Update pages/LIFE_CYCLE_OF_MACHINE_LEARNING.py

pages/LIFE_CYCLE_OF_MACHINE_LEARNING.py

@@ -147,44 +147,50 @@ def unstructured_data_page():
     - Social media posts
     """)
 
-    ### Handling Image Data Section
+   
+
+    # Button to Navigate to Introduction to Image
+    if st.button("Introduction to Image"):
+        st.session_state.page = "introduction_to_image"
+
+       ###Handling Image Data Section
     st.header("🖼️ Handling Image Data")
     st.markdown("""
     Image data can be processed using libraries like OpenCV and PIL (Pillow). Images often need to be preprocessed for tasks like analysis, classification, or feature extraction. Common operations include:
-    - **Reading and displaying images**
-    - **Converting to grayscale**
-    - **Resizing and cropping**
-    - **Rotating and flipping**
-    - **Applying filters**
-    - **Edge detection and other transformations**
-    """)
+    
+Reading and displaying images
+Converting to grayscale
+Resizing and cropping
+Rotating and flipping
+Applying filters
+Edge detection and other transformations
+""")
 
     st.code("""
 from PIL import Image
 import numpy as np
 import matplotlib.pyplot as plt
 
-# Open an image file
+Open an image file
 image = Image.open('sample_image.jpg')
 image.show()
 
-# Convert image to grayscale
+Convert image to grayscale
 gray_image = image.convert('L')
 gray_image.show()
 
-# Resize the image
+Resize the image
 resized_image = image.resize((200, 200))
 resized_image.show()
 
-# Rotate the image by 90 degrees
+Rotate the image by 90 degrees
 rotated_image = image.rotate(90)
 rotated_image.show()
 
-# Convert the image to a NumPy array and display its shape
+Convert the image to a NumPy array and display its shape
 image_array = np.array(image)
 print(image_array.shape)
-
-# Display the image array as a plot
+Display the image array as a plot
 plt.imshow(image)
 plt.title("Original Image")
 plt.axis('off')
@@ -192,32 +198,31 @@ plt.show()
     """, language='python')
 
     st.markdown("""
-    **Common Image Processing Techniques:**
-    - **Resizing**: Adjust the dimensions of an image for uniformity in models.
-    - **Cropping**: Extract a region of interest (ROI) from an image.
-    - **Grayscale Conversion**: Simplify image data by reducing it to a single channel.
-    - **Rotation/Flipping**: Perform augmentations to increase the dataset for model training.
-    - **Edge Detection**: Identify edges in images using filters like the Sobel or Canny filters.
-    """)
+    Common Image Processing Techniques:
+    
+Resizing: Adjust the dimensions of an image for uniformity in models.
+Cropping: Extract a region of interest (ROI) from an image.
+Grayscale Conversion: Simplify image data by reducing it to a single channel.
+Rotation/Flipping: Perform augmentations to increase the dataset for model training.
+Edge Detection: Identify edges in images using filters like the Sobel or Canny filters.
+""")
 
     ### Challenges and Solutions Section
     st.markdown("### Challenges with Unstructured Data")
     st.write("""
-    - *Noise and Inconsistency*: Data is often incomplete or noisy.
-    - *Storage Requirements*: Large size and variability in data types.
-    - *Processing Time*: Analyzing unstructured data is computationally expensive.
-    """)
+    
+Noise and Inconsistency: Data is often incomplete or noisy.
+Storage Requirements: Large size and variability in data types.
+Processing Time: Analyzing unstructured data is computationally expensive.
+""")
 
     st.markdown("### Solutions")
     st.write("""
-    - *Data Cleaning*: Preprocess data to remove noise.
-    - *Efficient Storage*: Use NoSQL databases (e.g., MongoDB) or cloud storage.
-    - *Parallel Processing*: Utilize frameworks like Apache Spark.
-    """)
-
-    # Button to Navigate to Introduction to Image
-    if st.button("Introduction to Image"):
-        st.session_state.page = "introduction_to_image"
+    
+Data Cleaning: Preprocess data to remove noise.
+Efficient Storage: Use NoSQL databases (e.g., MongoDB) or cloud storage.
+Parallel Processing: Utilize frameworks like Apache Spark.
+""")
 
     # Navigation Button
     if st.button("Back to Data Collection"):