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Ensemble Learning: Bagging and Random Forests

This repository demonstrates the application of ensemble learning methods to a medical diagnostic task. By utilizing Bagging and Random Forest algorithms, the project aims to reduce variance and prevent overfitting commonly associated with individual decision trees.

Project Overview

The core objective is to predict whether a patient suffers from liver disease based on 10 clinical features, including Albumin levels, age, and gender. The project evaluates the performance gain achieved by transitioning from a single estimator to a robust ensemble of models.

Key Features

  • Ensemble Methods: Implementation of Bagging (Bootstrap Aggregating) to create diverse subsets of the training data.
  • Medical Data Analysis: Processing and feature engineering for the Indian Liver Patient dataset.
  • Classifier Evaluation: Comparative analysis of Decision Tree performance versus ensemble-based Bagging Classifiers.
  • Hyperparameter Tuning: Configuration of estimators and sample distribution to optimize classification accuracy.

Tech Stack

  • Language: Python
  • Machine Learning: scikit-learn (DecisionTreeClassifier, BaggingClassifier)
  • Data Handling: pandas, numpy

Dataset Information

  • Source: UCI Machine Learning Repository Liver Dataset
  • Source: Kaggle Bike Rentals
  • Task: Binary Classification (Predicting liver disease status)
  • Features: 10 clinical attributes

Setup & Installation

1. Environment Configuration

It is recommended to use a virtual environment for this project:

# Create and activate the environment
git clone git clone https://github.com/Joe-Naz01/bagging_random.git
cd bagging_random
conda create -n ensemble_ml python=3.10 -y
conda activate ensemble_ml
# Install dependencies
pip install -r requirements.txt
jupyter notebook

