a ppt designed for project on ipl win predictor

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1. ipl win predictor PROJECT COORDINATOR PRESENTED BY ANAGHA DURUGKAR AARYAN JHA RONAK SINGH YASH KALYANI PARTH KADAM

2. Contents: Introduction of project History of IPL Problems Methodology Algorithm Used Future scope Conclusion

3. Introduction: IPL (Indian Premier League) is a highly popular professional Twenty20 cricket league in India. With the increasing popularity of cricket and the IPL, there has been a growing demand for accurate match predictions. Machine learning can be used to develop an IPL match prediction system that can analyze the past performance of teams, players, weather conditions, and other relevant factors to predict the outcome of a match. The first step in building an IPL match prediction system using machine learning is to collect and preprocess the data. The data can include historical match data, team and player statistics, weather conditions, pitch conditions, and other relevant information. After the model is trained, it can be used to predict the outcome of a match by inputting the relevant data for the upcoming match. The model can provide a probability of the outcome, such as the probability of a team winning or losing the match.

4. History of ipl : The idea of the IPL was conceptualized by the Board of Control for Cricket in India (BCCI). The first season took place in 2008, with the Rajasthan Royals emerging as the inaugural champions led by Shane Warne. The IPL adopted a franchise model, with various city-based teams bidding for players in an auction system. Eight franchises participated in the initial season: Chennai Super Kings, Delhi Daredevils (now Delhi Capitals), Kings XI Punjab, Kolkata Knight Riders, Mumbai Indians, Rajasthan Royals, Royal Challengers Bangalore.

5. problem Here are some possible problem statements for an IPL match prediction system using machine learning: Limited accuracy of IPL match predictions: The accuracy of IPL match predictions using machine learning algorithms can be limited by various factors, such as limited availability of high-quality data, variability in performance due to factors like weather and pitch conditions, and complex player interactions. Therefore, there is a need to develop more accurate prediction models that can incorporate more relevant features and factors. Inability to capture dynamic changes in player performance: Player performance in IPL matches can vary greatly based on factors like injuries, fatigue, and team dynamics. Machine learning models used for IPL match predictions need to be able to capture these dynamic changes in player performance to provide accurate predictions.

6. methodology In our project proposed work is predict the winning possibility of a IPL team. We take a historical data of IPL from 2008 to 2019 and trained our model. Model is deployed on the website and takes some input from the user and predict the possibility of winning. The user will Enter : The Teams The runs scored How many runs will be required to win the match (target) The number of wickets that have fallen How many overs are completed On what venue is the match being played The website will show the winning result in terms of winning probability in percentage , along with a pie chart .

7. Algorithm : We’ve used linear regression algorithm . Another algorithm of same type is random forest algorithm to predict the outcome of IPL matches. The results showed that the random forest algorithm achieved an accuracy of 98.21%, outperforming the linear regression algorithm. Using Linear regression to predict the outcome of IPL matches, the results showed that the model achieved an accuracy of 80.33%, indicating that the approach could be effective for IPL match prediction instead of random forest.

8. Future scope The future scope of IPL match prediction systems using machine learning is vast and promising. Here are some potential areas of development and research: Integration of real-time data: Real-time data can provide valuable insights into the performance of teams and players during a match. Therefore, there is a need to develop methods for integrating real-time data into IPL match prediction systems to improve the accuracy of predictions. Use of advanced machine learning techniques: Advanced machine learning techniques, such as deep learning and reinforcement learning, can be used to improve the accuracy and reliability of IPL match prediction systems.

9. future scope Incorporation of contextual information: The performance of teams and players can be influenced by contextual factors, such as the location of the match, fan support, and team morale. Therefore, there is a need to incorporate such contextual information into IPL match prediction systems to provide more accurate predictions. Development of explainable AI: Explainable AI can help analysts and fans better understand the reasoning behind IPL match predictions. Therefore, there is a need to develop more interpretable models and methods for IPL match prediction.

10. BASIC BLOCK DIAGRAM:

11. conclusion In conclusion, IPL match prediction systems using machine learning have the potential to provide valuable insights into the outcome of matches. These systems can analyze historical match data, player and team statistics, weather conditions, and other relevant factors to predict the outcome of an upcoming match. However, there are still several research gaps and challenges that need to be addressed to improve the accuracy and reliability of these systems. These include issues related to data quality, feature selection, model interpretability, and handling dynamic changes in player performance. Despite these challenges, continued research and development in IPL match prediction systems using machine learning can help analysts and fans make more informed predictions about the outcome of IPL matches.

12. THANK YOU