The most famous competition over the kaggle . The task of the competition is to predict if a passenger will survive (1) or not (0). This is my first ever Kaggle competition. In this Repository my intention was to perform Exploratory Data Analyses (EDA), explore various classification algorithms (KNN, NaiveBayes, LogisticRegression, SVM, Bagging methods such as RandomForest, Boosting such as AdaBoost, XGBoost), evaluate their accuracy.
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.
One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.
The notebooks in the repo are divided into the following categories:
- EDA - Performed Exploratory Data Analyses using pandas, matplotlib and seaborn
- Featurization - Explored various feature engineering techniques
- Imputation- Implementated Feature imputation techniques such as Recursive Feature Elimination etc
- Pipeline - Prepared several ML pipelines in sklearn for the preprocessing, fitting, predicting, hyperparameter tuning of the whole ML process.
- MLmodel - explore various classification algorithms (KNN, NaiveBayes, LogisticRegression, SVM, Bagging methods such as RandomForest, Boosting such as AdaBoost, XGBoost), evaluate their accuracy.
- HyperParameterTuning - Performed Randomized and Grid Search cross validation for finding the best hyperparameters to improve the accuracy of the model
Competition Website: kaggle