Hi, I'm Habibullah Salmani

Built a regression model to predict house prices using a housing dataset. Includes data cleaning, feature engineering, model training and evaluation. The trained model is exported as a .pkl file and integrated with a simple UI for predictions.

Developed a movie recommendation system in Python that suggests relevant movies based on user preferences and similarity logic. The project includes data processing, recommendation algorithms, and a simple interface to explore and test movie recommendations, along with clear documentation.

Built a Machine Learning–based Rock vs Mine classification system that predicts whether an object detected by SONAR signals is a Rock or a Mine. The model is trained on the SONAR dataset using supervised learning algorithms and deployed as an interactive Streamlit web application for real-time predictions.

Developed a Fake News Detection system using Natural Language Processing (NLP) and Machine Learning to classify news articles as real or fake. The project uses TF-IDF vectorization for text feature extraction and a Logistic Regression model trained on labeled news data, achieving an accuracy of 98.6%. The model is deployed as an interactive Streamlit web application for real-time news verification.

Developed a Machine Learning–based Diabetes Prediction system that predicts whether a person is diabetic or not based on medical input parameters. The project includes data preprocessing with feature scaling using StandardScaler and a supervised classification model trained on a diabetes dataset. The trained model is deployed as an interactive Streamlit web application for real-time predictions.

Built a fully functional Telegram-based food ordering bot using Python and python-telegram-bot v20. The bot allows users to browse a food menu, place orders step-by-step, select quantity, choose payment methods, and instantly notifies the owner with complete order details. Deployed on Render for 24×7 availability.