SleepIQ is a Machine Learning-based Sleep Quality Predictor web application built using Python, Flask, and Random Forest algorithm. It analyzes key lifestyle and behavioral factors such as sleep duration, stress level, exercise, screen time, caffeine intake, and mood to classify a person's sleep quality as Good, Average, or Poor.
Email/SMS spam detector with a Flask UI/API, tuned ML models (TF‑IDF + SVM/LogReg/NB), and a ready-to-run web form plus JSON endpoint for predictions.
FraudWatch is a machine learning-based credit card fraud detection system that uses a Random Forest classifier. It visualizes model performance with an interactive confusion matrix heatmap. The system is deployed as a user-friendly Flask web application. 📊
This project delivers a complete web app for obesity risk assessment using a multi-class ML model. Built with FastAPI, it integrates JWT-secured authentication, a PostgreSQL database, and Docker deployment. Patients receive predictions and history tracking, while admins manage users and monitor system activity.
edX Capstone Project: ML-based prediction of shelter animal outcomes using R and Random Forest
The 'Machine Failure Predictor' is a web application designed to enable predictive maintenance for industrial equipment. Leveraging machine learning techniques, this application predicts whether equipment will fail and identifies the type of failure based on key process parameters.
Machine learning based potential distribution of Siam weed in Bangladesh
machine learning project under the JHU course and analyzing the exercise dataset and predict the type of the exercise they do.
Malware analysis of files, detect the type of malware and check whether its a malware or not by using virus total.
Ipny and HTML files EDA Case Studies
In this project i used many supervised learning algorithms available in scikit-learn, and also provided a method of evaluating, just how each model works and performs on a certain type of data.
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