https://github.com/soumyaco/spaceship-titanic

Famous Kaggle competition solution notebook with step by step guide.
https://github.com/soumyaco/spaceship-titanic

data-analysis-python data-science kaggle-competition machine-learning python3 spaceship-titanic

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Famous Kaggle competition solution notebook with step by step guide.

• Host: GitHub
• URL: https://github.com/soumyaco/spaceship-titanic
• Owner: SoumyaCO
• License: mit
• Created: 2023-08-18T17:02:22.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-08-20T16:07:09.000Z (over 1 year ago)
• Last Synced: 2024-10-25T11:50:01.537Z (3 months ago)
• Topics: data-analysis-python, data-science, kaggle-competition, machine-learning, python3, spaceship-titanic
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/code/soumyadipbhat/spaceship-titanic-solved-notebook-detailed
• Size: 2.45 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Spaceship-Titanic Kaggle Competition Notebook

## Famous Kaggle Competition Solution Notebook with step by step guide

This repository contains the solved notebook of the competition.

This will help beginners to understand the workflow of a machine learning problem.

Machine Learning is not just about learning the algorithms, It consists of some crucial steps.

> * Data Preparation

> * Data Scaling

> * Dimensionality Reduction

> * Model choosing

> * Training and Testing Model

> * Hyperparamter tuning [*Grid Search*, *Random Search*]

> * Evaluation [`confusion_matix`, `F1_score`, `precision`]

> * Deployment [out of the scope of this repository]

### Problem Description

You can Read the whole problem description in kaggle : [Kaggle|Spaceship Titanic Competition](https://www.kaggle.com/competitions/spaceship-titanic/overview)

There are features [passenger details], we have to predict whether a passenger has transported or not.

Basically this is a binary classification problem which needs advance feature engineering skills.

**🥊 Challenges:**

* Have Categorical data which affect the performance if we just `LabelEncode()`it.

* Variance of the features are very uneven. Scaling the data is very necessary.

* Irrelivant columns of data like `PassengerId` have to be removed.

* Other Challenges: Intermediate to advance feature engineering skills needed

### Edit and Experiment with the Notebook

👉 Click on the notebook `spaceship-titanic.ipynb` and click on the `open in kaggle` button.

👉 Or if you want to open it in google colab, Click on the `open in colab` button on top of the notebook or on top of this README file.

### ❗️Warning

When opening in colab, dataset have to be downloaded and uploaded on google colab manually.

### 🏅 Welcome Contributors

* Found a mistake?

* Improved Accuracy of the model?

* Any suggestion related to the notebook or the workflow

* Or any other types of contribution will be appriciated.

👉 In the notebook I've provided detailed codes and concepts. If you like it please give a star ⭐️

🧑🏻‍💻 My Profiles:

> * [🔗 LinkedIn](https://www.linkedin.com/in/soumyadip-bhattacharjya-993974234/)

> * [🔗 Kaggle](https://www.kaggle.com/soumyadipbhat)