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All UFC Fight Outcomes

Based in the US, the Ultimate Fighting Championship (UFC) is a mixed martial arts (MMA) promotion organization. It showcases the best fighters and is the biggest MMA promotion globally. Since its founding in 1993, the UFC has racked up thousands of bouts. The organization has experienced exponential growth, gaining millions of fans worldwide and becoming a global phenomenon.

Introduction

This repository contains the resolution to the 2nd assignment of the course Artificial Intelligence of the Bachelor in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto.

Objective

The main goal of this assignment is to explore different supervised learning algorithms and their applications in predicting the outcomes of a given classification problem. In this case, the group has chosen to tackle the problem involving the prediction of the winner of a UFC fight. In our resolution, we will be using the following algorithms:

  • Decision Trees
  • k-Nearest Neighbors (k-NN)
  • Support Vector Machines (SVM)

Dataset

The dataset was obtained from Kaggle and can be found here. It contains information about all the UFC fights ever, including the venue details and fighter statistics. The dataset contains 2 distinct files: ufc_all_fights.csv and clean_ufc_all_fights.csv. The ufc_all_fights.csv file contains all the information about the fights, but it is not clean and contains some missing values. The clean_ufc_all_fights.csv file contains the same information, but it is clean and ready for analysis.

Prerequisites

To run the code in this repository, you need to have the following software installed on your machine:

  • Python 3.x installed.
  • Jupyter Notebook or any other Python IDE (e.g., PyCharm, Visual Studio Code).
  • Required Python libraries: pandas, numpy, scikit-learn, matplotlib, and seaborn.

To install these libraries, you can use the following command in your terminal:

pip install numpy pandas matplotlib seaborn scikit-learn

Or, if you are using Anaconda, you can use the following command:

conda install numpy pandas matplotlib seaborn scikit-learn

Running the Notebook

All the instructions to run the code are provided in the notebook itself. The notebook is organized into sections, each corresponding to a specific part of the assignment. You can run each section independently or run the entire notebook at once.

License

This project is distributed under the MIT License.

This assignment was developed by:

About

A machine learning project exploring classification algorithms to predict UFC fight outcomes, developed for the Artificial Intelligence course at FEUP.

sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894

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classificationcomparative-studydata-sciencejupyter-notebookmachine-learningpredictive-modelingpythonscikit-learnsports-analyticsufc

