🚀 AI Agent Hackathon Platform 🎯 Objective

Welcome to the AI Agent Hackathon! -In this challenge, you will: -Explore a real-world dataset -Build features based on your understanding -Train a machine learning model -Create your own AI Agent (Streamlit app) -Submit predictions -Get evaluated automatically using metrics + AI judge

🧠 Architecture Overview Dataset → Exploration → Feature Engineering → Model Training → Predictions ↓ AI Agent (Streamlit) ↓ Evaluation System ↓ Leaderboard 🏗️ Project Structure

ai-agent-hackathon/ │ ├── data/ # Dataset (already provided) ├── notebooks/ # Step-by-step notebooks ├── models/ # Saved model (model.pkl) ├── outputs/ # Your submission goes here ├── evaluations/ # Evaluation system ├── app/ # Streamlit UI (Agent + Leaderboard) ├── README.md

🧭 PHASE-WISE GUIDE 🔹 PHASE 1 — Data Exploration

📘 Notebook: 01_data_exploration.ipynb

What you will do:

Understand dataset structure Identify patterns Analyze relationships Choose your target variable

🎯 Target Options:

target_churn → Classification target_fraud → Classification target_revenue → Regression ** 🔹 PHASE 2 — Feature Engineering**

📘 Notebook: 02_feature_engineering.ipynb

What you will do:

Clean data Encode categorical variables Create new features 💡 Examples: activity_score engagement_ratio spend_per_transaction

👉 This is the most important phase 👉 Better features = better model

🔹 PHASE 3 — Model Training

📘 Notebook: 03_model_training.ipynb

What you will do:

Train ML models Compare performance Save best model

🤖 Models you can use:

Type Models Basic Logistic Regression Tree-based Random Forest, Gradient Boosting Advanced XGBoost Regression Linear Regression

🔹 PHASE 4 — Generate Predictions

📘 Notebook: 04_generate_predictions.ipynb

What you will do: Load model Predict on test data Save output

📤 FINAL OUTPUT FORMAT actual,prediction 📁 Save your file as: outputs/YOURNAME_predictions.csv

Example:

outputs/harshit_predictions.csv 🔹 PHASE 5 — Build AI Agent

📘 File: app/app.py

Run your agent: streamlit run app.py What your agent does:

Takes user input Uses your trained model Shows predictions Displays confidence

🔹 PHASE 6 — Evaluation

📘 File: app/leaderboard.py

Run leaderboard: streamlit run leaderboard.py Click:

👉 Run Evaluation

🏆 Scoring System For Classification:

Accuracy → 50% F1 Score → 30% AI Judge → 20% For Regression: MSE (lower better) → 60% R² Score → 20% AI Judge → 20%

🤖 AI JUDGE (SPECIAL FEATURE)

Your model is also evaluated by AI based on:

Prediction quality Generalization Business usability

🏆 LEADERBOARD

Automatically updated Supports tie-breaking Fair ranking system

⚠️ RULES

✅ Use any model ✅ Create your own features ✅ Customize your AI agent

❌ Not Allowed:

Changing output format Incorrect file naming Multiple submissions after deadline

🧠 HOW TO USE CHATGPT (VERY IMPORTANT)

You are encouraged to use ChatGPT to improve your solution.

🔥 Example Prompts Feature Engineering: Suggest 5 advanced features for churn prediction dataset Model Selection: Which model is best for classification with tabular data? Hyperparameter Tuning: How to improve RandomForest performance? Debugging: Why is my model overfitting? Evaluation: How to improve F1 score? 💡 Pro Tips

Focus on feature engineering first

Try multiple models Check feature importance Keep model simple but effective

**🎯 FINAL CHECKLIST Before submission:

✔ Model trained ✔ Predictions generated ✔ File saved in outputs/ ✔ File name correct ✔ Columns correct

🚀 FINAL GOAL

Build an AI Agent that is:

✅ Accurate ✅ Intelligent ✅ User-friendly ✅ Business-ready

**🔥 GOOD LUCK

“Your model is your brain, but your agent is your product.”

🚀 Go build something amazing!**