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IPL Match Prediction System Using Machine Learning.pptx
- 1. “ ” International Conference on InnovativeResearches in Engineering & Technology (IRET-2023) Buddha Institute of Technology , GIDA, Gorakhpur Date of Conference: 6th – 7th April, 2023 IPL Match Winning Prediction System BIT 1 Presented By- Annu Yadav Aman Raj Yadav Abhay mani Tripathi Shivangi Gupta Guided By- Mr Abhinandan Tripathi Department of Information Technology Buddha Institute of Technology , GIDA, Gorakhpur
- 2. Contents Introduction LiteratureSurvey Research Gap Problem Statement Proposed Methodology Experimental Results / Analysis Conclusions Future Scope References BIT 2
- 3. Introduction IPL (IndianPremier 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. BIT 4
- 4. Literature Survey SR NoPaper and publication Author Features 1 Prediction of IPL Match Outcome Using Machine Learning Techniques Srikantaih K C Aryan Srivastav Baibhav Kumar Divy Tolani How to the model is predict the possibility of the winning and discus the main factor of the model 2 Campusx, Krish Naik Mr. Rajeev It discus about the algorithm of the model and train a model 3 Ball-by-ball Indian Premier League (IPL) cricket dataset Navaneesh kumar It is a collector of data set of the from 2009 – 2021 and arrange into systematic order and upload to the kaggle BIT 5
- 5. Research Gap Despitethe growing popularity of IPL match prediction systems using machine learning, there are still several research gaps that need to be addressed. Some of these gaps include: Limited availability of high-quality data: The accuracy of IPL match prediction systems depends on the quality of the data used to train the models. However, high-quality data is often limited in availability, particularly in the case of player-specific data. Lack of research on ensemble learning: Ensemble learning is a powerful technique that combines the predictions of multiple models to improve accuracy. However, there is limited research on the effectiveness of ensemble learning for IPL match prediction systems. Limited research on feature selection: Feature selection is the process of selecting the most important features from the data for model training. However, there is limited research on which features are most important for IPL match prediction, and how to select them. Limited research on model interpretability: While machine learning models can provide accurate predictions, they can be difficult to interpret. There is limited research on how to make IPL match prediction models more interpretable, which can help users better understand the reasoning behind the predictions BIT 6
- 6. Problem Statement Here aresome 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. Lack of interpretability of prediction models: Machine learning models are often criticized for their lack of interpretability, which makes it difficult for analysts to understand the reasoning behind predictions. Therefore, there is a need to develop more interpretable models that can provide insights into the underlying factors that influence IPL match outcomes. BIT 6
- 7. Proposed Methodology Inour project proposed work is predict the winning possibility of a IPL team. We take a historical data of IPL from 2008 to 2021 and train our model. Model deploy on the website and take some input from the user and predict the possibility of winning. The user will choose the team then choose how many runs the team has made, then how many overs are left and how many wickets are left. The website will show the winning result in percentage BIT 7
- 8. Experimental Results /Analysis Several studies have reported promising results for IPL match prediction systems using machine learning. For example, a study by Agrawal and Srivastava (2019) used various machine learning algorithms, including decision trees, random forests, and neural networks, to predict the outcomes of IPL matches. The results showed that the neural network algorithm outperformed other algorithms, achieving an accuracy of 64.28%. Another study by Singh and Pandey (2020) used logistic regression and random forest algorithms to predict the outcome of IPL matches. The results showed that the random forest algorithm achieved an accuracy of 70.54%, outperforming the logistic regression algorithm. In a recent study by Patel and Patel (2021), the authors used a hybrid machine learning approach, combining decision trees and logistic regression, to predict the outcome of IPL matches. The results showed that the model achieved an accuracy of 69.33%, indicating that the approach could be effective for IPL match prediction. BIT 8
- 9. Conclusions In conclusion, IPLmatch 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. BIT 8
- 10. Future Scope The futurescope 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. 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. BIT 9
- 11. References Here are somereferences related to IPL match prediction systems using machine learning: Patel, P., & Patel, D. (2021). Predicting the Results of IPL Matches Using Machine Learning Techniques. International Journal of Advanced Research in Computer Science, 12(2), 47-54. http://ijarcs.info/index.php/Ijarcs/article/view/12387 Agrawal, M., & Srivastava, A. (2019). Analysis and prediction of Indian Premier League using machine learning. International Journal of Engineering and Advanced Technology, 8(5), 1415-1420. https://www.ijeat.org/wp- content/uploads/papers/v8i5s5/E11000585S519.pdf Singh, P., & Pandey, S. (2020). Analysis of Indian Premier League (IPL) Data Using Machine Learning. In Advances in Data and Information Sciences (pp. 441-449). Springer, Singapore. https://link.springer.com/chapter/10.1007/978- 981-15-7655-8_39 Sharma, A., & Raman, B. (2021). A Comparative Study of Machine Learning Algorithms for Indian Premier League Match Result Prediction. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(3), 1363-1373. https://www.ijsrcseit.com/paper/CSEIT2173103.pdf Singh, A. K., Kaur, G., & Kumar, A. (2019). Predictive analysis of Indian Premier League (IPL) cricket data using machine learning algorithms. International Journal of Advanced Science and Technology, 28(17), 311-316. BIT 12
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