https://github.com/florianwoelki/titanic_classification
This is a binary classification problem for the titanic dataset.
https://github.com/florianwoelki/titanic_classification
deep-learning jupyter-notebook kaggle keras machine-learning python tensorflow titanic titanic-dataset
Last synced: 7 months ago
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This is a binary classification problem for the titanic dataset.
- Host: GitHub
- URL: https://github.com/florianwoelki/titanic_classification
- Owner: FlorianWoelki
- Created: 2019-05-22T19:06:48.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-10-14T11:07:19.000Z (about 6 years ago)
- Last Synced: 2025-01-27T23:16:46.938Z (9 months ago)
- Topics: deep-learning, jupyter-notebook, kaggle, keras, machine-learning, python, tensorflow, titanic, titanic-dataset
- Language: Jupyter Notebook
- Homepage:
- Size: 189 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Titanic Classification
This simple classification project was meant to learn and train to handle and visualize data.
## 🤔 What is this project about?
With the titanic classification problem you learn, how to normalize data, visualize it and how to apply a neural network or other machine learning model on the dataset.
You can find the Dataset [here](https://www.kaggle.com/c/titanic).
This dataset is pretty simple and do not need to normalize it so heavily.
In this repository I visualized the data and created a DNN for predicting, how much passengers will survive oder die based on the given data (e.g. gender, passenger class).
## 📈 Getting Started
You can run this notebook by downloading it and running it by yourself.
Furthermore you can simply look at the notebook in the jupyter notebook file.
## 🛠 Built with
- [numpy](https://numpy.org/)
- [matplotlib](https://matplotlib.org/)
- [pandas](https://pandas.pydata.org/)
- [tensorflow](https://www.tensorflow.org/)
- [Kaggle Dataset](https://www.kaggle.com/c/titanic)
## 🤝 Contributing
If you have a issue or you want to build it better, feel free to contribute to this project by making a simple
pull request. :)
## 🤠 Authors
- [Florian Woelki](https://florianwoelki.com)