https://github.com/saroshfarhan/kaggle-playground-s4e12
Kaggle competition first attempt
https://github.com/saroshfarhan/kaggle-playground-s4e12
analytics data data-analysis-python data-science
Last synced: 4 months ago
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Kaggle competition first attempt
- Host: GitHub
- URL: https://github.com/saroshfarhan/kaggle-playground-s4e12
- Owner: saroshfarhan
- Created: 2024-12-21T02:59:54.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-01-28T16:50:12.000Z (5 months ago)
- Last Synced: 2025-01-28T17:36:51.989Z (5 months ago)
- Topics: analytics, data, data-analysis-python, data-science
- Language: Jupyter Notebook
- Homepage:
- Size: 10.7 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Regression with an Insurance Dataset
## Playground Series - Season 4, Episode 12
I participated in this competition just to start my kaggle journey after the first trimester in UCD, where I got to learn EDA and checking of statistic of regression and optimizing the parameters to get good results, I thoug practising on kaggle will help me build my potfolio as well as get me learn from the community of data analysts.
In this playground series I am to do regression on Insurance dataset provided on [kaggle](https://www.kaggle.com/competitions/playground-series-s4e12/data)
## Dataset Description
The dataset for this competition (both train and test) was generated from a deep learning model trained on the Insurance Premium Prediction dataset. Feature distributions are close to, but not exactly the same, as the original. Feel free to use the original dataset as part of this competition, both to explore differences as well as to see whether incorporating the original in training improves model performance.
## Files
*train.csv* - the training dataset; Premium Amount is the continuous target
*test.csv* - the test dataset; your objective is to predict target Premium Amount for each row
*sample_submission.csv* - a sample submission file in the correct format
## Result
Ranked 984 on my first Kaggle competition, good learning curve, onto next one!
