This project deepened my machine learning knowledge significantly and I strengthened my ability to apply concepts that I learned from textbooks, blogs and various other sources, on a different type of problem. This project had a heavy focus on the data preparation part, since this is what data scientists work on most of their time.
I started with the data exploration where I got a feeling for the dataset, checked about missing data and learned which features are important. During this process I used seaborn and matplotlib to do the visualizations. During the data preprocessing part, I computed missing values, converted features into numeric ones, grouped values into categories and created a few new features. Afterwards I started training 8 different machine learning models, picked one of them (random forest) and applied cross validation on it. Then I explained how random forest works, took a look at the importance it assigns to the different features and tuned it's performace through optimizing it's hyperparameter values. Lastly I took a look at it's confusion matrix and computed the models precision, recall and f-score, before submitting my predictions on the test-set to the Kaggle leaderboard.
To see the project, just open the Jupyter Notebook: "binary_classification_titanic.ipynb".