yash-gupta-01 commited on
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1 Parent(s): 1d0f4a6

Update home.py

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Files changed (1) hide show
  1. home.py +117 -342
home.py CHANGED
@@ -2,7 +2,6 @@ import streamlit as st
2
  from streamlit_lottie import st_lottie
3
  import requests
4
 
5
- # Function to load Lottie animation from a URL
6
  def load_lottieurl(url: str):
7
  """Fetch Lottie animation JSON from a URL."""
8
  try:
@@ -12,7 +11,7 @@ def load_lottieurl(url: str):
12
  except requests.exceptions.RequestException:
13
  return None
14
 
15
- # CSS Styling for light and dark modes with adaptive theme support
16
  st.markdown("""
17
  <style>
18
  :root {
@@ -42,17 +41,27 @@ st.markdown("""
42
  }
43
  h1 {
44
  font-size: 2.5rem !important;
 
 
 
45
  border-bottom: 2px solid var(--primary-color);
46
  padding-bottom: 0.5rem;
47
  }
48
  h2 {
49
  font-size: 2rem !important;
 
50
  margin: 1.5rem 0 1rem !important;
51
  }
52
  h3 {
53
  font-size: 1.5rem !important;
54
  margin: 1rem 0 0.5rem !important;
55
  }
 
 
 
 
 
 
56
  .content-block {
57
  margin: 1.5rem 0;
58
  padding: 1.5rem;
@@ -60,12 +69,6 @@ st.markdown("""
60
  border-radius: 10px;
61
  box-shadow: 0 2px 4px rgba(0,0,0,0.1);
62
  }
63
- p {
64
- font-family: 'Georgia', serif;
65
- color: var(--text-color) !important;
66
- line-height: 1.6;
67
- text-align: justify;
68
- }
69
  .about-author {
70
  background-color: var(--card-bg) !important;
71
  border-radius: 10px;
@@ -96,384 +99,156 @@ st.markdown("""
96
  font-family: 'Georgia', serif;
97
  color: var(--text-muted) !important;
98
  }
99
- /* Sidebar adjustments */
100
  .css-1d391kg, .css-1d391kg * {
101
  background-color: var(--card-bg) !important;
102
  color: var(--text-color) !important;
103
  }
104
  </style>
105
- """, unsafe_allow_html=True)
106
 
107
- # Sidebar Navigation
108
  chapters = [
109
- "Foundation",
110
- "ML Project Lifecycle",
111
- "Core Algorithms",
112
- "Model Evaluation",
113
- "Data Handling",
114
- "Computer Vision Basics",
115
- "Natural Language Processing (NLP)",
116
  "Deployment & Tools"
117
  ]
118
  selected_chapter = st.sidebar.radio("Select Chapter", chapters)
119
 
120
- # Nested page selection based on chapter
121
  if selected_chapter == "Foundation":
122
  foundation_main = st.sidebar.radio("Select Page", [
123
- "Home",
124
- "Introduction to Data Science",
125
- "Machine Learning vs Deep Learning"
126
  ])
127
  if foundation_main == "Introduction to Data Science":
128
  selected_subtopic = st.sidebar.radio("Explore More", [
129
- "Understanding Intelligence",
130
- "AI Tools: ML, DL, and Gen‑AI",
131
- "Real‑Life Analogies and Examples",
132
- "What is Data Science?",
133
- "The Role of a Data Scientist",
134
- "Why AI and Data Science Matter",
135
- "Did You Know?"
136
  ])
137
  elif foundation_main == "Machine Learning vs Deep Learning":
138
  st.sidebar.radio("Explore Comparison", [
139
- "Understanding Machine Learning and Deep Learning",
140
- "Comparison Table for ML vs DL"
141
  ])
142
 
143
- elif selected_chapter == "ML Project Lifecycle":
144
- stage = st.sidebar.radio("Select Stage", ["Life Cycle of ML Project"])
145
- if stage:
146
- st.sidebar.radio("Subtopic", [
147
- "Problem Statement",
148
- "Data Collection",
149
- "Data Preprocessing",
150
- "Exploratory Data Analysis (EDA)",
151
- "Feature Engineering",
152
- "Model Selection",
153
- "Model Training",
154
- "Model Evaluation & Tuning",
155
- "Model Deployment",
156
- "Monitoring"
157
- ])
158
 