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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All UFC Fight Outcomes
Based in the US, the Ultimate Fighting Championship (UFC) is a mixed martial arts (MMA) promotion organization. It showcases the best fighters and is the biggest MMA promotion globally. Since its founding in 1993, the UFC has racked up thousands of bouts. The organization has experienced exponential growth, gaining millions of fans worldwide and becoming a global phenomenon.
Introduction
This repository contains the resolution to the 2nd assignment of the course Artificial Intelligence of the Bachelor in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto.
Objective
The main goal of this assignment is to explore different supervised learning algorithms and their applications in predicting the outcomes of a given classification problem. In this case, the group has chosen to tackle the problem involving the prediction of the winner of a UFC fight. In our resolution, we will be using the following algorithms:
  • Decision Trees
  • k-Nearest Neighbors (k-NN)
  • Support Vector Machines (SVM)
Dataset
The dataset was obtained from Kaggle and can be found here. It contains information about all the UFC fights ever, including the venue details and fighter statistics. The dataset contains 2 distinct files: ufc_all_fights.csv and clean_ufc_all_fights.csv. The ufc_all_fights.csv file contains all the information about the fights, but it is not clean and contains some missing values. The clean_ufc_all_fights.csv file contains the same information, but it is clean and ready for analysis.
Prerequisites
To run the code in this repository, you need to have the following software installed on your machine:
  • Python 3.x installed.
  • Jupyter Notebook or any other Python IDE (e.g., PyCharm, Visual Studio Code).
  • Required Python libraries: pandas, numpy, scikit-learn, matplotlib, and seaborn.
To install these libraries, you can use the following command in your terminal:
pip install numpy pandas matplotlib seaborn scikit-learn
Or, if you are using Anaconda, you can use the following command:
conda install numpy pandas matplotlib seaborn scikit-learn
Running the Notebook
All the instructions to run the code are provided in the notebook itself. The notebook is organized into sections, each corresponding to a specific part of the assignment. You can run each section independently or run the entire notebook at once.
License
This project is distributed under the MIT License.
This assignment was developed by:
About
A machine learning project exploring classification algorithms to predict UFC fight outcomes, developed for the Artificial Intelligence course at FEUP.
sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894
Topics
classificationcomparative-studydata-sciencejupyter-notebookmachine-learningpredictive-modelingpythonscikit-learnsports-analyticsufc
Resources
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MIT license
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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All UFC Fight Outcomes
Based in the US, the Ultimate Fighting Championship (UFC) is a mixed martial arts (MMA) promotion organization. It showcases the best fighters and is the biggest MMA promotion globally. Since its founding in 1993, the UFC has racked up thousands of bouts. The organization has experienced exponential growth, gaining millions of fans worldwide and becoming a global phenomenon.
Introduction
This repository contains the resolution to the 2nd assignment of the course Artificial Intelligence of the Bachelor in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto.
Objective
The main goal of this assignment is to explore different supervised learning algorithms and their applications in predicting the outcomes of a given classification problem. In this case, the group has chosen to tackle the problem involving the prediction of the winner of a UFC fight. In our resolution, we will be using the following algorithms:
  • Decision Trees
  • k-Nearest Neighbors (k-NN)
  • Support Vector Machines (SVM)
Dataset
The dataset was obtained from Kaggle and can be found here. It contains information about all the UFC fights ever, including the venue details and fighter statistics. The dataset contains 2 distinct files: ufc_all_fights.csv and clean_ufc_all_fights.csv. The ufc_all_fights.csv file contains all the information about the fights, but it is not clean and contains some missing values. The clean_ufc_all_fights.csv file contains the same information, but it is clean and ready for analysis.
Prerequisites
To run the code in this repository, you need to have the following software installed on your machine:
  • Python 3.x installed.
  • Jupyter Notebook or any other Python IDE (e.g., PyCharm, Visual Studio Code).
  • Required Python libraries: pandas, numpy, scikit-learn, matplotlib, and seaborn.
To install these libraries, you can use the following command in your terminal:
pip install numpy pandas matplotlib seaborn scikit-learn
Or, if you are using Anaconda, you can use the following command:
conda install numpy pandas matplotlib seaborn scikit-learn
Running the Notebook
All the instructions to run the code are provided in the notebook itself. The notebook is organized into sections, each corresponding to a specific part of the assignment. You can run each section independently or run the entire notebook at once.
License
This project is distributed under the MIT License.
This assignment was developed by:
About
A machine learning project exploring classification algorithms to predict UFC fight outcomes, developed for the Artificial Intelligence course at FEUP.
sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894
Topics
classificationcomparative-studydata-sciencejupyter-notebookmachine-learningpredictive-modelingpythonscikit-learnsports-analyticsufc
Resources
Readme
MIT license
Activity
Stars
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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All UFC Fight Outcomes
Based in the US, the Ultimate Fighting Championship (UFC) is a mixed martial arts (MMA) promotion organization. It showcases the best fighters and is the biggest MMA promotion globally. Since its founding in 1993, the UFC has racked up thousands of bouts. The organization has experienced exponential growth, gaining millions of fans worldwide and becoming a global phenomenon.
Introduction
This repository contains the resolution to the 2nd assignment of the course Artificial Intelligence of the Bachelor in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto.
Objective
The main goal of this assignment is to explore different supervised learning algorithms and their applications in predicting the outcomes of a given classification problem. In this case, the group has chosen to tackle the problem involving the prediction of the winner of a UFC fight. In our resolution, we will be using the following algorithms:
