AI-powered system to predict metro interstate traffic volume using historical traffic and weather data. Features include time-based and weather-based inputs, Random Forest regression, interactive Streamlit app, and model persistence for easy deployment and real-time predictions.

Web application using Flask and Machine Learning to predict industrial compressor performance (OCP Group). Features Excel data analysis.

This repository contains a Jupyter notebook that predicts whether Nvidia's stock price will increase or decrease tomorrow. The project leverages machine learning models, specifically a Random Forest Classifier, to make predictions based on today’s stock data.

A smart question tagging and matching system using traditional ML and NLP. It finds similar questions, tags them, and groups related ones to reduce duplicates. Built with TF-IDF, cosine similarity , KMeans & XgBoost, GBDT,Logistic Regression for fast, scalable, and real-world use.

Este proyecto desarrolla un modelo de clasificación binaria cuyo objetivo es estimar la probabilidad de que un cliente realice al menos un pago, a partir de información financiera y comportamental consolidada a nivel cliente.

End-to-end machine learning solution to predict insurance premiums based on customer demographics and policy details. Includes data preprocessing, regression modeling, ML pipelines, experiment tracking with MLflow, and real-time prediction app deployment using Streamlit.