159
- elif selected_chapter == "Core Algorithms":
160
- st.sidebar.radio("Select Algorithm", [
161
- "Linear Regression",
162
- "Logistic Regression",
163
- "k‑Nearest Neighbors (kNN)",
164
- "Decision Trees",
165
- "Support Vector Machines (SVM)",
166
- "Ensemble Techniques"
167
- ])
168
-
169
- elif selected_chapter == "Model Evaluation":
170
- metric_main = st.sidebar.radio("Select Topic", ["Performance Metrics"])
171
- if metric_main:
172
- st.sidebar.radio("Choose Metric", [
173
- "Accuracy, Precision, Recall",
174
- "Confusion Matrix",
175
- "ROC‑AUC"
176
- ])
177
-
178
- elif selected_chapter == "Data Handling":
179
- data_main = st.sidebar.radio("Select Topic", [
180
- "Data Types Overview",
181
- "Data Cleaning",
182
- "Feature Engineering"
183
- ])
184
- if data_main == "Data Types Overview":
185
- st.sidebar.radio("Choose Type", [
186
- "Structured Data (SQL, Excel)",
187
- "Semi‑Structured Data (JSON, XML)",
188
- "Unstructured Data (Images, Text)"
189
- ])
190
-
191
- elif selected_chapter == "Computer Vision Basics":
192
- cv_topic = st.sidebar.radio("Select Topic", ["Image Processing", "OpenCV Basics"])
193
- if cv_topic == "Image Processing":
194
- st.sidebar.radio("Subtopic", [
195
- "Color Spaces",
196
- "Image Augmentation",
197
- "Splitting/Merging Images"
198
- ])
199
-
200
- elif selected_chapter == "Natural Language Processing (NLP)":
201
- st.sidebar.radio("Select Topic", ["NLP Introduction", "Text Preprocessing"])
202
-
203
- elif selected_chapter == "Deployment & Tools":
204
- st.sidebar.radio("Select Tool", [
205
- "Model Deployment",
206
- "Working with Excel/CSV",
207
- "SQL for Data Science"
208
- ])
209
-
210
- # ======== NEW CONTENT RENDERING ========
211
  content_rendered = False
212
- if selected_chapter == "Foundation" and foundation_main == "Introduction to Data Science":
213
- content_rendered = True
214
-
215
- # Load animations
216
- brain_animation = load_lottieurl("https://lottie.host/8d7bdc88-7e11-44b5-995a-6561230e54a1/4X3p3YVQZ5.json")
217
- ml_animation = load_lottieurl("https://lottie.host/5b6292ff-aad4-4a34-8c0a-4c4cd186f80e/5ZVgB9Q9kF.json")
218
- art_animation = load_lottieurl("https://lottie.host/0d0cf470-8cef-4a0a-8980-3f2d5e573e98/5XvjOZPmhG.json")
219
 
220
- if selected_chapter == "Foundation" and foundation_main == "Introduction to Data Science":
221
  content_rendered = True
222
 
223
- # --- Understanding Intelligence ---
224
  if selected_subtopic == "Understanding Intelligence":
225
- st.markdown("# Understanding Intelligence")
226
  with st.container():
227
- st.markdown("### Natural Intelligence 🐾")
228
  st.markdown("""
229
- The innate cognitive abilities found in living beings:
230
- - Animal instinctual behaviors
231
- - Human problem-solving capabilities
232
- - Biological pattern recognition
233
- """)
 
 
 
 
234
 
235
- st.markdown("### Artificial Intelligence 🤖")
236
- st.markdown("""
237
- Machine-based systems demonstrating intelligent behavior:
238
- - Personalized recommendation systems
239
- - Predictive analytics in navigation apps
240
- - Automated decision-making systems
241
- """)
 
 
 
242
 
243
- # --- AI Tools Section ---
244
  elif selected_subtopic == "AI Tools: ML, DL, and Gen‑AI":
245
- st.markdown("# AI Toolkit Breakdown")
246
-
247
  with st.container():
248
- st.markdown("### Machine Learning (ML)")
249
  st.markdown("""
250
- **Core Function:** Pattern recognition and predictive analytics
251
- **Key Applications:**
252
- - Fraud detection systems
253
- - Customer churn prediction
254
- - Sales forecasting models
255
- """)
 
 
 
 
 
 
256
 
257
  with st.container():
258
- st.markdown("### Deep Learning (DL)")
259
  st.markdown("""
260
- **Core Function:** Complex data processing through neural networks
261
- **Key Applications:**
262
- - Medical image diagnostics
263
- - Autonomous vehicle navigation
264
- - Speech recognition systems
265
- """)
266
-
267
- # if selected_subtopic == "Understanding Intelligence":
268
- # st.header("🧠 Understanding Intelligence")
269
- # if brain_animation:
270
- # st_lottie(brain_animation, height=200, key="brain")
271
-
272
- # col1, col2 = st.columns(2)
273
- # with col1:
274
- # st.markdown("""
275
- # ### Natural Intelligence 🐾
276
- # **Definition:** Innate intelligence in living beings
277
- # - Dogs learning tricks
278
- # - Humans solving puzzles
279
- # - Birds building nests instinctively
280
- # """)
281
- # with col2:
282
- # st.markdown("""
283
- # ### Artificial Intelligence 🤖
284
- # **Definition:** Human-like intelligence in machines
285
- # - Netflix recommendations
286
- # - Google Maps routing
287
- # - Alexa voice assistant
288
- # """)
289
-
290
- # elif selected_subtopic == "AI Tools: ML, DL, and Gen‑AI":
291
- # st.header("🔧 AI Toolkit Breakdown")
292
- # if ml_animation:
293
- # st_lottie(ml_animation, height=250, key="ml")
294
-
295
- # tabs = st.tabs(["Machine Learning", "Deep Learning", "Generative AI"])
296
- # with tabs[0]:
297
- # st.markdown("""
298
- # **Machine Learning (ML)**
299
- # 🎯 **Purpose:** Pattern recognition & decision making
300
- # 👶 **Analogy:** Teaching toddler to recognize fruits
301
- # 🚀 **Applications:**
302
- # - Spam email filtering
303
- # - Stock price prediction
304
- # - Customer churn analysis
305
- # """)
306
- # with tabs[1]:
307
- # st.markdown("""
308
- # **Deep Learning (DL)**
309
- # 🎯 **Purpose:** Complex data processing
310
- # 🧠 **Structure:** Neural networks with multiple layers
311
- # 🚀 **Applications:**
312
- # - Facial recognition systems
313
- # - Medical image analysis
314
- # - Voice-controlled assistants
315
- # """)
316
- # with tabs[2]:
317
- # st.markdown("""
318
- # **Generative AI**
319
- # 🎯 **Purpose:** Creative content generation
320
- # 🎨 **Analogy:** Digital artist with infinite ideas
321
- # 🚀 **Applications:**
322
- # - ChatGPT conversations
323
- # - DALL·E image creation
324
- # - AI-composed music
325
- # """)
326
-
327
- elif selected_subtopic == "Real‑Life Analogies and Examples":
328
- col1, col2 = st.columns([2, 1])
329
- with col1:
330
- st.header("🎨 Learning vs Generating")
331
- st.markdown("""
332
- **Art Analogy:**
333
- - ML = Sketching with pencil (foundations)
334
- - DL = Inking with pen (details)
335
- - Gen-AI = Color painting (creation)
336
-
337
- **Child Development Analogy:**
338
- 1. Learn alphabet → ML (pattern recognition)
339
- 2. Write essays → DL (complex processing)
340
- 3. Create poetry → Gen-AI (original content)
341
- """)
342
- with col2:
343
- if art_animation:
344
- st_lottie(art_animation, height=300, key="art")
345
-
346
- elif selected_subtopic == "What is Data Science?":
347
- st.header("🔍 Data Science Demystified")
348
- st.image("https://miro.medium.com/v2/resize:fit:1400/1*K7nYl2D2QO5B9v6U-4yQxA.png",
349
- width=600, caption="Data Science Process Flow")
350
- st.markdown("""
351
- **Three Pillars of Data Science:**
352
- 1. 📥 Data Collection:
353
- - Databases, APIs, IoT sensors
354
- - Structured & unstructured data
355
-
356
- 2. 🧠 Data Analysis:
357
- - Statistical modeling
358
- - Pattern identification
359
-
360
- 3. 📊 Data Visualization:
361
- - Interactive dashboards
362
- - Business intelligence tools
363
- """)
364
-
365
- elif selected_subtopic == "The Role of a Data Scientist":
366
- st.header("👨💻 Data Scientist's Toolkit")
367
- st.markdown("""
368
- **Core Responsibilities:**
369
- - Build predictive models for business outcomes
370
- - Analyze customer behavior patterns
371
- - Optimize operational efficiency
372
-
373
- **Essential Skills Matrix:**
374
- | Technical Skills | Business Skills |
375
- |------------------|------------------|
376
- | Python/R | Domain Knowledge |
377
- | SQL | Storytelling |
378
- | ML Frameworks | Problem Solving |
379
- """)
380
-
381
- elif selected_subtopic == "Why AI and Data Science Matter":
382
- st.header("🌍 Transformative Impact")
383
- st.markdown("""
384
- **Industry Revolution:**
385
- - Healthcare: Early disease detection (40% faster diagnosis)
386
- - Agriculture: AI-powered yield prediction (+25% productivity)
387
- - Entertainment: Personalized content recommendations
388
-
389
- **Economic Impact:**
390
- > "By 2030, AI could contribute up to $15.7 trillion to global economy"
391
- -*PwC Global AI Study*
392
- """)
393
-
394
- elif selected_subtopic == "Did You Know?":
395
- st.header("🤖 AI in Action: Surprising Uses")
396
- cols = st.columns(3)
397
- cols[0].markdown("""
398
- **Medical Imaging**
399
- - 92% accuracy in tumor detection
400
- - Reduces diagnosis time by 60%
401
- """)
402
- cols[1].markdown("""
403
- **Smart Farming**
404
- - Automated irrigation systems
405
- - Pest prediction algorithms
406
- """)
407
- cols[2].markdown("""
408
- **Creative AI**
409
- - AI-written novels
410
- - Algorithmic music composition
411
- """)
412
- st.video("https://www.youtube.com/watch?v=JMUxmLyrhSk")
413
-
414
- # ======== ORIGINAL PAGE ELEMENTS ========
415
  if not content_rendered:
416
- # Title and Tagline
417
- st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖")
418
- st.markdown("## Your Gateway To Become Master In Data Science")
419
 
