Pub Date : 2022-04-14DOI: 10.46610/rrmlcc.2022.v01i01.005
S. Singh, A. Dalvi, Nitish Patel, R. Khokale
Machine learning is the most well-known field nowadays for predicting future output and making better decisions based on these predictions. Cricket is a popular sport that is watched and played in over 100 nations across the world. Many of these cricket fans are rooting for their side to succeed and win the match. Teams must focus on their performance and areas of strength in order to ensure that their teams win. Similarly, predicting the winner of a cricket match is dependent on a number of criteria such as the toss, team strengths, venue and weather conditions, and so on. The purpose of this research study is to perform exploratory data analysis on a cricket dataset and to predict the winner of the IPL match. Machine learning models trained on the given features are used to predict the winner of an IPL match. Varied machine learning techniques, like Random Forest, SVM, Linear Regression, Logistic Regression, and Decision Trees, have been utilized and deployed on test and training datasets of various sizes for the goal of model construction. For legal betting applications, match reporting media, and cricket fans, this concept is quite valuable. Exploratory data analysis on cricket dataset will be beneficial for cricket team management or analytics team to assess the team’s strength.
{"title":"Analysis and Prediction of Cricket Match Using Machine Learning","authors":"S. Singh, A. Dalvi, Nitish Patel, R. Khokale","doi":"10.46610/rrmlcc.2022.v01i01.005","DOIUrl":"https://doi.org/10.46610/rrmlcc.2022.v01i01.005","url":null,"abstract":"Machine learning is the most well-known field nowadays for predicting future output and making better decisions based on these predictions. Cricket is a popular sport that is watched and played in over 100 nations across the world. Many of these cricket fans are rooting for their side to succeed and win the match. Teams must focus on their performance and areas of strength in order to ensure that their teams win. Similarly, predicting the winner of a cricket match is dependent on a number of criteria such as the toss, team strengths, venue and weather conditions, and so on. The purpose of this research study is to perform exploratory data analysis on a cricket dataset and to predict the winner of the IPL match. Machine learning models trained on the given features are used to predict the winner of an IPL match. Varied machine learning techniques, like Random Forest, SVM, Linear Regression, Logistic Regression, and Decision Trees, have been utilized and deployed on test and training datasets of various sizes for the goal of model construction. For legal betting applications, match reporting media, and cricket fans, this concept is quite valuable. Exploratory data analysis on cricket dataset will be beneficial for cricket team management or analytics team to assess the team’s strength.","PeriodicalId":276657,"journal":{"name":"Research & Reviews: Machine Learning and Cloud Computing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132310978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-08DOI: 10.46610/rrmlcc.2022.v01i01.004
Saumya Singh, Shivani Chauhan, Er.Mahendra Kumar
For a long time, people have been trying to find a way to retrieve information from a large text database. Convert data into information we need. In current search engines, when we search about something rather than giving the precise answer it takes out keywords from our search and gives us documents or web pages related to those words but what we want is the exact answer, why does the user have to search for it. That is, search engines deal more with whole document retrieval. However, a user often wants an exact or specific answer to the question. For instance, given the question "When is Holi festival this year?", what he wants is the answer "March 9, 2022", rather than to read through lots of web pages that contain the words "Holi", "festival", "year", etc. to find the date of the festival. That is, what a user needs is information retrieval, rather than current document retrieval. We handle the task of answering questions, where the answers are in documents in an extensive text database. We take on a machine learning technique to answer questions. In particular, answer candidates are classified and ranked by a classifier trainee donaset of question-answerpairs.
{"title":"Framework for Designing Questionnaire Using Machine Learning","authors":"Saumya Singh, Shivani Chauhan, Er.Mahendra Kumar","doi":"10.46610/rrmlcc.2022.v01i01.004","DOIUrl":"https://doi.org/10.46610/rrmlcc.2022.v01i01.004","url":null,"abstract":"For a long time, people have been trying to find a way to retrieve information from a large text database. Convert data into information we need. In current search engines, when we search about something rather than giving the precise answer it takes out keywords from our search and gives us documents or web pages related to those words but what we want is the exact answer, why does the user have to search for it. That is, search engines deal more with whole document retrieval. However, a user often wants an exact or specific answer to the question. For instance, given the question \"When is Holi festival this year?\", what he wants is the answer \"March 9, 2022\", rather than to read through lots of web pages that contain the words \"Holi\", \"festival\", \"year\", etc. to find the date of the festival. That is, what a user needs is information retrieval, rather than current document retrieval. We handle the task of answering questions, where the answers are in documents in an extensive text database. We take on a machine learning technique to answer questions. In particular, answer candidates are classified and ranked by a classifier trainee donaset of question-answerpairs.","PeriodicalId":276657,"journal":{"name":"Research & Reviews: Machine Learning and Cloud Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114512123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-31DOI: 10.46610/rrmlcc.2022.v01i01.003
Profound learning strategies are significantly respected in the exploration field of agribusiness. The farming variables climate, downpour, soil, pesticides, and manures are the really mindful angles to raise the creation of yields. The central fundamental key part of farming is Soil for crop developing. Assessment of soil is an imperative piece of soil resource the executives in cultivation. The fundamental objective of this work is to explore soil supplements using profound learning order strategies.Toanalyse the soil nutrients, the former need to go to the branch of Agriculture or Cooperation and Farmers Welfare. This work takes an areaofTamil Nadu in India to dissect the dirt supplements.Particular sort's dirt has an assorted assortment of enhancements. The dirt examination is particularly helpful for cultivators to find which kind of harvests to be created in a particular soil condition. This framework picks Nitrogen, Phosphorus, Potassium, Calcium, Magnesium, Sulphur, Iron, Zinc, etc, supplements for examining the dirt enhancements using the CRA approach of the Neural organization. The fundamental objective of this work is to examine soil supplements using profound learning order methods.
{"title":"Recognition of Solid Inorganic Substances and Crop Recommendation","authors":"","doi":"10.46610/rrmlcc.2022.v01i01.003","DOIUrl":"https://doi.org/10.46610/rrmlcc.2022.v01i01.003","url":null,"abstract":"Profound learning strategies are significantly respected in the exploration field of agribusiness. The farming variables climate, downpour, soil, pesticides, and manures are the really mindful angles to raise the creation of yields. The central fundamental key part of farming is Soil for crop developing. Assessment of soil is an imperative piece of soil resource the executives in cultivation. The fundamental objective of this work is to explore soil supplements using profound learning order strategies.Toanalyse the soil nutrients, the former need to go to the branch of Agriculture or Cooperation and Farmers Welfare. This work takes an areaofTamil Nadu in India to dissect the dirt supplements.Particular sort's dirt has an assorted assortment of enhancements. The dirt examination is particularly helpful for cultivators to find which kind of harvests to be created in a particular soil condition. This framework picks Nitrogen, Phosphorus, Potassium, Calcium, Magnesium, Sulphur, Iron, Zinc, etc, supplements for examining the dirt enhancements using the CRA approach of the Neural organization. The fundamental objective of this work is to examine soil supplements using profound learning order methods.","PeriodicalId":276657,"journal":{"name":"Research & Reviews: Machine Learning and Cloud Computing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130841961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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