Short project to practice classification: Predicting the Man of the Match in an IPL game based on performances in the game.
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Updated
Aug 17, 2018 - R
Short project to practice classification: Predicting the Man of the Match in an IPL game based on performances in the game.
Fantasy sports in this age of technology has been a closely connected part of every particular sport community. This repository tries to predict the player performance index of each player.
Data Set of 2018 and 2019 IPL matches were chosen to predict runs scored by players in 2020. Exploratory Data Analysis was done on data set followed by ensemble model XGBoost to get accuracy of 99.6%
Predict the match winning outcome of Dhoni for CSK. Further conditions: • It is given that Dhoni has to play the last over is not dismissed given that the last over has to be of the second innings.
Explore the exciting world of the IPL through the analysis using Power BI and presented on Streamlit! Our interactive visualizations break down player stats, team strategies, and match highlights in a simple and engaging way. Join our journey to simplify IPL analysis – it's cricket, made fun for everyone!
This project provides analysis of IPL(2008-2018) data using Python
IPL-Dashboard is the data visualization project which is built with a dash-plotly python framework. IPL data up to 2017 are visualized in different types of interactive graphs.
Scrapping details of IPL_2021 using cheerio module and storing all the details offline in excel sheets.
If you want to view the deployed model, click on the following link here ↴↴↴↴↴↴
A web app to visualise IPL data across all seasons with kaggle dataset, https://www.kaggle.com/manasgarg/ipl
Basic Data Analysis on an IPL dataset using Python
This package will simulate the points table based on different possible results in a sports tournament and generate the favorable outcomes needed to make your favorite team to be placed at your expected position in the points table
The dataset covers the Indian Premier League (IPL) with details on matches (date, teams, venue, results), player stats (runs, wickets), team stats (wins, losses), season summaries, and umpire info. The EDA reveals patterns and insights, highlighting dominant teams, star players, and trends across seasons.
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