Use Terraform to set up infrastructure described in AWS's example of fraud detection with SageMaker. https://docs.aws.amazon.com/solutions/latest/fraud-detection-using-machine-learning/architecture.html

TF-IDF Fraud Call Detector: A machine learning-based system to classify call transcripts as fraudulent or non-fraudulent using TF-IDF and classifiers like SVM and Random Forest. It analyzes call content for patterns associated with fraud, useful for telecommunication and customer service fraud detection.

This project develops a fraud detection system using machine learning applied to a transactional dataset sourced from Kaggle.

AI - powered Spam & Fraud Detection Web Application using Flask and Machine Learning (TF - IDF + Multinomial Naive Bayes).

Chat Fraud Detection system using Machine Learning (TF-IDF + Logistic Regression) with Flask web application to classify messages as Spam or Ham.

Fraud Detection Web App built using Machine Learning and Streamlit. The model uses TF-IDF vectorization and Random Forest with threshold tuning to accurately classify fraudulent text messages.

Machine Learning projects using Python & Scikit-learn: SMS Spam Detection (TF-IDF + Logistic Regression), Credit Card Fraud Detection (Random Forest + SMOTE), and Bank Customer Churn Prediction. Includes preprocessing, visualization & model evaluation.

Developed machine learning models for fraud detection and multi-label movie genre classification using Python, NLP, TF-IDF, and Naive Bayes, including data preprocessing and model evaluation.

A machine learning project to detect auto insurance fraud using structured claim data and NLP-based text analysis. Combines SMOTE, TF-IDF, and models like Random Forest, Decision Tree, and Logistic Regression to improve fraud detection accuracy and efficiency.

Fraud detection project that identifies fake job advertisements using machine learning. The EMSCAD Fake Job Postings dataset is analysed using TF-IDF text features and encoded metadata. Logistic Regression and SVM classifiers are used to classify legitimate and fraudulent job listings

This project applies machine learning to detect fake news by analyzing article text. Data is cleaned, transformed with TF-IDF, and classified using Logistic Regression. The system achieves reliable accuracy, automates fraud detection, and helps reduce misinformation online.

Email Fraud Detection is a machine learning project that classifies emails as legitimate or fraudulent. It uses Natural Language Processing (NLP) and TF-IDF to analyze email content, training a model to detect phishing or spam emails and enhance online security.

A real-time email fraud and phishing detection system built using Machine Learning and Natural Language Processing (NLP). The project preprocesses email text, extracts features using TF-IDF, and classifies emails as safe or phishing using a Naive Bayes model. A Flask web application provides instant predictions with confidence scores.

This project is an AI-based fraud detection web app using Flask and Machine Learning to classify job descriptions as fraudulent or legitimate in real time. It uses TF-IDF vectorization, a trained ML model, and MongoDB to store and analyze prediction results for future monitoring.

Built a scalable, real-time anomaly detection system for transaction monitoring using unsupervised machine learning. The system applies Isolation Forest to detect fraudulent patterns without labelled training data — ideal for real-world financial environments where fraud labels are scarce. Includes NLP feature extraction (TF-IDF, Bag-of-Words)

Fake Job Detection System is a Streamlit-based web app that uses machine learning to detect fraudulent job postings. It takes job data as input, predicts if a listing is real or fake using a Logistic Regression model with TF-IDF features, and displays results with fraud scores, charts, and keyword insights.