Today I made major progress in my Titanic Survival Prediction project 🚀
I completed full feature engineering and finalized the EDA notebook.

🔍 What I accomplished today

1. Finished EDA and cleaned the data
2. Created new features through feature engineering:
   • Extracted Title from the Name
   • Grouped rare titles together (e.g., Lady, Countess → Rare)
   • Calculated FamilySize by adding SibSp and Parch
   • Created IsAlone feature for passengers traveling alone
   • Binned Age into 5 groups (AgeBand)
   • Binned Fare into 4 groups (FareBand)
   • Converted Embarked into one-hot encoded variables
   • Created Has_Cabin feature based on missing cabin information
3. Dropped unnecessary columns (PassengerId, Name, Ticket) to prepare for modeling
4. Updated README.md with a link to the finalized EDA notebook

🧠 Key Takeaway

Feature engineering is not just about adding more variables — it’s about making the information in your data more useful for the model.
Today’s transformations laid a strong foundation for building better predictive models.

🧩 Next Steps

• Start building baseline machine learning models
  • Try Logistic Regression, Random Forest, etc.
• Compare model performances
• Prepare for Kaggle submissions

One step closer to mastering data science workflows 🏁
Tomorrow, it’s time to build the first models!