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OUR TAGLINE: By leveraging the power of machine learning, this project aims to contribute to a more comprehensive understanding of player selection in the IPL
THE PROBLEM IT SOLVES: The Indian Premier League (IPL) features high competition for spots in the playing eleven (XI). Accurately predicting team selection can benefit fans, fantasy players, and analysts. This project aims to develop a machine learning model to predict which players will be selected for a specific IPL match according to the accuracy of each player’s batting or bowling.
THE PROJECT: The Project comprises of two components : a GUI component and the main Machine Learning Component. The Machine Learning Component uses Linear Regression to predict whether the player is eligible for the team, analysing and projecting data like player gender, nationality, batting style, bowling style and position. The GUI component consists of two subcomponents, the first being a front page which allows us to add, update or delete any aforementioned player data, and the second being a view page showing all the Player Name and Player Status. Both the GUI components are linked with a database file which stores any changes to the data and is recalled when displaying the data.
CHALLENGES WE RAN INTO: While processing user input in a data analysis script using pandas, we encountered a KeyError because a column name referenced in our code didn’t exist in the dataset. We had to handle this runtime error gracefully to ensure our script continues execution without crashing. Apart from this we encountered few basic syntax errors in one of our views due to incorrect indentation. This error prevented our application from starting properly until we fixed the indentation issue.
TECHNOLOGIES USED: Python Programming Language, Linear Regression Algorithm, tkinter library, sqlite4 library, pillow library
TEAM MEMBERS AND THEIR CONTRIBUTION: Mainak Sen and Soumya Snehal - Machine Learning Component Sayak Das and Harsh Gupta - GUI component
HOPE YOU ALL LIKE OUR PROJECT!!