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https://github.com/zizekuros/kaggle-titanic
My prediction solution for the Kaggle Titanic competition. I used Snowflake ML Classification model.
https://github.com/zizekuros/kaggle-titanic
classification cortex kaggle kaggle-competition kaggle-titanic machine-learning ml prediction snowflake
Last synced: about 2 months ago
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My prediction solution for the Kaggle Titanic competition. I used Snowflake ML Classification model.
- Host: GitHub
- URL: https://github.com/zizekuros/kaggle-titanic
- Owner: zizekuros
- License: mit
- Created: 2024-06-25T13:10:30.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-08-03T15:03:11.000Z (5 months ago)
- Last Synced: 2024-08-03T16:24:02.889Z (5 months ago)
- Topics: classification, cortex, kaggle, kaggle-competition, kaggle-titanic, machine-learning, ml, prediction, snowflake
- Homepage: https://www.kaggle.com/competitions/titanic
- Size: 229 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Titanic: Machine Learning from Disaster
This repository contains my prediction solution for the Kaggle Titanic competition. I used the Snowflake ML Classification model to tackle this challenge, both to learn and to evaluate its efficiency.
## Table of Contents
- [Project Overview](#project-overview)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Download the Data](#download-the-data)
- [Running the Prediction](#running-the-prediction)
- [Submit Prediction](#submit-prediction)
- [Score](#score)
- [Acknowledgements](#acknowledgements)
## Project Overview
The Titanic competition on Kaggle is a classic machine learning challenge. The goal is to build a predictive model that answers the question: "what sorts of people were more likely to survive?" using passenger data (i.e., name, age, gender, socio-economic class, etc.).
I described the entire solution step-by-step in [this article](https://medium.com/@zizek.uros/how-i-solved-the-kaggles-titanic-ml-challenge-using-snowflake-classification-model-5e249563a79e).
## Getting Started
### Prerequisites
To run the code in this repository, you need to ensure the following prerequisites:
- Create a Kaggle account.
- Install the Kaggle API and configure Kaggle API credentials. Instructions are available [here](https://github.com/Kaggle/kaggle-api/blob/main/docs/).
- Ensure you have an active account on the Snowflake platform. If you don't have one yet, sign up for a Snowflake account on their website.
### Download the Data
Once you have the Kaggle API and credentials configured, you can download the competition data by running:
```
kaggle competitions download -c titanic
```
Unzip the data into the data folder, remove the original zip archive, and you are ready to go:
```
unzip titanic.zip -d data/
rm titanic.zip
```
## Running the Prediction
To run the prediction, follow the steps:
1. Create a new Snowflake Database (in my case it's called KAGGLE).
2. Load the train and test data sets into the tables (in my case they are named `titanic_train` and `titanic_test`).
- You can do this through the Snowflake Web UI by uploading files through the web browser or by loading them from an S3 bucket. You can use the [these SQL queries](load-data-example.sql) for loading the data that were generated when I was loading the data. Make sure to replace the filename.
- The data schemes of the tables with train and test data should [look like this](schemas.sql).
3. Run one of the sequences of the queries:
- [Basic Classification Prediction](snowflake-ml-classification-basic.sql)
- [Classification Prediction based on some custom features and cleaned data](snowflake-ml-classification-advanced.sql)
4. Save results to a .csv file that only contains two cloumns:
- PassengerId
- Survived
## Submit Prediction
You can submit your score through the Kaggle Web UI or with the Kaggle CLI tool. The following command can be used to do it:
```
kaggle competitions submit -c titanic -f {file} -m "{message}"
```
## Score
The advanced prediction with no some features engineering scored **0.78229**, meaning it successfully predicted for ~78.23% of people whether survived or not.
I finished in 2810th place out of 17,290 at the time of submitting the results, which is in the **84th percentile**.
Maybe I'll keep working on it and see if I can improve the score with some feature engineering, or alternative approach.
## Acknowledgements
Thanks ❤️ to Kaggle for providing the Titanic dataset and competition. It is a super fun and educational challenge.