Add Applied ML movie score prediction project

- Integrated comprehensive machine learning project with CatBoost regression
- Added detailed project description highlighting 17% performance improvement
- Included model comparison and optimization progress visualizations
- Added Machine Learning filter category to project portfolio
- Featured technical achievements: Bayesian optimization, FastAPI deployment, Docker containerization
- Showcased end-to-end ML pipeline from preprocessing to production deployment

Author: coding-crying

images/README.md

@@ -11,6 +11,8 @@ This directory should contain all your portfolio images:
 - `ecommerce-platform.jpg` - Detailed screenshot for projects page
 - `task-manager.jpg` - Mobile app screenshots
 - `hoody-robot.jpg` - Hoody smart kitchen hood robot (3D model or prototype)
+- `ml-model-comparison.png` - Machine learning model performance comparison chart
+- `ml-optimization-progress.png` - Hyperparameter optimization progress visualization
 - `weather-api.jpg` - API documentation or interface screenshot
 - `blog-platform.jpg` - Blog platform screenshot
 - `code-generator.jpg` - CLI tool screenshot

images/ml-model-comparison.png

@@ -62,12 +62,12 @@ <h3>E-Commerce Platform</h3>
                     </div>
 
                     <div class="project-card">
-                        <img src="images/project2.jpg" alt="Project 2">
+                        <img src="images/ml-model-comparison.png" alt="ML Model Performance">
                         <div class="project-info">
-                            <h3>Task Management App</h3>
-                            <p>React Native mobile app with real-time collaboration features</p>
+                            <h3>Movie Score Prediction ML System</h3>
+                            <p>CatBoost regression model achieving 17% improvement over baseline</p>
                             <div class="project-links">
-                                <a href="#" class="btn btn-small">Live Demo</a>
+                                <a href="#" class="btn btn-small">View Demo</a>
                                 <a href="#" class="btn btn-small btn-outline">GitHub</a>
                             </div>
                         </div>

images/ml-optimization-progress.png

@@ -40,6 +40,7 @@ <h1>My Projects</h1>
                     <button class="filter-btn" data-filter="api">APIs</button>
                     <button class="filter-btn" data-filter="tools">Tools</button>
                     <button class="filter-btn" data-filter="robotics">Robotics</button>
+                    <button class="filter-btn" data-filter="ml">Machine Learning</button>
                 </div>
             </div>
         </section>
@@ -84,27 +85,41 @@ <h4>Key Features:</h4>
                         </div>
                     </div>
 
-                    <div class="project-card" data-category="mobile">
+                    <div class="project-card" data-category="ml">
                         <div class="project-image">
-                            <img src="images/task-manager.jpg" alt="Task Management App">
+                            <img src="images/ml-model-comparison.png" alt="Movie Score Prediction ML Model">
                             <div class="project-overlay">
                                 <div class="project-links">
-                                    <a href="#" class="btn btn-primary">Live Demo</a>
+                                    <a href="#" class="btn btn-primary">View Demo</a>
                                     <a href="#" class="btn btn-outline">GitHub</a>
                                 </div>
                             </div>
                         </div>
                         <div class="project-info">
-                            <h3>Task Management App</h3>
+                            <h3>Movie Score Prediction ML System</h3>
                             <p class="project-description">
-                                Cross-platform mobile app for team collaboration with 
-                                real-time updates and offline synchronization.
+                                End-to-end machine learning pipeline using CatBoost regression to predict IMDb 
+                                movie scores. Achieved 17% improvement over baseline with comprehensive 
+                                hyperparameter optimization and production deployment.
                             </p>
                             <div class="tech-stack">
-                                <span class="tech-tag">React Native</span>
-                                <span class="tech-tag">Firebase</span>
-                                <span class="tech-tag">Redux</span>
-                                <span class="tech-tag">AsyncStorage</span>
+                                <span class="tech-tag">CatBoost</span>
+                                <span class="tech-tag">Optuna</span>
+                                <span class="tech-tag">FastAPI</span>
+                                <span class="tech-tag">Streamlit</span>
+                                <span class="tech-tag">Docker</span>
+                                <span class="tech-tag">Python</span>
+                            </div>
+                            <div class="project-features">
+                                <h4>Key Achievements:</h4>
+                                <ul>
+                                    <li>17% performance improvement over baseline (Test RMSE: 0.70)</li>
+                                    <li>Bayesian hyperparameter optimization with 50 trials</li>
+                                    <li>Production-ready FastAPI deployment with Docker</li>
+                                    <li>Comprehensive evaluation with 5-fold cross-validation</li>
+                                    <li>Interactive Streamlit web interface for predictions</li>
+                                    <li>Robust preprocessing pipeline with outlier detection</li>
+                                </ul>
                             </div>
                         </div>
                     </div>