Predicting The Results Of IPL Match Using Random Forest Classifier

Registration ID: IJNRD_216787

Published ID: IJNRD2403669

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Ms.D. Shanmugapriya, S.Shobika, A.Bushra Begum

IPL, random forest, logistic regression, streamlit

In today’s date data analysis is need for every data analytics to examine the sets of data to extract the useful information from it and to draw conclusion according to the information. Data analytics techniques and algorithms are more used by the commercial industries which enables them to take precise business decisions. It is also used by the analysts and the experts to authenticate or negate experimental layouts, assumptions and conclusions. In recent years the analytics is being used in the field of sports to predict and draw various insights. Due to the involvement of money, team spirit, city loyalty and a massive fan following, the outcome of matches is very important for all stake holders. In this paper, the past seven year’s data of IPL containing the player’s details, match venue details, teams, ball to ball details, is taken and analysed to draw various conclusions which help in the improvement of a player’s performance. Predicting the outcomes [3] of IPL matches has become a captivating challenge for data enthusiasts and sports enthusiasts alike. In this project, we leverage the power of machine learning algorithms, specifically Random Forest and Logistic Regression, to forecast IPL match results. We collect and analyse historical IPL match data. Through feature engineering and preprocessing, we prepare the data for modelling. Random Forest and Logistic Regression, two robust machine learning algorithms, are applied to build predictive models. We evaluate the models using performance metrics like accuracy, precision, and recall. By employing these machine learning techniques, we aim to provide an application-based solution by deploying on Streamlit for forecasting IPL match result. Stream lit is an open-source app framework that allows you to create web apps from data scripts in pure Python, without requiring front-end development experience. The model used the supervised machine learning algorithm to predict the winning [8]. Random Forest Classifier used for good accuracy and the stable accuracy so that desired predicted output is accurate.

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Ms.D. Shanmugapriya, S.Shobika, & A.Bushra Begum (March-2024). Predicting The Results Of IPL Match Using Random Forest Classifier. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), g558-g566. https://ijnrd.org/papers/IJNRD2403669.pdf

Volume 9 Issue 3, March-2024

Pages : g558-g566

Paper Reg. ID: IJNRD_216787

Published Paper Id: IJNRD2403669

Research Area: Computer Engineering 

Author Type: Indian Author

Country: kallakurichi, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403669.pdf

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