This file consists of my work on the player performance prediction for my major project as final year student from nirma university. One of the most popular sports in the world, cricket captivates millions of fans with its rich heritage and fierce competitiveness. India has become one of the cricketing nations to be reckoned with, continuously generating elite athletes who have made significant contributions to the game. As a part of this major project, we embarked on the process of creating a comprehensive program that automates the gathering and analysis of statistical data on Indian cricket players. Gaining a deeper comprehension of player performance, team dynamics, and strategic decision-making is now possible because to the exponential expansion of digital information and the growing accessibility of data connected to cricket. To speed up the process of data scraping and analysis, we use a combination of Selenium, Pandas, and Requests libraries, using the power of automation. Beginning with Selenium automation, an efficient web scraping tool, our project enables us to extract relevant hyperlinks to statistical information on Indian cricket players. We effectively gather a substantial dataset that forms the basis of our study by automating the browsing of and interaction with online sites. The scraped data is then processed and organized using the Pandas library, which is recognized for its data analysis and manipulation capabilities. Pandas gives us a flexible framework to clean, transform, and arrange the data, allowing us to get insightful information and ease additional research. We use the Requests library alongside with Pandas to obtain web page's raw HTML content. This makes it possible for us to quickly and precisely access and extract particular data items, such as player statistics, career stats, and match information. Our research aims to automate the time-consuming and laborious collection process. This will allow us to gather and analyze statistical data about Indian cricket players more quickly. We can better comprehend player performance trends, assess team dynamics, and make strategic decisions for both individual players and the Indian cricket team as a whole thanks to the created dataset, which is enhanced with relevant insights. In a nutshell this substantial responsibility makes use of automation and machine learning to drastically change how we collect and analyze statistical information about Indian cricket players. We develop an integrated system using Selenium, Pandas, and Requests that speeds the data scraping procedure and enables us to gain insightful information. With this initiative, we hope to advance the field of cricket analytics through facilitating data-driven decision making in the Indian cricket industry. To Follow the code properply i have divided codes as following section

Data scrapping

Data cleaning

Data visualization and use of streamlit platform for hosting graphs and statistics

LSTM algorithm Applications