  • Decision Trees
  • k-Nearest Neighbors (k-NN)
  • Support Vector Machines (SVM)
Dataset
The dataset was obtained from Kaggle and can be found here. It contains information about all the UFC fights ever, including the venue details and fighter statistics. The dataset contains 2 distinct files: ufc_all_fights.csv and clean_ufc_all_fights.csv. The ufc_all_fights.csv file contains all the information about the fights, but it is not clean and contains some missing values. The clean_ufc_all_fights.csv file contains the same information, but it is clean and ready for analysis.
Prerequisites
To run the code in this repository, you need to have the following software installed on your machine:
  • Python 3.x installed.
  • Jupyter Notebook or any other Python IDE (e.g., PyCharm, Visual Studio Code).
  • Required Python libraries: pandas, numpy, scikit-learn, matplotlib, and seaborn.
To install these libraries, you can use the following command in your terminal:
pip install numpy pandas matplotlib seaborn scikit-learn
Or, if you are using Anaconda, you can use the following command:
conda install numpy pandas matplotlib seaborn scikit-learn
Running the Notebook
All the instructions to run the code are provided in the notebook itself. The notebook is organized into sections, each corresponding to a specific part of the assignment. You can run each section independently or run the entire notebook at once.
License
This project is distributed under the MIT License.
This assignment was developed by:
About
A machine learning project exploring classification algorithms to predict UFC fight outcomes, developed for the Artificial Intelligence course at FEUP.
sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894
Topics
classificationcomparative-studydata-sciencejupyter-notebookmachine-learningpredictive-modelingpythonscikit-learnsports-analyticsufc
Resources
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MIT license
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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All UFC Fight Outcomes
Based in the US, the Ultimate Fighting Championship (UFC) is a mixed martial arts (MMA) promotion organization. It showcases the best fighters and is the biggest MMA promotion globally. Since its founding in 1993, the UFC has racked up thousands of bouts. The organization has experienced exponential growth, gaining millions of fans worldwide and becoming a global phenomenon.
Introduction
This repository contains the resolution to the 2nd assignment of the course Artificial Intelligence of the Bachelor in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto.
Objective
The main goal of this assignment is to explore different supervised learning algorithms and their applications in predicting the outcomes of a given classification problem. In this case, the group has chosen to tackle the problem involving the prediction of the winner of a UFC fight. In our resolution, we will be using the following algorithms:
  • Decision Trees
  • k-Nearest Neighbors (k-NN)
  • Support Vector Machines (SVM)
Dataset
The dataset was obtained from Kaggle and can be found here. It contains information about all the UFC fights ever, including the venue details and fighter statistics. The dataset contains 2 distinct files: ufc_all_fights.csv and clean_ufc_all_fights.csv. The ufc_all_fights.csv file contains all the information about the fights, but it is not clean and contains some missing values. The clean_ufc_all_fights.csv file contains the same information, but it is clean and ready for analysis.
Prerequisites
To run the code in this repository, you need to have the following software installed on your machine:
  • Python 3.x installed.
  • Jupyter Notebook or any other Python IDE (e.g., PyCharm, Visual Studio Code).
  • Required Python libraries: pandas, numpy, scikit-learn, matplotlib, and seaborn.
To install these libraries, you can use the following command in your terminal:
pip install numpy pandas matplotlib seaborn scikit-learn
Or, if you are using Anaconda, you can use the following command:
conda install numpy pandas matplotlib seaborn scikit-learn
Running the Notebook
All the instructions to run the code are provided in the notebook itself. The notebook is organized into sections, each corresponding to a specific part of the assignment. You can run each section independently or run the entire notebook at once.
License
This project is distributed under the MIT License.
This assignment was developed by:
About
A machine learning project exploring classification algorithms to predict UFC fight outcomes, developed for the Artificial Intelligence course at FEUP.
sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894
Topics
classificationcomparative-studydata-sciencejupyter-notebookmachine-learningpredictive-modelingpythonscikit-learnsports-analyticsufc
Resources
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MIT license
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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All UFC Fight Outcomes
Based in the US, the Ultimate Fighting Championship (UFC) is a mixed martial arts (MMA) promotion organization. It showcases the best fighters and is the biggest MMA promotion globally. Since its founding in 1993, the UFC has racked up thousands of bouts. The organization has experienced exponential growth, gaining millions of fans worldwide and becoming a global phenomenon.
Introduction
This repository contains the resolution to the 2nd assignment of the course Artificial Intelligence of the Bachelor in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto.
Objective
The main goal of this assignment is to explore different supervised learning algorithms and their applications in predicting the outcomes of a given classification problem. In this case, the group has chosen to tackle the problem involving the prediction of the winner of a UFC fight. In our resolution, we will be using the following algorithms:
  • Decision Trees
  • k-Nearest Neighbors (k-NN)
  • Support Vector Machines (SVM)
Dataset
The dataset was obtained from Kaggle and can be found here. It contains information about all the UFC fights ever, including the venue details and fighter statistics. The dataset contains 2 distinct files: ufc_all_fights.csv and clean_ufc_all_fights.csv. The ufc_all_fights.csv file contains all the information about the fights, but it is not clean and contains some missing values. The clean_ufc_all_fights.csv file contains the same information, but it is clean and ready for analysis.
Prerequisites
To run the code in this repository, you need to have the following software installed on your machine:
  • Python 3.x installed.
  • Jupyter Notebook or any other Python IDE (e.g., PyCharm, Visual Studio Code).
