Update pages/8Model Training.py

pages/8Model Training.py

@@ -34,6 +34,7 @@ st.markdown("""
 st.markdown("""
 <h4 style='color:#BB3385;'>For Example</h4>
 Think of yourself as a teacher, and the machine as a student.You show math problems (inputs) and answers (outputs). The student starts to learn patterns.
+
 Just like that:
 - Machine = student
 - Data = problem
@@ -45,14 +46,16 @@ After training, the model is ready to solve new problems.
 
 st.markdown("""
 <h3 style='color:#2a52be;'>Who Are We Actually Training?</h3>
-<p>We are training machines to learn — not robots or humans, but something called a <strong>machine learning model</strong>.This model is like a smart system that doesn’t know anything in the beginning. It needs examples and a method to understand those examples.</p>
+<p>We are training machines to learn — not robots or humans, but something called a <strong>machine learning model</strong>.</p>
+<p>This model is like a smart system that doesn’t know anything in the beginning. It needs examples and a method to understand those examples.</p>
 
 <p>As programmers, the machine is guided to learn by providing:</p>
 
 <p>▶ <strong>Data</strong> – the examples it should learn from</p>
 <p>▶ <strong>Algorithm</strong> – the method it should use to learn from the data</p>
 
-<p>With the right guidance, the machine can learn how to make decisions on its own.The machine follows the steps given by the algorithm to learn from the data. If the learning doesn’t go well, we usually don’t change the data. Instead, we try using a better algorithm that suits the data.So, how we guide the machine using the algorithm is very important for its learning.</p>
+<p>With the right guidance, the machine can learn how to make decisions on its own.The machine follows the steps given by the algorithm to learn from the data.</p>
+<p>If the learning doesn’t go well, we usually don’t change the data. Instead, we try using a better algorithm that suits the data.So, how we guide the machine using the algorithm is very important for its learning.</p>
 """, unsafe_allow_html=True)
 
 
@@ -85,5 +88,5 @@ st.markdown("""
 <p>- One set for <strong>testing</strong>: used to check how well the model learned.</p>
 
 <p>Common ways to split the data include: 80% training & 20% testing, 70% training & 30% testing, or 60% training & 40% testing.</p>
-<p>The split should be random so that every data point has a fair chance. A data point should appear in only one of the two sets.After splitting, these names are used:</p>
+<p>The split should be random so that every data point has a fair chance. A data point should appear in only one of the two sets.</p>
 """, unsafe_allow_html=True)