import streamlit as st

st.header(":red[**Life Cycle Of Machine Learning Project**]")
st.write(":blue[Click the buttons below to explore detailed steps involved in an ML project:]")

# Problem Statement Section
if st.button("**Problem Statement**"):
    st.write("""
        **A problem statement in machine learning defines the specific issue you want to solve using data and machine learning techniques.**
        
        **Key Elements of a Problem Statement:**
        - What the problem is
        - Why solving it is important
        - What data is available
        - What the expected outcome will look like
        
        **Example - Predicting House Prices:**
        - **Problem:** Predict house prices based on size, location, number of bedrooms, etc.
        - **Why:** Helps buyers and real estate agents make informed decisions.
        - **Data:** Historical data on house prices and features.
        - **Expected Outcome:** A predictive model for house prices.
    """)
    st.markdown("[Learn more about Problem Statements](https://huggingface.co/spaces/shwetashweta05/Zero_to_Hero_Machine_Learning)")

# Data Collection Section
if st.button("**Data Collection**"):
    st.write("""
        **Data collection involves gathering relevant data to solve your ML problem.**
        - Identify the source of data (e.g., sensors, databases, web scraping).
        - Ensure data quality and relevance.
        - Examples include datasets for image classification, sales prediction, etc.
    """)
    st.markdown("[Learn more about Data Collection](https://huggingface.co/spaces/shwetashweta05/Zero_to_Hero_Machine_Learning)")

# Simple EDA Section
if st.button("**Simple EDA**"):
    st.write("**Exploring data for initial insights and understanding.**")

# Data Preprocessing Section
if st.button("**Data Pre-processing**"):
    st.write("**Cleaning and preparing data for analysis.**")

# Exploratory Data Analysis Section
if st.button("**Exploratory Data Analysis (EDA)**"):
    st.write("**In-depth data analysis to discover patterns and relationships.**")

# Feature Engineering Section
if st.button("**Feature Engineering**"):
    st.write("**Creating or transforming features to improve model performance.**")

# Training Section
if st.button("**Training**"):
    st.write("**Building and training machine learning models.**")

# Testing Section
if st.button("**Testing**"):
    st.write("**Evaluating model performance on test data.**")

# Deployment Section
if st.button("**Deployment**"):
    st.write("**Deploying the model for real-world use.**")

# Monitoring Section
if st.button("**Monitoring**"):
    st.write("**Continuously tracking model performance and making improvements.**")