pages/Life Cycle Of Machine Learning.py

import streamlit as st
import numpy as np 
import pandas as pd

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

if st.button("**Problem Statement**"):
    if st.link_button("**Problem statement of Machine Learning**"):
        st.write("""
        *A problem statement in machine learning defines the specific issue you want to solve using data and machine learning techniques. It should clearly explain:**
    - What the problem is
    - Why solving it is important
    - What data is available
    - What the expected outcome will look like
        """)
        st.write("""
         **Examples of ML Problem Statements:**
    - **Predicting House Prices:**
    - Problem: We want to predict the price of houses based on features like size, location, number of bedrooms, etc.
    - Why: This helps buyers make informed decisions and real estate agents price houses correctly.
    - Data: Historical data about house prices and their features.
    - Expected Outcome: A model that predicts the price of a house given its features.
    """)
    
if st.button("**Data Collection**"):
    if st.link_button("**About Data Collection of Machine Learning**"):
        st.write("")
    
#if st.button("**Simple EDA**"):
   
#if st.button("**Data Pre-processing**"):
   
#if st.button("**Exploratory Data Analysis (EDA)**"):
    
#if st.button("**Feature Engineering**"):

#if st.button("**Training**"):
   
#if st.button("**Testing**"):
    
#if st.button("**Deployment**"):
   
#if st.button("**Monitoring**"):