Pokémon Data Analysis and Legendary classification.

[thejatingupta7]

Pull Request for PyVerse 💡

Issue Title : 📃: Pokémon Data Analysis and Legendary Prediction #304

• Info about the related issue (Aim of the project) : This project aims to analyze Pokémon data to predict whether a Pokémon is legendary or not using advanced machine learning models. The dataset includes various attributes such as base stats, type, and generation, which will be used to train and evaluate different models.
• Name: Jatin Gupta
• GitHub ID: thejatingupta7
• Email ID: [email protected]
• Idenitfy yourself: (Mention in which program you are contributing in. Eg. For a WoB 2024 participant it's, WoB Participant)
  GSSOC
  Hacktoberfest

Closes: #issue number that will be closed through this PR #304

Describe the add-ons or changes you've made 📃

This project aims to analyze Pokémon data to predict whether a Pokémon is legendary or not using advanced machine learning models. The dataset includes various attributes such as base stats, type, and generation, which will be used to train and evaluate different models.

Type of change ☑️

What sort of change have you made:

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Code style update (formatting, local variables)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

How Has This Been Tested? ⚙️

Various data analysis tools, various preprocessing techniques (such as Imputation by K Nearest Neighbours, Normalization, etc), and various machine learning models, from logistic regression to Neural Nets (MLP),

Checklist: ☑️

• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented on my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added things that prove my fix is effective or that my feature works.
• Any dependent changes have been merged and published in downstream modules.

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