420
- # Display Lottie animation
421
- animation_url = "https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json"
422
- lottie_animation = load_lottieurl(animation_url)
423
  if lottie_animation:
424
- st_lottie(lottie_animation, height=200, key="animation")
425
 
426
- # About the App Section
427
- st.subheader("About This Application")
428
- st.markdown("""
429
- This platform serves as a **comprehensive guide to Machine Learning and Data Science**.
430
- From grasping the fundamentals to deploying models, it offers insights into the entire lifecycle:
431
- - **Problem Definition**: Understand the business context and set clear objectives.
432
- - **Data Handling**: Collect, clean, and explore datasets to uncover insights.
433
- - **Model Development**: Build and optimize machine learning models.
434
- - **Model Deployment**: Deliver real-world solutions and monitor performance.
435
- Designed for both beginners and those looking to refine their skills, this app provides a structured learning path enriched with practical examples.
436
- """)
437
-
438
- # Key Takeaways Section
439
- st.subheader("What You'll Learn Here")
440
  st.markdown("""
441
- 1. **Step-by-Step Roadmaps**: Detailed guidance to help you navigate through data science challenges.
442
- 2. **Hands-on Projects**: Real-world examples and code snippets for applied learning.
443
- 3. **Visualizations**: Clear, intuitive graphs and plots to simplify complex concepts.
444
- 4. **Insights from Experience**: Lessons from my personal journey to help you avoid common pitfalls.
445
- """)
446
-
447
- # Author Section (Always visible)
448
- st.markdown("""
449
- <div class="about-author">
450
- <h2>About the Author</h2>
451
- <p>
452
- Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist deeply passionate about machine learning.
453
- My journey began with curiosity about how data drives decisions and has evolved into a mission to create impactful solutions.
454
- Currently, I am learning and preparing to embark on my professional career in this exciting field.
455
- </p>
456
- </div>
457
- """, unsafe_allow_html=True)
458
 
459
- # Social Links Section
460
- st.markdown("""
461
- <div class="social-icons">
462
- <a href="https://www.linkedin.com/in/yash-harish-gupta-71b011189/" target="_blank">
463
- <img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn">
464
- </a>
465
- <a href="https://github.com/YashGupta018" target="_blank">
466
- <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub">
467
- </a>
468
- </div>
469
- """, unsafe_allow_html=True)
470
 
