Streamlit Jobs

Spam Message Detection Using NLP & Machine Learning I built a machine learning tool that uses NLP to label text messages as Spam or Not Spam. The tool works with the SMS Spam Collection Dataset from the UCI Machine Learning Repository. Key Features Used a labelled set of spam and normal SMS messages. Did text cleaning and TF‑IDF Vectorization to turn words into numbers. Ran the Multinomial Naive Bayes model to predict labels. Separated the data into training and test parts to check performance. Checked outcomes with accuracy, precision, recall and F1‑score. Made a Streamlit app so you can try your own messages. Loaded the model and TF‑IDF tool with Joblib. Technologies Used Python | NLP | Pandas | Scikit‑learn TF‑IDF | Multinomial Naive Bayes | Streamlit Joblib Dataset SMS Spa...

# Collaboration Opportunity: SafeLink AI **Project:** Explainable Phishing and Scam Message Detector **Duration:** 7–14 days **Collaboration type:** Unpaid academic / portfolio collaboration **Technology:** Python, Pandas, scikit-learn, Streamlit, Joblib, Git/GitHub ## 1. Project overview I am developing SafeLink AI as a college group experiential learning project. The objective is to build a simple web application that analyses text messages, predicts whether a message belongs to the classes supported by the selected dataset, highlights observable warning indicators, and provides general safety guidance. I already have a detailed project proposal explaining the objectives, scope, methodology, mathematical logic component, evaluation metrics, and expected deliverables. I am...