T-20 Cricket Predicator
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T-20 Predictor is a tool developed in WEKA to predict cricket scores and match results in T-20 matches based on parameters like venue and pitch type. It uses linear regression to predict scores in the first innings and decision trees to classify whether the batting team will win, lose or tie while chasing a target in the second innings. Data on past T-20 matches was collected from espncricinfo.com and cleaned before being used to build and test the predictive models.
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This document discusses decision tree induction and attribute selection measures. It describes common measures like information gain, gain ratio, and Gini index that are used to select the best splitting attribute at each node in decision tree construction. It provides examples to illustrate information gain calculation for both discrete and continuous attributes. The document also discusses techniques for handling large datasets like SLIQ and SPRINT that build decision trees in a scalable manner by maintaining attribute value lists.
Data Mining Concepts and Techniques.ppt
This document discusses classification techniques in data mining, including decision trees. It covers supervised vs. unsupervised learning, the classification process, decision tree induction using information gain and other measures, handling continuous attributes, overfitting, and tree pruning. Specific algorithms covered include ID3, C4.5, CART, and CHAID. The goal of classification and how decision trees are constructed from the training data is explained at a high level.
Data Mining Concepts and Techniques.ppt
This document discusses classification techniques for data mining. It covers supervised and unsupervised learning methods. Specifically, it describes classification as a two-step process involving model construction from training data and then using the model to classify new data. Several classification algorithms are covered, including decision tree induction, Bayes classification, and rule-based classification. Evaluation metrics like accuracy and techniques to improve classification like ensemble methods are also summarized.
unit classification.pptx
The document discusses classification techniques for supervised learning problems. It describes classification as predicting categorical class labels based on a training set of labeled data. The classification process involves constructing a model from the training set and then using the model to classify new unlabeled data. Common classification techniques discussed include decision tree induction, Bayesian classification methods, and rule-based classification. Model evaluation and techniques for improving accuracy, such as ensemble methods, are also covered.
Application of K-Means Clustering Algorithm for Classification of NBA Guards
In this study, we discuss the application of K-means clustering technique on classification of NBA guards, including
determination category number, classification results analysis and evaluation about result. Based on the NBA data, using rebounds,
assists and points as clustering factors to K-Means clustering analysis. We implement an improved K-Means clustering analysis for
classification of NBA guards. Further experimental result shows that the best sample classification number is six according to the
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Machine Learning-Driven Injury Prediction for a Professional Sports Team
Machine Learning-Driven Injury Prediction for a Professional Sports TeamInstitute of Contemporary Sciences
Thomas Huijskens is a Principal Data Scientist at QuantumBlack, McKinsey company, an advanced analytics firm operating at the intersection of strategy, technology, and design to improve performance outcomes for organizations.
His talk during the Data Science Conference will focus on the usage of analytics and AI in order to predict sport injuries.Implementation of Improved ID3 Algorithm to Obtain more Optimal Decision Tree.
Decision tree learning is the discipline to create a predictive model to map the different items in the
set and respective target values and associate them in a way that is true to every element. This concept is used in
statistics, data mining and machine learning due to its simple and effectiveness.
Among the various strategies available to construct the decision trees ID3 is one of the simplest and
most widely used decision tree algorithm, but ID3 algorithm gives more importance to attributes having
multiple values while selecting node. This major shortcoming affects the accuracy of decision tree. In this paper
we are proposing improvement in ID3 algorithm using association function (AF). The Experimental result
shows improved ID3 algorithm can overcome shortcomings of ID3 which will also improve the accuracy of ID3
algorithm.
Data Mining:Concepts and Techniques, Chapter 8. Classification: Basic Concepts
Slides contain:
Classification: Basic Concepts,
Decision Tree Induction,
Bayes Classification Methods,
Rule-Based Classification,
Model Evaluation and Selection,
Techniques to Improve Classification Accuracy: Ensemble Methods,
Summary
by
Jiawei Han, Micheline Kamber, and Jian Pei,
University of Illinois at Urbana-Champaign &
Simon Fraser University,
©2013 Han, Kamber & Pei. All rights reserved.
UNIT 2 _ Data Processing and Aanalytics.pptx
This document provides an overview of data processing and analytics using MATLAB. It discusses importing data into MATLAB from files like CSV and Excel files. It describes how to organize data into tables and matrices and how to index and subset tables. The document also covers working with date and time data, including converting between datetime and duration formats. Finally, it discusses preprocessing data, such as handling missing values, normalizing data, and identifying missing or non-zero values using functions like isnan, ismissing, and nnz.
Classification (ML).ppt
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
IBM has a rich history with machine learning. One of its own, Arthur Samuel, is credited for coining the term, “machine learning” with his research (link resides outside ibm.com) around the game of checkers. Robert Nealey, the self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962, and he lost to the computer. Compared to what can be done today, this feat seems trivial, but it’s considered a major milestone in the field of artificial intelligence.
