https://github.com/bepb/kaggle_titanic
https://github.com/bepb/kaggle_titanic
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/bepb/kaggle_titanic
- Owner: BEPb
- License: mit
- Created: 2023-01-04T18:38:58.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2023-05-17T06:10:47.000Z (about 3 years ago)
- Last Synced: 2025-03-24T13:04:29.517Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 4.41 MB
- Stars: 36
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# kaggle titanic
## How it works?
It's very simple: here are the solutions for the [titanic competition ](https://www.kaggle.com/competitions/titanic)
## Order of preparation and work
1. Clone the repository or download the archive from github or using the following commands on the command line
```command line
$cmd
$ git clone https://github.com/BEPb/kaggle_titanic
$ cd kaggle_titanic
```
2. Create a Python virtual environment.
3. Install all necessary packages for our code to work using the following command:
```
pip install -r requirements.txt
```
4. file list
- data - directory with data files
- data/titanic.zip - archive of the initial tabular data of the competition (3 files)
- data/gender_submission.csv - one of the original data files
- data/test.csv - one of the original data files
- data/train.csv - one of the original data files
- notebooks - directory with jupiter notebooks
- notebooks/eda_and_analysis - directory with notebooks food and data analysis
- notebooks/eda_and_analysis/titanic_universal_eda.ipynb - universal food notebook
- notebooks/eda_and_analysis/titanic_eda.ipynb - food notebook
- notebooks/solutions - directory with notebook solutions
- python_code - directory with python code solutions
5. Well, as a result of the training, I wrote a console application that, based on the model, predicts whether the
passenger whose data you enter in the fields will survive or not.
- python_code/Titanic_gui.py
prediction for my data, let's say I'm traveling first class with my family:
prediction for data from a set:
