Update pages/introds.py

pages/introds.py

@@ -1,41 +1,49 @@
-import streamlit
-import pandas
-import numpy 
-st.markdown("<h1 style='text-align: center; color: Balck;'>DATA SCIENCE INTRODUCTION</h1>", unsafe_allow_html=True)
-st.markdown("<h2 style='color: Black;'>What is data science</h2>"
- 
-<div class="section">
-    <h2>What is Data Science? </h2>
-    <p>
-       It combines techniques from statistics, computer science, mathematics, and domain expertise to analyze and interpret data effectively.
-       In essence, data science transforms raw data into actionable insights, driving smarter decisions across industries.
-        There are some several types of explanations which are given below :
-     </p>
-    <ul>
-        <li>Analyzing customer reviews to identify product sentiment</li> 
-          <li>Techniques Used: Natural Language Processing (NLP), sentiment analysis.</li>
-        
-        <li>Optimizing delivery routes for logistics companies</li>
-              <li>Techniques Used: Graph algorithms, optimization techniques </li>
-    </ul>
-</div>
-<div class="section">
-    <h2>Few Important steps in DS </h2>
-    <p>
-        Data Science has several steps ,such as :
-    </p>
-    <ul>    
-         <li><strong>Understanding the given problem:</strong>First thing is you need to understand the given type of problem</li>
-         <li><strong>Data Collection:</strong> Gather relevant data from various sources to address the defined problem.</li>
-         <li><strong>Data Cleaning and Preprocessing: </strong> Prepare the raw data for analysis by handling inconsistencies, missing values, and errors. </li>
-         <li><strong>Exploratory Data Analysis (EDA):</strong>Gain an initial understanding of the datas main characteristics through visualization and summary statistics.</li>
-         <li><strong> Deployment: </strong Implement the model into a production environment where it can provide actionable insights or make decisions in real-time.> </li>
-       </ul>
-</div>
- , unsafe_allow_html=True)
-
-
-
+import streamlit as st  # Aliased streamlit as 'st' for consistency
+import pandas as pd
+import numpy as np
+
+# Header Section
+st.markdown(
+    "<h1 style='text-align: center; color: Black;'>DATA SCIENCE INTRODUCTION</h1>",
+    unsafe_allow_html=True
+)
+
+# Main Content Section
+st.markdown(
+    """
+    <h2 style='color: Black;'>What is Data Science</h2>
+     
+    <div class="section">
+        <h2>What is Data Science?</h2>
+        <p>
+           It combines techniques from statistics, computer science, mathematics, and domain expertise to analyze and interpret data effectively.
+           In essence, data science transforms raw data into actionable insights, driving smarter decisions across industries.
+           There are several types of applications, as explained below:
+        </p>
+        <ul>
+            <li>Analyzing customer reviews to identify product sentiment</li> 
+            <li>Techniques Used: Natural Language Processing (NLP), sentiment analysis.</li>
+            <li>Optimizing delivery routes for logistics companies</li>
+            <li>Techniques Used: Graph algorithms, optimization techniques.</li>
+        </ul>
+    </div>
+
+    <div class="section">
+        <h2>Few Important Steps in Data Science</h2>
+        <p>
+            Data Science involves several steps, such as:
+        </p>
+        <ul>    
+             <li><strong>Understanding the Given Problem:</strong> First, you need to understand the type of problem you are addressing.</li>
+             <li><strong>Data Collection:</strong> Gather relevant data from various sources to address the defined problem.</li>
+             <li><strong>Data Cleaning and Preprocessing:</strong> Prepare the raw data for analysis by handling inconsistencies, missing values, and errors.</li>
+             <li><strong>Exploratory Data Analysis (EDA):</strong> Gain an initial understanding of the data's main characteristics through visualization and summary statistics.</li>
+             <li><strong>Deployment:</strong> Implement the model into a production environment where it can provide actionable insights or make decisions in real-time.</li>
+        </ul>
+    </div>
+    """,
+    unsafe_allow_html=True
+)