Ai Sem3

This repository serves primarily as a collection of educational or exploratory scripts demonstrating core functionalities of the Python data science ecosystem (Pandas, NumPy, Matplotlib, Scikit-learn, Streamlit). * Adopt descriptive file naming; numerical scripts impede modular organization and clarity for long-term codebase collaboration. * Refactor `app.py` by implementing `@st.cache_data` for efficient global data loading and improved application responsiveness. * Pipeline implementation demonstrates strong ML methodology, correctly integrating feature scaling with standard classifier training workflows. * Isolate data processing logic into reusable functions, clearly separating data preparation from analysis and script entry points. * Comprehensive visualization stack (Seaborn, Plotly, Matplotlib) caters excellently to diverse analytical reporting requirements. * The Streamlit app execution model requires modularization; data fetching happens globally upon module import, reducing efficiency.