Body Area Network (BAN) This repository implements a machine learning model for anomaly detection in body sensor data collected through a Body Area Network (BAN). The model analyzes heart rate and body temperature readings to identify potential health concerns.

Works by building many individual decision trees and combining their outputs. For a final answer, it takes the average (for numbers) or majority vote (for categories) of all trees, making it stable and reliable.

Fertilizer prediction Api (https://predict-fertilizer-api.onrender.com/predict)

Quantifying Integrity in the Digital Age Misinformation spreads rapidly, accountability often falters, and the lines between transparency and manipulation blur

Also builds decision trees, but does so sequentially. Each new tree is built to specifically correct the errors made by the ones before it, allowing the model to become progressively more accurate.

An LSTM is a type of AI model designed to understand sequences and time-based patterns. Its key feature is a "memory" that lets it remember important information from the past (like previous traffic jams) to make better predictions about the future.

A high-performance gradient boosting algorithm that specializes in handling categorical data (like location names or event types) directly. It excels at understanding "context" without needing complex data preprocessing.

This project is a Content-Based Movie Recommendation System that recommends movies to users based on similarity of overview, genres, keywords, cast, and crew. The system uses TF-IDF, Count Vectorization, Cosine Similarity, and NLP techniques to compute how similar two movies are.

An unsupervised learning algorithm that analyzes data to find hidden groups or structures. Instead of predicting a number, it classifies roads into distinct "Traffic Profiles" (e.g., "Quiet Residential" vs. "Major Hub") based on their historical patterns.