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Parameters of Cricket Analysis #723
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Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊 |
Can you share the dataset? Please be specific with the sports you are analyzing for this problem statement. |
I have used these two datasets for the above mentioned project. Sports : Cricket |
What are the aspects of Cricket you are planning to analyze here? @SOMNATH0904 |
In this project, I plan to analyze various aspects, including player performance metrics such as batting averages, strike rates, and bowling economy rates. Additionally, I will examine team performance by analyzing win/loss ratios, match outcomes, and scoring patterns. Fielding statistics, player consistency, and performance trends over time will also be explored to provide a comprehensive overview of cricket dynamics. |
Cool. Go ahead @SOMNATH0904 |
Hello @SOMNATH0904! Your issue #723 has been closed. Thank you for your contribution! |
ML-Crate Repository (Proposing new issue)
🔴 Project Title : Exploratory Data Analysis (Sports)
🔴 Aim : It is a comprehensive guide for conducting EDA on sports-related datasets. It aims to equip users with the skills necessary to uncover insights and patterns from raw data, leveraging various statistical and graphical techniques. This project covers a range of essential EDA steps, including data cleaning, data visualization, and summary statistics. Users will learn how to handle missing values, identify outliers, and understand the distribution and relationships within the data. The project provides practical examples and code snippets to facilitate hands-on learning, making it an ideal resource for anyone looking to enhance their data analysis capabilities in the sports domain. By the end of the project, users will be able to perform robust EDA, gaining valuable insights that can inform decision-making and strategy in sports analytics.
🔴 Dataset : Will be given in the Pull Request file.
🔴 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.
🔴 Reference Project Folder: NA
📍 Follow the Guidelines to Contribute in the Project :
requirements.txt
- This file will contain the required packages/libraries to run the project in other machines.Model
folder, theREADME.md
file must be filled up properly, with proper visualizations and conclusions.🔴🟡 Points to Note :
✅ To be Mentioned while taking the issue :
Happy Contributing 🚀
All the best. Enjoy your open source journey ahead. 😎
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