Link to check out: https://colab.research.google.com/drive/1Hyf7NbezFe_baJxUh7LruqIaxh0k8uHK
Like any other ML project, in this one, I took a closer look at the training data and did some preprocessing to it. Based on a few independent variables like sex, social class, age, number of siblings, the presence of parents, the price of the fare, the numbers of cabin, the letter of each cabin, title of passenger(Dr, Mr, Ms), I implemented different classification classifers without any parameter tuning to find the most suitable model. Afterward, I used Grid Search to find best parameters to my model.
Data: https://www.kaggle.com/competitions/titanic/data?select=train.csv
Most Common Models for Classification 1
Most Common Models for Classification 2
This project requires to imported and installed libraries: pandas, numpy, seaborn, itertools, matplotlib, xgboost and scikit-learn.