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Predict survival on the Titanic and get familiar with ML basics.
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
The goal is to predict who onboard the Titanic survived the accident. In our initial analysis, we wanted to see how much the predictions would change when the input data was scaled properly as opposed to unscaled (violating the assumptions of the underlying SVM model).
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 .
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.
Titanic project overview. The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class.
A public repo of datasets. Contribute to datasciencedojo/datasets development by creating an account on GitHub.
May 30, 2024 · The Titanic dataset is available on Kaggle, and you can download it from Titanic — Machine Learning from Disaster. Save the train.csv and test.csv files in your working directory.
Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources.
Mar 31, 2022 · Discover the fascinating world of Titanic dataset analysis using Python and Kaggle. This in-depth blog tutorial explores classification techniques and machine learning algorithms. Dive into data preprocessing, feature engineering, and model evaluation.
