import streamlit as st

def run():
    st.title("Data Preparation")
    st.header("Introduction")
    st.write("""
    Data Preparation involves cleaning and transforming raw data into a format suitable for analysis.
    """)
    st.header("Objectives")
    st.write("""
    - Clean the data.
    - Transform the data.
    - Integrate data from multiple sources.
    """)
    st.header("Key Activities")
    st.write("""
    - Data cleaning.
    - Data transformation.
    - Data integration.
    """)
    import pandas as pd

    st.title("3. Data Preparation")
    st.write("## Overview")
    st.write("Cleaning and transforming data for better model performance.")

    st.write("## Key Concepts & Explanations")
    st.markdown("""
    - red[**Handling Missing Values**]: Fill with mean/median or drop rows.  
    - **Feature Engineering**: Creating new features for better modeling.  
    - **Scaling**: Normalization and standardization for consistency.
    """)

    file = st.file_uploader("Upload a dataset", type=["csv"])
    if file:
        df = pd.read_csv(file)
        option = st.radio("Choose a method to handle missing values", ["Fill with Mean", "Fill with Median", "Drop Rows"])
        if option == "Fill with Mean":
            df.fillna(df.mean(), inplace=True)
        elif option == "Fill with Median":
            df.fillna(df.median(), inplace=True)
        elif option == "Drop Rows":
            df.dropna(inplace=True)

    st.write("## Quiz: Conceptual Questions")
    q1 = st.radio("What is feature engineering?", ["Data visualization", "Creating new features", "Data storage"])
    if q1 == "Creating new features":
        st.success("✅ Correct!")
    else:
        st.error("❌ Incorrect.")

    st.write("## Code-Based Quiz")
    code_input = st.text_area("Write a function to normalize a column", value="def normalize(col):\n    return (col - col.min()) / (col.max() - col.min())")
    if "col.max() - col.min()" in code_input:
        st.success("✅ Correct!")
    else:
        st.error("❌ Try again.")

    st.write("## Learning Resources")
    st.markdown("""
    - 📝 [Data Cleaning with Pandas](https://realpython.com/pandas-data-cleaning/)
    """)