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A notebook that uses Titanic dataset on kaggle to build a predictive model that answers the question: “what sorts of people were more likely to survive?, this notebook includes Data Exploration with visualizations, Data Wrangling and Model building. Competition Link

Models 🧮

Model Parameters Acc
Logistic Regression Defaults with max_iters = 200 0.74
GradientBoostingClassifier Defaults 0.78
SVC Defaults 0.69

Score 🏆

Rank 🏅 TOP 22%
Acc 🎯 0.78

Machine Learning Workflow 3️⃣🔄

  1. Data Exploration 🔎

    • Univariate
    • Bivariate

  2. Data Pre-processing 🛠

    1. Data Cleaning 🚿
      • Fill NaNs in Age with Mean
      • Fill NaNs in Cabin with most frequent value for each group (grouped by Pclass)
      • Fill NaNs in Embarked with most frequent value 'Southampton'
      • Fill NaNs in Fare with Mean
      • Drop Ticket and PassengerId
    2. Feature Engineering ⚙️
      • map Cabin values to first letter in its value: to be [A,B,C,D,E,F,G,T]
      • create Family Size, = Parch + SibSp +1
      • create FSize as a categorical value [single, small, Mid, Large]
      • create weightedFare = pow(Fare,i) where i is in [4,3,2]
      • map Name values to ['Mr', 'Mrs', 'Miss', 'Master']

  3. Model Building ⚖️

    • split data to train, test with ratios 80%, 20% respectively
    • build the models stated above
    • fit the model on train
    • evaluate the model on test before submission

To DO 📌:

  • Improve the accuracy and rank