final proj

Author: anikasikka

docs/final_project.md

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+---
+title: CRUP Final Project
+nav_order: 3
+layout: home
+---
+
+# <span style="color: #397DFF; font-weight: 350">CRUP Fall 2025 Final Project</span>
+
+## <span style="color: #397DFF">Project Overview</span>
+You will design and execute a complete Machine Learning project that answers a real-world question and present your findings through an interactive website. This project integrates both machine learning and software engineering skills.
+
+## <span style="color: #397DFF">Core Requirements</span>
+- Formulate a specific, real-world problem that can be solved with **Machine Learning**  
+- Build and train an **ML model** using a real dataset  
+- Create an **interactive website** that explains your process and allows users to interact with your model  
+
+---
+
+# <span style="color: #397DFF; font-weight: 350">Part A: Machine Learning Component</span>
+
+## <span style="color: #397DFF">1. Problem Formulation & Dataset Selection</span>
+Your task is to identify a clear, answerable question that requires machine learning to solve.
+
+### Dataset Requirements
+- Must come from a real dataset (e.g., **Kaggle**, **Hugging Face**)  
+- Your project must be one of the following:  
+  - **Classification** (e.g., “Will this customer churn?”)  
+  - **Regression** (e.g., “What will this house sell for?”)  
+
+---
+
+## <span style="color: #397DFF">2. Complete ML Pipeline Implementation</span>
+You must implement all stages of a professional ML workflow.
+
+### a) Data Acquisition and Cleaning
+- Download and load your dataset  
+- Perform **Exploratory Data Analysis (EDA)** with visualizations and summary statistics  
+- Handle missing values (remove, impute, etc.)  
+- Encode categorical variables (e.g., one-hot encoding or label encoding)  
+
+### b) Model Training and Hyperparameter Tuning
+- Try multiple models appropriate for your task  
+- Train each model  
+- Tune hyperparameters using **Grid Search**, **Random Search**, etc.  
+
+### c) Rigorous Model Evaluation
+
+#### For Classification
+- **F1 Score**  
+- **AUC**  
+- **Accuracy** (use carefully)  
+- **Confusion Matrix**  
+
+#### For Regression
+- **R-squared (R²)**  
+- **MAE**  
+- **RMSE**  
+
+### d) Artifact Preservation
+- Save your trained model (e.g., using `pickle` or `torch.save`)  
+- You will load this model into your website  
+
+---
+
+# <span style="color: #397DFF; font-weight: 350">Part B: Software Engineering Component</span>
+
+## <span style="color: #397DFF">1. Public-Facing Website</span>
+- Built using **React**  
+- Serves as documentation + interactive demonstration  
+
+## <span style="color: #397DFF">2. Required Website Content (Documentation)</span>
+
+### a) Central Problem & Real-World Impact
+Explain:
+- What question you're answering  
+- Why it matters  
+- Who benefits  
+- What real-world decisions your model could influence  
+
+### b) Data Source & Nature
+Include:
+- Dataset link  
+- What each row represents  
+- Features included  
+- Number of examples  
+- Any limitations or biases  
+
+### c) ML Methodology
+Clarify:
+- Which algorithms you tried  
+- Which you chose  
+- Why you chose it  
+- What hyperparameters you tuned  
+
+### d) Final Performance Metrics
+Report:
+- Your final evaluation metrics  
+- A direct answer to your core question  
+- Limitations + failure modes  
+
+---
+
+# <span style="color: #397DFF; font-weight: 350">3. Interactive Component (MANDATORY)</span>
+
+Your website must include at least **one interactive ML-powered element**.
+
+### Acceptable Options
+- **Prediction Form** (user enters input → model predicts)  
+- **Slider-Based Dynamic Prediction**  
+- **Interactive Visualizations**  
+- **Comparative Predictions (What-if Analysis)**  
+
+---
+
+# <span style="color: #397DFF; font-weight: 350">Part C: Deadlines & Deliverables</span>
+
+## <span style="color: #397DFF">📅 Deadlines</span>
+- **Research Proposal** — *Due: End of Thanksgiving Break*  
+  - One paragraph  
+  - Includes central question, dataset, and approach  
+- **Final Project** — *Due: Before Banquet*  
+  - Full ML pipeline  
+  - Fully functional website  
+  - Complete documentation  
+
+## <span style="color: #397DFF">✅ Deliverables Checklist</span>
+- [ ] **Research Proposal**  
+- [ ] **ML Solution**  
+  - Dataset acquired & cleaned  
+  - Multiple models compared  
+  - Best model selected  
+  - Model evaluated  
+  - Model saved  
+- [ ] **Website Component**  
+  - React website (public-facing)  
+  - Full documentation  
+  - At least one interactive component  
+  - Accessible, clear design  
+
+---
+
+# <span style="color: #397DFF; font-weight: 350">🌟 Exemplary Projects for Inspiration</span>
+- https://llm-attacks.org  
+- https://thinkingmachines.ai/blog/modular-manifolds  
+- Distill-style explorations:  
+  - Feature Visualization  
+  - Activation Atlas  
+  - Handwriting with Neural Networks  
+  - Building Blocks of Interpretability  
+- https://distill.pub  
+
+---
+
+# <span style="color: #397DFF; font-weight: 350">Tips for Success</span>
+- Choose a **focused** question  
+- Select a **manageable dataset**  
+- Document continuously  
+- Build the interactive component early  
+- Make explanations accessible to non-ML audiences  
+
+Good luck! 🚀