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