About

This project implements Ensemble Learning techniques to predict liver disease using the Indian Liver Patient dataset. It focuses on improving model robustness by implementing Bagging and Random Forest classifiers to aggregate the predictions of multiple decision trees.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} 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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Ensemble Learning: Bagging and Random Forests
This repository demonstrates the application of ensemble learning methods to a medical diagnostic task. By utilizing Bagging and Random Forest algorithms, the project aims to reduce variance and prevent overfitting commonly associated with individual decision trees.
Project Overview
The core objective is to predict whether a patient suffers from liver disease based on 10 clinical features, including Albumin levels, age, and gender. The project evaluates the performance gain achieved by transitioning from a single estimator to a robust ensemble of models.
Key Features
  • Ensemble Methods: Implementation of Bagging (Bootstrap Aggregating) to create diverse subsets of the training data.
  • Medical Data Analysis: Processing and feature engineering for the Indian Liver Patient dataset.
  • Classifier Evaluation: Comparative analysis of Decision Tree performance versus ensemble-based Bagging Classifiers.
  • Hyperparameter Tuning: Configuration of estimators and sample distribution to optimize classification accuracy.
Tech Stack
  • Language: Python
  • Machine Learning: scikit-learn (DecisionTreeClassifier, BaggingClassifier)
  • Data Handling: pandas, numpy
Dataset Information
  • Source: UCI Machine Learning Repository Liver Dataset
  • Source: Kaggle Bike Rentals
  • Task: Binary Classification (Predicting liver disease status)
  • Features: 10 clinical attributes
Setup & Installation
1. Environment Configuration
It is recommended to use a virtual environment for this project:
# Create and activate the environment
git clone git clone https://github.com/Joe-Naz01/bagging_random.git
cd bagging_random
conda create -n ensemble_ml python=3.10 -y
conda activate ensemble_ml
# Install dependencies
pip install -r requirements.txt
jupyter notebook
About
This project implements Ensemble Learning techniques to predict liver disease using the Indian Liver Patient dataset. It focuses on improving model robustness by implementing Bagging and Random Forest classifiers to aggregate the predictions of multiple decision trees.
Resources
Readme
Activity
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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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Ensemble Learning: Bagging and Random Forests
This repository demonstrates the application of ensemble learning methods to a medical diagnostic task. By utilizing Bagging and Random Forest algorithms, the project aims to reduce variance and prevent overfitting commonly associated with individual decision trees.
Project Overview
The core objective is to predict whether a patient suffers from liver disease based on 10 clinical features, including Albumin levels, age, and gender. The project evaluates the performance gain achieved by transitioning from a single estimator to a robust ensemble of models.
Key Features
  • Ensemble Methods: Implementation of Bagging (Bootstrap Aggregating) to create diverse subsets of the training data.
  • Medical Data Analysis: Processing and feature engineering for the Indian Liver Patient dataset.
  • Classifier Evaluation: Comparative analysis of Decision Tree performance versus ensemble-based Bagging Classifiers.
  • Hyperparameter Tuning: Configuration of estimators and sample distribution to optimize classification accuracy.
Tech Stack
  • Language: Python
  • Machine Learning: scikit-learn (DecisionTreeClassifier, BaggingClassifier)
  • Data Handling: pandas, numpy
Dataset Information
  • Source: UCI Machine Learning Repository Liver Dataset
  • Source: Kaggle Bike Rentals
  • Task: Binary Classification (Predicting liver disease status)
  • Features: 10 clinical attributes
Setup & Installation
1. Environment Configuration
It is recommended to use a virtual environment for this project:
# Create and activate the environment
git clone git clone https://github.com/Joe-Naz01/bagging_random.git
cd bagging_random
conda create -n ensemble_ml python=3.10 -y
conda activate ensemble_ml
# Install dependencies
pip install -r requirements.txt
jupyter notebook
About
This project implements Ensemble Learning techniques to predict liver disease using the Indian Liver Patient dataset. It focuses on improving model robustness by implementing Bagging and Random Forest classifiers to aggregate the predictions of multiple decision trees.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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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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Ensemble Learning: Bagging and Random Forests
This repository demonstrates the application of ensemble learning methods to a medical diagnostic task. By utilizing Bagging and Random Forest algorithms, the project aims to reduce variance and prevent overfitting commonly associated with individual decision trees.
Project Overview
The core objective is to predict whether a patient suffers from liver disease based on 10 clinical features, including Albumin levels, age, and gender. The project evaluates the performance gain achieved by transitioning from a single estimator to a robust ensemble of models.
Key Features
  • Ensemble Methods: Implementation of Bagging (Bootstrap Aggregating) to create diverse subsets of the training data.
  • Medical Data Analysis: Processing and feature engineering for the Indian Liver Patient dataset.
  • Classifier Evaluation: Comparative analysis of Decision Tree performance versus ensemble-based Bagging Classifiers.
  • Hyperparameter Tuning: Configuration of estimators and sample distribution to optimize classification accuracy.
Tech Stack
  • Language: Python
  • Machine Learning: scikit-learn (DecisionTreeClassifier, BaggingClassifier)
  • Data Handling: pandas, numpy
Dataset Information
  • Source: UCI Machine Learning Repository Liver Dataset
  • Source: Kaggle Bike Rentals
  • Task: Binary Classification (Predicting liver disease status)
  • Features: 10 clinical attributes
Setup & Installation
1. Environment Configuration
It is recommended to use a virtual environment for this project:
# Create and activate the environment
git clone git clone https://github.com/Joe-Naz01/bagging_random.git
cd bagging_random
conda create -n ensemble_ml python=3.10 -y
conda activate ensemble_ml
# Install dependencies
pip install -r requirements.txt
jupyter notebook
About
This project implements Ensemble Learning techniques to predict liver disease using the Indian Liver Patient dataset. It focuses on improving model robustness by implementing Bagging and Random Forest classifiers to aggregate the predictions of multiple decision trees.
Resources
Readme
Activity
Stars
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0 forks
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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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Ensemble Learning: Bagging and Random Forests
This repository demonstrates the application of ensemble learning methods to a medical diagnostic task. By utilizing Bagging and Random Forest algorithms, the project aims to reduce variance and prevent overfitting commonly associated with individual decision trees.
Project Overview
The core objective is to predict whether a patient suffers from liver disease based on 10 clinical features, including Albumin levels, age, and gender. The project evaluates the performance gain achieved by transitioning from a single estimator to a robust ensemble of models.
Key Features
  • Ensemble Methods: Implementation of Bagging (Bootstrap Aggregating) to create diverse subsets of the training data.
  • Medical Data Analysis: Processing and feature engineering for the Indian Liver Patient dataset.
