Machine Learning project

1. “
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International Conference on
Innovative Researches in Engineering & Technology (IRET-2023)
Buddha Institute of Technology , GIDA, Gorakhpur
Date of Conference: 6th – 7th April, 2023
IPL Match Winning Prediction System
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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
 Literature Survey
 Research Gap
 Problem Statement
 Proposed Methodology
 Experimental Results / Analysis
 Conclusions
 Future Scope
 References
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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.
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4. Literature Survey
SR No Paper 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
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5. Research Gap
 Despite the 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
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6. Problem Statement
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.
 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.
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7. Proposed 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 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
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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.
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9. Conclusions
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.
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10. 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.
 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.
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11. References
Here are some references 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.
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12. BIT
Thank You
Q/A