Update pages/Life Cycle Of Machine Learning.py

pages/Life Cycle Of Machine Learning.py

@@ -1,45 +1,66 @@
 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:]")
+st.write(":blue[Click the buttons below to explore detailed steps involved in an ML project:]")
 
+# Problem Statement Section
 if st.button("**Problem Statement**"):
-    if st.link_button("**Problem statement of Machine Learning**","https://huggingface.co/spaces/shwetashweta05/Zero_to_Hero_Machine_Learning/edit/main/pages/Life%20Cycle%20Of%20Machine%20Learning.py"):
-        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.
+    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**"):
-    if st.link_button("**About Data Collection of Machine Learning**","https://huggingface.co/spaces/shwetashweta05/Zero_to_Hero_Machine_Learning/edit/main/pages/Life%20Cycle%20Of%20Machine%20Learning.py"):
-        st.write("About the data")
-    
-#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**"):
+    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.**")