Update pages/6_Feature_Engineering.py

pages/6_Feature_Engineering.py

@@ -190,33 +190,50 @@ if file_type == "One-Hot Vectorization":
         -  This method is useful for transforming text into a numerical format for Machine Learning tasks.
     """)
 
-    st.subheader(":green[Advantages]")
+    st.subheader(":red[Advantages]")
     st.markdown('''
     - One-Hot Vectorization is easy to implement
     ''')
-    st.subheader(":green[Disadvantages]")
-    st.markdown('''
-    - 1.Every document have different no.of words (here we're not converting document to vector , we're converting word to vector)
-        - We can't convert into tabular data
-        - It would be possible to convert into tabular data when we're converting document into vector(this is solved by Bag of Words(BOW))
-    - 2.**Sparsity** - The vector which is created using one-hhot vectorization gives sparse vector 
-        - Entire data is given to any alogorithm and machine is going to learn fom data and algorithm it is biasd towards zero values as the data is sparse data
-        - This issue in ML is known as overfitting
-        - It is solved in Deep learning
-    - 3.**Curse of Dimensionality** 
-        - Document increases ↑ Vocabulary ↑ and vector increases ↑ dimensionality also increases ↑
-        - Ml performance decreases ↓ - as the dimensionality totally depends on vocabulary and it shootup as the document increases and different
-    - 4.**Out of Vocabulary** 
-        - Document only converted during training time and we're giving our own dataset
-        - If the word is not present in our dataset while training it can't convert into vector format results in key error
-        - This is solved by Fasttext
-    - 5.**Unable to preserve semantic meaning of the words
-        - While converting text → vector format (same relationship should be preserved)
-        - We need to convert document into vector in such a way that semantic relationship should be preserved 
-        - Similarity ⬆️ and Distance ⬇️
-        - Similarity ∝ 1 / Distance
-        - Distance between vectors should be very small
-        - If this is satisfied then the technique has good semantic meaning
-    - 6.**No Sequential information**
-        - Sequential information is not preserved 
-    ''')
+    st.subheader(":red[Disadvantages]")
+    st.markdown("<h1 class='title'>Challenges in One-Hot Vectorization</h1>", unsafe_allow_html=True)
+
+    st.markdown("<h2 class='subtitle'>Different Document Lengths</h2>", unsafe_allow_html=True)
+    st.markdown(
+    "<p class='content'>Every document contains a different number of words. Here, we are not converting the entire document into a vector, but rather each word separately. "
+    "This makes it difficult to structure the data into a tabular format. Converting entire documents into vectors, which is addressed by Bag of Words (BOW), solves this issue.</p>",
+    unsafe_allow_html=True,
+    )
+
+    st.markdown("<h2 class='subtitle'>Sparsity</h2>", unsafe_allow_html=True)
+    st.markdown(
+    "<p class='content'>The vectors created using one-hot encoding tend to be sparse. When data is given to any algorithm, the model may become biased towards zero values, "
+    "leading to an issue in machine learning known as overfitting. This problem is primarily addressed in deep learning.</p>",
+    unsafe_allow_html=True,
+    )
+
+    st.markdown("<h2 class='subtitle'>Curse of Dimensionality</h2>", unsafe_allow_html=True)
+    st.markdown(
+    "<p class='content'>As the number of documents increases, the vocabulary size grows, leading to an increase in dimensionality. This negatively impacts machine learning performance "
+    "because the dimensionality of vectors is directly dependent on vocabulary size, which grows as more documents are introduced.</p>",
+    unsafe_allow_html=True,
+    )
+
+    st.markdown("<h2 class='subtitle'>Out of Vocabulary Issue</h2>", unsafe_allow_html=True)
+    st.markdown(
+    "<p class='content'>One-hot encoding only converts words that were present in the dataset at the time of training. If a new word appears during inference and was not included in the "
+    "training dataset, it cannot be converted into a vector, causing a key error. This issue is effectively solved by FastText.</p>",
+    unsafe_allow_html=True,
+    )
+
+    st.markdown("<h2 class='subtitle'>Inability to Preserve Semantic Meaning</h2>", unsafe_allow_html=True)
+    st.markdown(
+    "<p class='content'>When converting text into vector format, the relationships between words should be preserved. Ideally, similar words should be represented by similar vectors, "
+    "meaning the distance between their vectors should be small. If this is achieved, the vectorization method successfully preserves semantic meaning.</p>",
+    unsafe_allow_html=True,
+    )
+
+    st.markdown("<h2 class='subtitle'>Lack of Sequential Information</h2>", unsafe_allow_html=True)
+    st.markdown(
+    "<p class='content'>One-hot encoding does not preserve sequential information in text. The order of words, which is crucial in natural language, is completely lost in this encoding method.</p>",
+    unsafe_allow_html=True,
+    )