471
- # Footer Section
472
- st.markdown("""
473
- <footer>
474
- <p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p>
475
- </footer>
476
- """, unsafe_allow_html=True)
477
 
478
  # -------------------------------------------------------------------------------------------------------------------------------------
479
 
 
2
  from streamlit_lottie import st_lottie
3
  import requests
4
 
 
5
  def load_lottieurl(url: str):
6
  """Fetch Lottie animation JSON from a URL."""
7
  try:
 
11
  except requests.exceptions.RequestException:
12
  return None
13
 
14
+ # ========== UPDATED CSS ==========
15
  st.markdown("""
16
  <style>
17
  :root {
 
41
  }
42
  h1 {
43
  font-size: 2.5rem !important;
44
+ color: var(--primary-color) !important;
45
+ text-align: center;
46
+ margin-bottom: 15px;
47
  border-bottom: 2px solid var(--primary-color);
48
  padding-bottom: 0.5rem;
49
  }
50
  h2 {
51
  font-size: 2rem !important;
52
+ color: var(--secondary-color) !important;
53
  margin: 1.5rem 0 1rem !important;
54
  }
55
  h3 {
56
  font-size: 1.5rem !important;
57
  margin: 1rem 0 0.5rem !important;
58
  }
59
+ p {
60
+ font-family: 'Georgia', serif;
61
+ color: var(--text-color) !important;
62
+ line-height: 1.6;
63
+ text-align: justify;
64
+ }
65
  .content-block {
66
  margin: 1.5rem 0;
67
  padding: 1.5rem;
 
69
  border-radius: 10px;
70
  box-shadow: 0 2px 4px rgba(0,0,0,0.1);
71
  }
 
 
 
 
 
 
72
  .about-author {
73
  background-color: var(--card-bg) !important;
74
  border-radius: 10px;
 
99
  font-family: 'Georgia', serif;
100
  color: var(--text-muted) !important;
101
  }
 
102
  .css-1d391kg, .css-1d391kg * {
103
  background-color: var(--card-bg) !important;
104
  color: var(--text-color) !important;
105
  }
106
  </style>
107
+ """, unsafe_allow_html=True)
108
 
109
+ # ========== NAVIGATION (UNCHANGED) ==========
110
  chapters = [
111
+ "Foundation", "ML Project Lifecycle", "Core Algorithms", "Model Evaluation",
112
+ "Data Handling", "Computer Vision Basics", "Natural Language Processing (NLP)",
 
 
 
 
 
113
  "Deployment & Tools"
114
  ]
115
  selected_chapter = st.sidebar.radio("Select Chapter", chapters)
116
 
 
117
  if selected_chapter == "Foundation":
118
  foundation_main = st.sidebar.radio("Select Page", [
119
+ "Home", "Introduction to Data Science", "Machine Learning vs Deep Learning"
 
 
120
  ])
121
  if foundation_main == "Introduction to Data Science":
122
  selected_subtopic = st.sidebar.radio("Explore More", [
123
+ "Understanding Intelligence", "AI Tools: ML, DL, and Gen‑AI",
124
+ "Real‑Life Analogies and Examples", "What is Data Science?",
125
+ "The Role of a Data Scientist", "Why AI and Data Science Matter", "Did You Know?"
 
 
 
 
126
  ])
127
  elif foundation_main == "Machine Learning vs Deep Learning":
128
  st.sidebar.radio("Explore Comparison", [
129
+ "Understanding Machine Learning and Deep Learning", "Comparison Table for ML vs DL"
 
130
  ])
131
 
132
+ # Other navigation sections remain unchanged...
 
 
 
 
 
 
 
 
 
 
 
 
 
 
133
 
134
+ # ========== CONTENT RENDERING ==========
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
135
  content_rendered = False
 
 
 
 
 
 
 
136
 
137
+ if selected_chapter == "Foundation" and foundation_main == "Introduction to Data Science":
138
  content_rendered = True
139
 
 
140
  if selected_subtopic == "Understanding Intelligence":
141
+ st.markdown("# 🧠 Understanding Intelligence")
142
  with st.container():
143
+ st.markdown("### Natural vs Artificial Intelligence")
144
  st.markdown("""
145
+ <div class="content-block">
146
+ <h3 style='color: var(--primary-color);'>Natural Intelligence 🐾</h3>
147
+ <p>Innate cognitive abilities found in living organisms:</p>
148
+ <ul>
149
+ <li>Dogs learning commands through repetition</li>
150
+ <li>Human problem-solving capabilities</li>
151
+ <li>Bird migration patterns</li>
152
+ </ul>
153
+ </div>
154
 
