[Kaggle Study] Titanic - Machine Learning from Disaster

I started Kaggle Study alone.

I will write most of my writing in English to become familiar with English.

The first competition is ‘Titanic - Machine Learning from Disaster’, It’s very famous competition.

The kernel I’m currently studying is ‘EDA To Prediction(DieTanic)’.

Today I learned(TIL) will be updated at my kernel.

TIL

Sun Mar 14

1. Check competition overview and contents of the kernel.
2. Check type of features
   • Categorical Features : Sex, Embarked
   • Ordinal Features : Pclass
   • Continuous Features : Age
3. Analysis a categorical feature(‘Sex’), a ordinal feature(‘Pclass’) and a continuous feature(‘Age’).
4. Make a new categorical feature(‘Initial’) from ‘Name’.
5. Fill continuous data(‘Age’).

Mon Mar 15

1. Analysis categorical feature(‘Embarked’).
2. Fill categorical data(‘Embarked’).

Tue Mar 16 ~ Wed Mar 18

• I Studied another topics.

Fri Mar 19

1. Review that I studied up to now.

Sat Mar 20

1. Analysis two discrete features(‘SibSip’, ‘Parch’).
2. Pracitce Feature Enginnering for categorical values(Sum three categorical values(‘Pclass’, ‘Sex’, ‘Embarked’) -> Make a new feature).
3. Analysis a continuous feature(‘Fare’).
4. Summary Observations.
5. Check correlation between the features(Heatmap)
6. Feature Engineering and Data Cleaning
   • Binning ‘Age’ Features -> ‘Age_band’
   • Summation ‘Parch’ and ‘SibSp’ -> ‘Family_Size’
   • ‘Family_Size’ == 0 -> ‘Alone’

Sun Mar 21

1. Feature Engineering and Data Cleaning
   • ‘Fare’ -> ‘Fare range’ -> ‘Fare_cat’
2. Label Encoding
   • ‘Sex’, ‘Embarked’, ‘Initial’
3. Drop unneeded features
   • ‘Name’, ‘Age’, ‘Ticket’, ‘Fare’, ‘Cabin’, ‘Fare_Range’, ‘PassengerId’
4. Heatmap
5. Predicitve Modeling
   • Logistic Regression
   • Support Vector Machines(Linear and radial)
   • Random Forest
   • K-Nearest Neighbours
   • Naive Bayes
   • Decision Tree
   • Logistic Regression

Mon Mar 22

1. Cross Validation
   • K-Fold
   • Confusion Matrix
2. Hyper-Parameters Tuning
   • GridSearchCV
   • Ensembling
     • Voting Classifier

Tue Mar 23

1. Hyper-Parameters Tuning
   • Bagging
   • Boosting
   • Hyper-Parameter Tuning for AdaBoost
2. Confusion Matrix for the Best Model
   • Feature Importance

Sun Mar 28

• Finish ‘EDA To Prediction(DieTanic)’ kernel.
• Add a few contents by referring to other blogs.
• I will restart with the next kernel.