Update pages/Machine Learning vs Deep Learning.py

pages/Machine Learning vs Deep Learning.py

@@ -26,17 +26,69 @@ st.markdown("""
 
 # Adding the table comparison content
 markdown_content = """
-# Machine Learning vs Deep Learning
-| Features                | Machine Learning (ML)                                      | Deep Learning (DL)                                      |
-|----------------------|------------------------------------------------------------|---------------------------------------------------------|
-| **Learning Approach** | Uses a statistical approach to analyze data and make predictions. | Uses neural networks to automatically learn patterns. |
-| **Data Requirement** | Works well with smaller datasets.                          | Requires large amounts of data to perform well.         |
-| **Feature Engineering** | Requires manual feature selection and extraction.       | Automatically learns features from raw data.            |
-| **Interpretability** | Easier to interpret and explain model decisions.          | Harder to interpret due to complex layers in the network. |
-| **Computation Power** | Can run on CPUs (low computational power).               | Requires GPUs/TPUs (high computational power).         |
-| **Algorithms Used** | Uses models like KNN, Decision Trees, Linear Regression.  | Uses ANN, CNN, RNN for feature extraction and learning. |
-| **Training Time** | Faster training due to simpler computations.                | Longer training time due to deep layers and complex processing. |
-| **Data Types Processed** | Works with structured/tabular data.                  | Works with images, videos, text, and audio.            |
+    <style>
+        table {
+            font-size: 20px;
+            width: 100%;
+            border-collapse: collapse;
+        }
+        table, th, td {
+            border: 2px solid black;
+            padding: 10px;
+            text-align: left;
+        }
+        th {
+            background-color: #f2f2f2;
+        }
+    </style>
+    <h3>Machine Learning vs Deep Learning</h3>
+    <table>
+        <tr>
+            <th>Features</th>
+            <th>Machine Learning (ML)</th>
+            <th>Deep Learning (DL)</th>
+        </tr>
+        <tr>
+            <td><b>Learning Approach</b></td>
+            <td>Uses a statistical approach to analyze data and make predictions.</td>
+            <td>Uses neural networks to automatically learn patterns.</td>
+        </tr>
+        <tr>
+            <td><b>Data Requirement</b></td>
+            <td>Works well with smaller datasets.</td>
+            <td>Requires large amounts of data to perform well.</td>
+        </tr>
+        <tr>
+            <td><b>Feature Engineering</b></td>
+            <td>Requires manual feature selection and extraction.</td>
+            <td>Automatically learns features from raw data.</td>
+        </tr>
+        <tr>
+            <td><b>Interpretability</b></td>
+            <td>Easier to interpret and explain model decisions.</td>
+            <td>Harder to interpret due to complex layers in the network.</td>
+        </tr>
+        <tr>
+            <td><b>Computation Power</b></td>
+            <td>Can run on CPUs (low computational power).</td>
+            <td>Requires GPUs/TPUs (high computational power).</td>
+        </tr>
+        <tr>
+            <td><b>Algorithms Used</b></td>
+            <td>Uses models like KNN, Decision Trees, Linear Regression.</td>
+            <td>Uses ANN, CNN, RNN for feature extraction and learning.</td>
+        </tr>
+        <tr>
+            <td><b>Training Time</b></td>
+            <td>Faster training due to simpler computations.</td>
+            <td>Longer training time due to deep layers and complex processing.</td>
+        </tr>
+        <tr>
+            <td><b>Data Types Processed</b></td>
+            <td>Works with structured/tabular data.</td>
+            <td>Works with images, videos, text, and audio.</td>
+        </tr>
+    </table>
 """
 
-st.markdown(markdown_content)
+st.markdown(markdown_content, unsafe_allow_html=True)