Over the last couple of decades, the technological advances in storage and processing power have enabled some innovative products based on machine learning, such as Netflix’s recommendation engine and self-driving cars.
Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, and to uncover key insights in data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase. They will be required to help identify the most relevant business questions and the data to answer them.
Machine learning algorithms are typically created using frameworks that accelerate solution development, such as TensorFlow and PyTorch.
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Machine Learning vs. Deep Learning vs. Neural Networks
Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, neural networks is actually a sub-field of machine learning, and deep learning is a sub-field of neural networks.
The way in which deep learning and machine learning differ is in how each algorithm learns. "Deep" machine learning can use labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled dataset. Deep learning can ingest unstructured data in its raw form (e.g., text or images), and it can automatically determine the set of features which distinguish different categories of data from one another. This eliminates some of the human intervention required and enables the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman notes in this MIT lecture (link resides outside ibm.com).
Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the set of features to understand the differences between data inputs,
20MEMECH Part 3- Classification.pdf
This document discusses various classification algorithms including logistic regression, Naive Bayes, support vector machines, k-nearest neighbors, decision trees, and random forests. It provides examples of using logistic regression and support vector machines for classification tasks. For logistic regression, it demonstrates building a model to classify handwritten digits from the MNIST dataset. For support vector machines, it uses a banknote authentication dataset to classify currency notes as authentic or fraudulent. The document discusses evaluating model performance using metrics like confusion matrix, accuracy, precision, recall, and F1 score.
GAMING BOT USING REINFORCEMENT LEARNING
The document discusses using reinforcement learning techniques to develop a gaming bot that can learn to play blackjack and other games. Specifically, it proposes using Q-learning, a model-free reinforcement learning approach, to train an agent to learn an optimal blackjack strategy based on rewards and punishments received from the environment during gameplay. It also discusses using Monte Carlo prediction and actor-critic learning methods to help overcome some of the limitations of traditional machine learning approaches for developing gaming bots. The goal is to eventually create an agent that can learn a variety of games in an open-ended, human-like manner through reinforcement learning.
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"Of course, you prefer to take players who are fully fit, but that's okay. We want to wait and be patient for some players even if they cannot play in those first matches," he told a press conference. The 37-year-old Vertonghen, Belgium’s Euro Cup 2024 most-capped international with 154 appearances, is struggling to shake off a groin injury.
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However, in 2023, they played one another twice, with France endearing both matches 4-0 and 2-1 individually. Against Poland and Austria, the Netherlands also have a stout record, winning just under half the matches. They faced Austria at Euro 2020, engaging 2-0, and they haven't lost to Poland since 1979.
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Five days later, they travel south to face France in Leipzig, a side led by Kylian Mbappe - one of the finest players in the world currently and one of the most impressive players in his nation's history. To conclude, they face Austria in Berlin, knowing it could be the end of the road if they don't perform.
Ronald Koeman is widely considered one of the more successful Dutch managers in Premier League history, considering the nation has a reputation for struggling to replicate their talents in England. The former Everton manager went against that script and shone — and now he is back managing his nation.
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T-20 Cricket Predicator
- 1. BY : RAJAT BAJAJ 101303130 COE-6 T20 PREDICTOR
- 2. T-20 PREDICTOR T-20 Predictor is a calculation tool developed in WEKA , which will be used in cricket to predict scores and possible results of a T-20 match depending upon certain parameters like Venue , Pitch etc.
- 3. Objectives 1. In the first innings of a T-20 match, it acts as a Score Predictor i.e it will predict score at various stages for Batting Team. 2. In the second innings, it works as a Win predictor i.e it will predict whether Batting Team will win or lose while chasing a Target given by the opposite Team.
- 4. Data Selection and Collection -> Data was gathered from espncricinfo.com -> Data gathered initially was unstructured and raw -> Data was cleaned and duplicates were removed -> Only important attributes were taken in consideration -> Data was stored
- 6. Methodologies 1. Score Predictor : In this part , we will use linear regression technique to predict a continuous numeric value for score. 2. Win predictor : In this part , we will use Classification using Decision Tree to predict either win , loss or a tie.
- 7. Methodologies Linear Regression : It is a data mining function that predicts a numerical value for an attribute (to be predicted ), by using values of other attributes Classification : Decision tree builds classification models in the form of a tree structure. It breaks down a dataset into smaller subsets and at the same time an associated decision tree is incrementally developed.
- 8. Attributes There are total 9 attributes in the data-set Data-set will consist of 73 records. Venue has values : Home , Away and Neutral Pitch type has values Green , Flat , Hard and Dry Scores are given by their values and number of wickets fallen Batting has two values : 1st and 2nd Result can have three values : Win , Loss or Tie In Phase-1 : 20-over score attribute will be class attribute In Phase-2: Result attribute will be class attribute