in US market. Repository has code in Python using Pandas, NumPy, & Scikit-learn. ML algorithms used to predict demand based on gathered data. Aim is to provide insights for successful business plan & stay ahead of competition in market.
Credit_card_fraud_detection
Credit Card Fraud Detection uses ML to detect fraudulent transactions. Repository has code in Python using Pandas, NumPy, & Scikit-learn. Approaches include logistic regression, random forests & XGboost on balanced & imbalanced datasets. Aims to build a robust fraud detection system w/ accurate fraud identification & minimal false alarms.
face-similarity-pinecone
This repository hosts a cutting-edge facial recognition system designed to enhance customer identification and verification. Leveraging MTCNN for accurate face detection and DeepFace-FaceNet for facial embeddings, the system integrates with Pinecone's vector database to efficiently match and verify repeat customers.
Lead_Scoring_Project
This repo uses Python & popular libs for data analysis w/ ML algorithms to optimize lead conversion rate (typical 30%). Analyzes factors affecting conversion from website browsing, form fills, & referrals. Goal is to increase overall conversion rate.
mlpipeline
This project is a modular machine learning project that uses Python and Flask. It is a good example of how to use modular programming to make machine learning projects easier to understand, maintain, and extend.
Telecom_churn_Project
Telecom Churn analyzes customer churn in the telecom industry using ML. Repository includes complete code & data in Python using Pandas, NumPy, & Scikit-learn. Compares results of logistic regression, random forests & XGboost to determine best approach. Aim is to prevent customer loss with a predictive model.
Tweets_hate_speech_classification
This repo has a transfer learning-based model that classifies tweets as hate speech or not. Based on "nnlm-en-dim50" text embedding, it uses NLP techniques & Tensorflow/Keras. Goal: to prevent hate speech on social media, promoting a safer online environment.
NitishKundu's Repositories
NitishKundu/face-similarity-pinecone
This repository hosts a cutting-edge facial recognition system designed to enhance customer identification and verification. Leveraging MTCNN for accurate face detection and DeepFace-FaceNet for facial embeddings, the system integrates with Pinecone's vector database to efficiently match and verify repeat customers.
NitishKundu/agency_swarm
The only reliable agent framework built on top of the latest OpenAI Assistants API.
NitishKundu/aws-deployment
This repository provides a comprehensive guide to deploying ML projects on AWS Cloud using Docker, ECR, EC2, and GitHub Actions. It offers step-by-step instructions for automation, CI/CD, and containerization, enabling efficient and scalable ML deployments in production environments.
NitishKundu/Bike_Sharing_Project
This Repo uses data analysis to understand post-Covid bike demand in US market. Repository has code in Python using Pandas, NumPy, & Scikit-learn. ML algorithms used to predict demand based on gathered data. Aim is to provide insights for successful business plan & stay ahead of competition in market.
NitishKundu/Credit_card_fraud_detection
Credit Card Fraud Detection uses ML to detect fraudulent transactions. Repository has code in Python using Pandas, NumPy, & Scikit-learn. Approaches include logistic regression, random forests & XGboost on balanced & imbalanced datasets. Aims to build a robust fraud detection system w/ accurate fraud identification & minimal false alarms.
NitishKundu/Lead_Scoring_Project
This repo uses Python & popular libs for data analysis w/ ML algorithms to optimize lead conversion rate (typical 30%). Analyzes factors affecting conversion from website browsing, form fills, & referrals. Goal is to increase overall conversion rate.
NitishKundu/mlpipeline
This project is a modular machine learning project that uses Python and Flask. It is a good example of how to use modular programming to make machine learning projects easier to understand, maintain, and extend.
NitishKundu/Telecom_churn_Project
Telecom Churn analyzes customer churn in the telecom industry using ML. Repository includes complete code & data in Python using Pandas, NumPy, & Scikit-learn. Compares results of logistic regression, random forests & XGboost to determine best approach. Aim is to prevent customer loss with a predictive model.
NitishKundu/Tweets_hate_speech_classification
This repo has a transfer learning-based model that classifies tweets as hate speech or not. Based on "nnlm-en-dim50" text embedding, it uses NLP techniques & Tensorflow/Keras. Goal: to prevent hate speech on social media, promoting a safer online environment.