Exploratory Data Analysis of Indian Premier League (Cricket) using R
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Updated
Dec 19, 2018 - Jupyter Notebook
Exploratory Data Analysis of Indian Premier League (Cricket) using R
This is a repo maintained for the web app that web-scrapes IPL cricket player rankings through all seasons and also provides some interactive EDA functionalities.
Sports Analytics on Kaggle's IPL Dataset
Data viz created for wionews.com on tosses in Test cricket
CricketScorePrediction is Machine Learning Implementation on a huge Cricket dataset. It contains 2 models. One for predicting the final score of the first Inning keeping the current situation of the match. Second for predicting whether the chasing team will win or not keeping the current situation of the match.
IPL Score Prediction using Machine Learning & Flask
Publishing all current matches and scores for the ICC World Test Cricket Championships 2019-21. Matches include the Ashes and all of the other test matches. SQL commands include runs, boundaries, catches, wickets, overs, strike rates and more exciting complex commands
An LSTM model is used to predict optimal England and Australian Squads for the next ashes series based on the recent performances of the players. Jupyter notebooks and scraped datasets are attached with this repository.
Using LangChain to answer cricket-specific natural language queries!
Data Analysis of Decision Review System (DRS)
Player detection for every cricketer playing in ICC Cricket World Cup 2019
About IPL stands for Indian Premier League and this is an exploratory data analysis of sport Cricket based on various factors. As a Sport Analyst, the objective is to find the most successful teams, players and the factors contributing to win or to loose the game.
WebScraping and data prepocessing of Cricket Stats
This repo contain script to get Pakistan cricket team batting and bowling data, (of each player) from cricinfo statsguru.
This repo contains data from the "cricketr" package. The dataset is being cleaned up and further visualization will be done.
Data Analysis and Modelling on IPL Data 2008-2017
Details analysis of IPL Data (2008-2021)
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