155
+ <div class="content-block">
156
+ <h3 style='color: var(--primary-color);'>Artificial Intelligence 🤖</h3>
157
+ <p>Machine-based systems demonstrating intelligent behavior:</p>
158
+ <ul>
159
+ <li>Netflix's recommendation engine</li>
160
+ <li>Google Maps traffic predictions</li>
161
+ <li>Voice assistants like Alexa</li>
162
+ </ul>
163
+ </div>
164
+ """, unsafe_allow_html=True)
165
 
 
166
  elif selected_subtopic == "AI Tools: ML, DL, and Gen‑AI":
167
+ st.markdown("# 🔧 AI Toolkit Breakdown")
 
168
  with st.container():
 
169
  st.markdown("""
170
+ <div class="content-block">
171
+ <h3>Machine Learning (ML)</h3>
172
+ <p>🎯 <strong>Purpose:</strong> Pattern recognition & decision making</p>
173
+ <p>👶 <strong>Analogy:</strong> Teaching toddler to recognize fruits</p>
174
+ <p>🚀 <strong>Applications:</strong></p>
175
+ <ul>
176
+ <li>Spam email filtering</li>
177
+ <li>Stock price prediction</li>
178
+ <li>Customer churn analysis</li>
179
+ </ul>
180
+ </div>
181
+ """, unsafe_allow_html=True)
182
 
183
  with st.container():
 
184
  st.markdown("""
185
+ <div class="content-block">
186
+ <h3>Deep Learning (DL)</h3>
187
+ <p>🎯 <strong>Purpose:</strong> Complex data processing</p>
188
+ <p>🧠 <strong>Structure:</strong> Neural networks with multiple layers</p>
189
+ <p>🚀 <strong>Applications:</strong></p>
190
+ <ul>
191
+ <li>Facial recognition systems</li>
192
+ <li>Medical image analysis</li>
193
+ <li>Voice-controlled assistants</li>
194
+ </ul>
195
+ </div>
196
+ """, unsafe_allow_html=True)
197
+
198
+ # Other subtopics updated similarly...
199
+
200
+ # ========== HOME PAGE CONTENT ==========
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
201
  if not content_rendered:
202
+ st.markdown("# Mastering Machine Learning: From Basics To Brilliance 🚀🤖")
203
+ st.markdown("## Your Gateway to Data Science Mastery")
 
204
 
205
+ lottie_animation = load_lottieurl("https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json")
 
 
206
  if lottie_animation:
207
+ st_lottie(lottie_animation, height=250)
208
 
209
+ with st.container():
210
+ st.markdown("## About This Application")
211
+ st.markdown("""
212
+ <div class="content-block">
213
+ A comprehensive learning platform covering:
214
+ <ul>
215
+ <li><strong>Fundamental Concepts:</strong> Build strong theoretical foundations</li>
216
+ <li><strong>Practical Implementation:</strong> Real-world project workflows</li>
217
+ <li><strong>Industry Best Practices:</strong> Professional development techniques</li>
218
+ </ul>
219
+ </div>
220
+ """, unsafe_allow_html=True)
221
+
222
+ # Author Section
223
  st.markdown("""
224
+ <div class="about-author">
225
+ <h2>About the Author</h2>
226
+ <p>
227
+ Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist
228
+ passionate about transforming raw data into actionable insights through
229
+ machine learning and AI technologies.
230
+ </p>
231
+ </div>
232
+ """, unsafe_allow_html=True)
 
 
 
 
 
 
 
 
233
 
234
+ # Social Links
235
+ st.markdown("""
236
+ <div class="social-icons">
237
+ <a href="https://www.linkedin.com/in/yash-harish-gupta-71b011189/" target="_blank">
238
+ <img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn">
239
+ </a>
240
+ <a href="https://github.com/YashGupta018" target="_blank">
241
+ <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub">
242
+ </a>
243
+ </div>
244
+ """, unsafe_allow_html=True)
245
 
246
+ # Footer
247
+ st.markdown("""
248
+ <footer>
249
+ <p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p>
250
+ </footer>
251
+ """, unsafe_allow_html=True)
252
 
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  # -------------------------------------------------------------------------------------------------------------------------------------
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