  • Required Python libraries: pandas, numpy, scikit-learn, matplotlib, and seaborn.
To install these libraries, you can use the following command in your terminal:
pip install numpy pandas matplotlib seaborn scikit-learn
Or, if you are using Anaconda, you can use the following command:
conda install numpy pandas matplotlib seaborn scikit-learn
Running the Notebook
All the instructions to run the code are provided in the notebook itself. The notebook is organized into sections, each corresponding to a specific part of the assignment. You can run each section independently or run the entire notebook at once.
License
This project is distributed under the MIT License.
This assignment was developed by:
About
A machine learning project exploring classification algorithms to predict UFC fight outcomes, developed for the Artificial Intelligence course at FEUP.
sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894
Topics
classificationcomparative-studydata-sciencejupyter-notebookmachine-learningpredictive-modelingpythonscikit-learnsports-analyticsufc
Resources
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MIT license
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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All UFC Fight Outcomes
Based in the US, the Ultimate Fighting Championship (UFC) is a mixed martial arts (MMA) promotion organization. It showcases the best fighters and is the biggest MMA promotion globally. Since its founding in 1993, the UFC has racked up thousands of bouts. The organization has experienced exponential growth, gaining millions of fans worldwide and becoming a global phenomenon.
Introduction
This repository contains the resolution to the 2nd assignment of the course Artificial Intelligence of the Bachelor in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto.
Objective
The main goal of this assignment is to explore different supervised learning algorithms and their applications in predicting the outcomes of a given classification problem. In this case, the group has chosen to tackle the problem involving the prediction of the winner of a UFC fight. In our resolution, we will be using the following algorithms:
  • Decision Trees
  • k-Nearest Neighbors (k-NN)
  • Support Vector Machines (SVM)
Dataset
The dataset was obtained from Kaggle and can be found here. It contains information about all the UFC fights ever, including the venue details and fighter statistics. The dataset contains 2 distinct files: ufc_all_fights.csv and clean_ufc_all_fights.csv. The ufc_all_fights.csv file contains all the information about the fights, but it is not clean and contains some missing values. The clean_ufc_all_fights.csv file contains the same information, but it is clean and ready for analysis.
Prerequisites
To run the code in this repository, you need to have the following software installed on your machine:
  • Python 3.x installed.
  • Jupyter Notebook or any other Python IDE (e.g., PyCharm, Visual Studio Code).
  • Required Python libraries: pandas, numpy, scikit-learn, matplotlib, and seaborn.
To install these libraries, you can use the following command in your terminal:
pip install numpy pandas matplotlib seaborn scikit-learn
Or, if you are using Anaconda, you can use the following command:
conda install numpy pandas matplotlib seaborn scikit-learn
Running the Notebook
All the instructions to run the code are provided in the notebook itself. The notebook is organized into sections, each corresponding to a specific part of the assignment. You can run each section independently or run the entire notebook at once.
License
This project is distributed under the MIT License.
This assignment was developed by:
About
A machine learning project exploring classification algorithms to predict UFC fight outcomes, developed for the Artificial Intelligence course at FEUP.
sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894
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All UFC Fight Outcomes
Based in the US, the Ultimate Fighting Championship (UFC) is a mixed martial arts (MMA) promotion organization. It showcases the best fighters and is the biggest MMA promotion globally. Since its founding in 1993, the UFC has racked up thousands of bouts. The organization has experienced exponential growth, gaining millions of fans worldwide and becoming a global phenomenon.
Introduction
This repository contains the resolution to the 2nd assignment of the course Artificial Intelligence of the Bachelor in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto.
Objective
The main goal of this assignment is to explore different supervised learning algorithms and their applications in predicting the outcomes of a given classification problem. In this case, the group has chosen to tackle the problem involving the prediction of the winner of a UFC fight. In our resolution, we will be using the following algorithms:
  • Decision Trees
  • k-Nearest Neighbors (k-NN)
  • Support Vector Machines (SVM)
Dataset
The dataset was obtained from Kaggle and can be found here. It contains information about all the UFC fights ever, including the venue details and fighter statistics. The dataset contains 2 distinct files: ufc_all_fights.csv and clean_ufc_all_fights.csv. The ufc_all_fights.csv file contains all the information about the fights, but it is not clean and contains some missing values. The clean_ufc_all_fights.csv file contains the same information, but it is clean and ready for analysis.
Prerequisites
To run the code in this repository, you need to have the following software installed on your machine:
  • Python 3.x installed.
  • Jupyter Notebook or any other Python IDE (e.g., PyCharm, Visual Studio Code).
  • Required Python libraries: pandas, numpy, scikit-learn, matplotlib, and seaborn.
To install these libraries, you can use the following command in your terminal:
pip install numpy pandas matplotlib seaborn scikit-learn
Or, if you are using Anaconda, you can use the following command:
conda install numpy pandas matplotlib seaborn scikit-learn
Running the Notebook
All the instructions to run the code are provided in the notebook itself. The notebook is organized into sections, each corresponding to a specific part of the assignment. You can run each section independently or run the entire notebook at once.
License
This project is distributed under the MIT License.
This assignment was developed by:
About
A machine learning project exploring classification algorithms to predict UFC fight outcomes, developed for the Artificial Intelligence course at FEUP.
sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894
Topics
classificationcomparative-studydata-sciencejupyter-notebookmachine-learningpredictive-modelingpythonscikit-learnsports-analyticsufc
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Contributors
Languages