Bundesliga Season Analysis

[abhisheks008]

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Bundesliga Season Analysis
🔴 Aim : Create a data analysis model based on the statistics given through the dataset.
🔴 Dataset : https://www.kaggle.com/datasets/memocan/bundesliga-2021-2024-complete-season-analysis
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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🔴🟡 Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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✅ To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Participant ID (If not, then put NA) :
• Approach for this Project :
• What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[Anwar11234]

I'm interested in contributing to this issue.

• Full name : Ahmed Mohammed Anwar
• GitHub Profile Link : https://github.com/Anwar11234
• Participant ID (If not, then put NA) : NA
• Approach for this Project : First I will understand the data, then perform data cleaning if necessary, then perform exploratory data analysis to gain further understanding of the data, then perform feature engineering and then I will start the modeling process and try different models and evaluate them using suitable evaluation metrics and choose the model with the best accuracy
• What is your participant role? KWoC

[abhisheks008]

Issue assigned to you @Anwar11234. You can start working on it.

[abhisheks008]

Hi @Anwar11234 any update on this issue?