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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Predicting The Results Of IPL Match Using Random Forest Classifier |
| Authors Name: | Ms.D. Shanmugapriya , S.Shobika , A.Bushra Begum |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_216787
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| Published Paper Id: | IJNRD2403669 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | 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. |
| Keywords: | IPL, random forest, logistic regression, streamlit |
| Cite Article: | "Predicting The Results Of IPL Match Using Random Forest Classifier", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.g558-g566, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403669.pdf |
| Downloads: | 00033 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2403669 Registration ID: 216787 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: g558-g566 Country: kallakurichi, Tamil Nadu, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403669 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403669 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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