Today was all about taking the Titanic Survival Prediction project to the next level through model experimentation and tuning ⚙️🚀

🔍 What I accomplished today

Built and evaluated multiple machine learning models:
- VotingClassifier (soft voting) with RandomForest, GradientBoosting, XGBoost
- StackingClassifier using LogisticRegression as meta model
- CatBoostClassifier single model
Performed hyperparameter tuning with:
- GridSearchCV for individual models
- Optuna for RandomForestClassifier
Compared all submissions on both local validation and Kaggle leaderboard
Translated modeling notebook to English for GitHub publication
Generated and updated submission logs for all experiments

🧠 Key Takeaway

Iterating across different models and tuning strategies is critical.
Sometimes the best local model doesn’t win on the leaderboard — always validate broadly and log everything!

📈 Submission Summary

Version	Model	Val Acc	Kaggle	Notes
v1	Soft Voting (RF + GB + XGB)	0.8090	0.7775	Baseline Ensemble
v2	Stacking (→ LogisticRegression)	0.8034	0.7751	Meta Model
v3	CatBoostClassifier (single)	0.7753	0.7631
v4	GridSearchCV + VotingClassifier	0.7921	0.7799	🥇 Best Kaggle Score
v5	Optuna-tuned RandomForestClassifier	0.8146	0.7751	Best Local Accuracy

🧩 Next Steps

Try Optuna tuning for other models (e.g., XGBoost, GB)
Experiment with feature selection and SHAP analysis
Build final model pipeline and prep for full documentation

Data science isn’t about one perfect model — it’s about learning through every iteration 🧠
On to the next breakthrough tomorrow 💪