  • Classifier Evaluation: Comparative analysis of Decision Tree performance versus ensemble-based Bagging Classifiers.
  • Hyperparameter Tuning: Configuration of estimators and sample distribution to optimize classification accuracy.
Tech Stack
  • Language: Python
  • Machine Learning: scikit-learn (DecisionTreeClassifier, BaggingClassifier)
  • Data Handling: pandas, numpy
Dataset Information
  • Source: UCI Machine Learning Repository Liver Dataset
  • Source: Kaggle Bike Rentals
  • Task: Binary Classification (Predicting liver disease status)
  • Features: 10 clinical attributes
Setup & Installation
1. Environment Configuration
It is recommended to use a virtual environment for this project:
# Create and activate the environment
git clone git clone https://github.com/Joe-Naz01/bagging_random.git
cd bagging_random
conda create -n ensemble_ml python=3.10 -y
conda activate ensemble_ml
# Install dependencies
pip install -r requirements.txt
jupyter notebook
About
This project implements Ensemble Learning techniques to predict liver disease using the Indian Liver Patient dataset. It focuses on improving model robustness by implementing Bagging and Random Forest classifiers to aggregate the predictions of multiple decision trees.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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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Ensemble Learning: Bagging and Random Forests
This repository demonstrates the application of ensemble learning methods to a medical diagnostic task. By utilizing Bagging and Random Forest algorithms, the project aims to reduce variance and prevent overfitting commonly associated with individual decision trees.
Project Overview
The core objective is to predict whether a patient suffers from liver disease based on 10 clinical features, including Albumin levels, age, and gender. The project evaluates the performance gain achieved by transitioning from a single estimator to a robust ensemble of models.
Key Features
  • Ensemble Methods: Implementation of Bagging (Bootstrap Aggregating) to create diverse subsets of the training data.
  • Medical Data Analysis: Processing and feature engineering for the Indian Liver Patient dataset.
  • Classifier Evaluation: Comparative analysis of Decision Tree performance versus ensemble-based Bagging Classifiers.
  • Hyperparameter Tuning: Configuration of estimators and sample distribution to optimize classification accuracy.
Tech Stack
  • Language: Python
  • Machine Learning: scikit-learn (DecisionTreeClassifier, BaggingClassifier)
  • Data Handling: pandas, numpy
Dataset Information
  • Source: UCI Machine Learning Repository Liver Dataset
  • Source: Kaggle Bike Rentals
  • Task: Binary Classification (Predicting liver disease status)
  • Features: 10 clinical attributes
Setup & Installation
1. Environment Configuration
It is recommended to use a virtual environment for this project:
# Create and activate the environment
git clone git clone https://github.com/Joe-Naz01/bagging_random.git
cd bagging_random
conda create -n ensemble_ml python=3.10 -y
conda activate ensemble_ml
# Install dependencies
pip install -r requirements.txt
jupyter notebook
About
This project implements Ensemble Learning techniques to predict liver disease using the Indian Liver Patient dataset. It focuses on improving model robustness by implementing Bagging and Random Forest classifiers to aggregate the predictions of multiple decision trees.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
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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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Ensemble Learning: Bagging and Random Forests
This repository demonstrates the application of ensemble learning methods to a medical diagnostic task. By utilizing Bagging and Random Forest algorithms, the project aims to reduce variance and prevent overfitting commonly associated with individual decision trees.
Project Overview
The core objective is to predict whether a patient suffers from liver disease based on 10 clinical features, including Albumin levels, age, and gender. The project evaluates the performance gain achieved by transitioning from a single estimator to a robust ensemble of models.
Key Features
  • Ensemble Methods: Implementation of Bagging (Bootstrap Aggregating) to create diverse subsets of the training data.
  • Medical Data Analysis: Processing and feature engineering for the Indian Liver Patient dataset.
  • Classifier Evaluation: Comparative analysis of Decision Tree performance versus ensemble-based Bagging Classifiers.
  • Hyperparameter Tuning: Configuration of estimators and sample distribution to optimize classification accuracy.
Tech Stack
  • Language: Python
  • Machine Learning: scikit-learn (DecisionTreeClassifier, BaggingClassifier)
  • Data Handling: pandas, numpy
Dataset Information
  • Source: UCI Machine Learning Repository Liver Dataset
  • Source: Kaggle Bike Rentals
  • Task: Binary Classification (Predicting liver disease status)
  • Features: 10 clinical attributes
Setup & Installation
1. Environment Configuration
It is recommended to use a virtual environment for this project:
# Create and activate the environment
git clone git clone https://github.com/Joe-Naz01/bagging_random.git
cd bagging_random
conda create -n ensemble_ml python=3.10 -y
conda activate ensemble_ml
# Install dependencies
pip install -r requirements.txt
jupyter notebook
About
This project implements Ensemble Learning techniques to predict liver disease using the Indian Liver Patient dataset. It focuses on improving model robustness by implementing Bagging and Random Forest classifiers to aggregate the predictions of multiple decision trees.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Ensemble Learning: Bagging and Random Forests
This repository demonstrates the application of ensemble learning methods to a medical diagnostic task. By utilizing Bagging and Random Forest algorithms, the project aims to reduce variance and prevent overfitting commonly associated with individual decision trees.
Project Overview
The core objective is to predict whether a patient suffers from liver disease based on 10 clinical features, including Albumin levels, age, and gender. The project evaluates the performance gain achieved by transitioning from a single estimator to a robust ensemble of models.
Key Features
  • Ensemble Methods: Implementation of Bagging (Bootstrap Aggregating) to create diverse subsets of the training data.
  • Medical Data Analysis: Processing and feature engineering for the Indian Liver Patient dataset.
  • Classifier Evaluation: Comparative analysis of Decision Tree performance versus ensemble-based Bagging Classifiers.
  • Hyperparameter Tuning: Configuration of estimators and sample distribution to optimize classification accuracy.
Tech Stack
  • Language: Python
  • Machine Learning: scikit-learn (DecisionTreeClassifier, BaggingClassifier)
  • Data Handling: pandas, numpy
Dataset Information
  • Source: UCI Machine Learning Repository Liver Dataset
  • Source: Kaggle Bike Rentals
  • Task: Binary Classification (Predicting liver disease status)
  • Features: 10 clinical attributes
Setup & Installation
1. Environment Configuration
It is recommended to use a virtual environment for this project:
# Create and activate the environment
git clone git clone https://github.com/Joe-Naz01/bagging_random.git
cd bagging_random
conda create -n ensemble_ml python=3.10 -y
conda activate ensemble_ml
# Install dependencies
pip install -r requirements.txt
jupyter notebook
About
This project implements Ensemble Learning techniques to predict liver disease using the Indian Liver Patient dataset. It focuses on improving model robustness by implementing Bagging and Random Forest classifiers to aggregate the predictions of multiple decision trees.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
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0 forks
Report repository
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