A Streamlit web app utilizing Python, scikit-learn, and pandas for used car price prediction. Features data preprocessing (scaling, encoding), Random Forest model optimization with GridSearchCV, and interactive user input handling. Achieves high accuracy (R² score: 0.9028), showcasing skills in machine learning, data engineering, and deployment.

This project predicts California housing prices using machine learning regression models, including Random Forests and Decision Trees. It covers data preprocessing, exploratory analysis, model training, and hyperparameter tuning to optimize performance.

Analisis Sentimen Teks Singkat Bahasa Indonesia dengan Hyperparameter Random Forest