Update pages/Life Cycle Of Machine Learning.py

pages/Life Cycle Of Machine Learning.py

@@ -4,7 +4,7 @@ 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.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. It should clearly explain:**
     - What the problem is
@@ -21,7 +21,7 @@ if st.button("Problem Statement"):
     - Expected Outcome: A model that predicts the price of a house given its features.
     """)
 
-if st.button("Data Collection"):
+if st.button("**Data Collection**"):
     st.write("""
     **Collecting data is the first and most important step in any machine learning project. This is where you gather the information needed to train your machine learning model.**
     
@@ -43,7 +43,7 @@ if st.button("Data Collection"):
        - Example: For customer churn prediction, make sure there are no missing customer details like age or usage data.
       """)
 
-if st.button("Simple EDA"):
+if st.button("**Simple EDA**"):
     st.write("""
     **EDA (Exploratory Data Analysis) is the process of exploring your data to understand its structure, patterns, and insights before building a machine learning model.Think of it as getting to know your data better!**
     
@@ -74,7 +74,7 @@ if st.button("Simple EDA"):
         - Example: Do students with high math marks also score high in science?
     """)
 
-if st.button("Data Pre-processing"):
+if st.button("**Data Pre-processing**"):
     st.write("**Data preprocessing is the process of cleaning and preparing raw data so it can be used by a machine learning model. It ensures that the data is in the right format, free from errors, and ready for analysis.**")
     st.write("""
     **Why is Data Preprocessing Important?**
@@ -114,7 +114,7 @@ if st.button("Data Pre-processing"):
         - Example: Split 80% of the data for training and 20% for testing.
         """)
 
-if st.button("Exploratory Data Analysis (EDA)"):
+if st.button("**Exploratory Data Analysis (EDA)**"):
     st.write("**EDA in Machine Learning (Easy Language)EDA (Exploratory Data Analysis) is like getting to know your dataset before using it in a machine learning model. It helps you understand the data's structure, patterns, and relationships to decide how to process and use it effectively.**")
     st.write("""
     Why is EDA Important?
@@ -148,7 +148,7 @@ if st.button("Exploratory Data Analysis (EDA)"):
         """)
 
 if st.button("**Feature Engineering**"):
-    st.write("Feature engineering is the process of creating, modifying, or selecting features (columns) in your dataset to make machine learning models work better. Features are the input data that the model uses to learn and make predictions.")
+    st.write("**Feature engineering is the process of creating, modifying, or selecting features (columns) in your dataset to make machine learning models work better. Features are the input data that the model uses to learn and make predictions.**")
     st.write("""
     Why is Feature Engineering Important?
     - Improves model accuracy and performance.
@@ -183,8 +183,8 @@ if st.button("**Feature Engineering**"):
         - Use methods like correlation analysis, feature importance scores, or PCA (Principal Component Analysis).
         """)
 
-if st.button("Training"):
-    st.write("Training a machine learning model is the process of teaching the model to make predictions by learning patterns in the data. This is done by showing the model examples (training data) and adjusting it so it performs well.")
+if st.button("**Training**")
+    st.write("**Training a machine learning model is the process of teaching the model to make predictions by learning patterns in the data. This is done by showing the model examples (training data) and adjusting it so it performs well.**")
     st.write("""
     **Steps in Training a Model:**
     - **1.Prepare the Data**
@@ -211,7 +211,7 @@ if st.button("Training"):
         - Mean Squared Error (MSE): Average error for numerical predictions.
         """)
 
-if st.button("Testing"):
+if st.button("**Testing**"):
     st.write("**Testing a machine learning model is the process of checking how well the model works on new, unseen data. This step helps you understand if the model can make accurate predictions or decisions when applied to real-world scenarios.**")
     st.write("""
     **Why Testing is Important?**
@@ -241,7 +241,7 @@ if st.button("Testing"):
         - Example: If the model struggles with students with low attendance, you might need more training data for that group.
         """)
 
-if st.button("Deployment"):
+if st.button("**Deployment**"):
     st.write("**Deployment is the process of making a trained machine learning model available for real-world use. It allows people or systems to use the model to make predictions or decisions on new data.**")
     st.write("""
     **Why Deployment is Important:**
@@ -273,7 +273,7 @@ if st.button("Deployment"):
         - Example: A house price prediction model might need updates as market trends change.
         """)
 
-if st.button("Monitoring"):
+if st.button("**Monitoring**"):
     st.write("**Monitoring a machine learning model means keeping track of how well it performs after it has been deployed. It helps you make sure the model continues to give accurate predictions when used in the real world.**")
     st.write("""
     **Why Monitoring is Important:**