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Refresh. Predict survival on the Titanic and get familiar with ML basics.
The dataset containing information about passengers aboard the Titanic is one of the most famous datasets used in data science and machine learning. It was created to analyze and understand the factors that influenced survival rates among passengers during the tragic sinking of the RMS Titanic on April 15, 1912.
This repository serves as your gateway to exploring the rich insights hidden within the Titanic dataset using Python and Kaggle. Delve deep into the realm of classification techniques and machine learning algorithms
Firstly, you need to download the dataset in Data Explorer by going into the Data tab (Figure 5.5). You should be downloading both 'train.csv' and 'test.csv'. After downloading the files, you should be navigating to DataLab .
A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.
Kaggle Competition | Titanic Machine Learning from Disaster. The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden...
Solution. In a form of a jupyter notebook, my solution goes through the basic steps of a data science pipeline: Exploratory data analysis with visualizations. Data cleaning. Feature engineering. Modeling. Modelfine-tuning. Note that I have included a script with stacking for information only as it achive lower score.
