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Weather Prediction Model ☀️🌧️ ⛅❄️

This is my master's project on predicting the weather using machine learning models. In this project, several models were evaluated to determine their accuracy. Each model was tested using data from a 10-day period, specifically from June 1st to June 10th, 2024.

Models Evaluated

  1. Decision Tree
  2. Random Forest
  3. Rigid Regression
  4. Linear Regression
  5. ARIMA

Python Libraries

  • numpy

  • pandas

  • scikit-learn

  • matplotlib

  • seaborn

  • matplotlib

  • datetime

  • statsmodels

Repository structure

Merging (Merging.ipynb): This contains the merging of the 24 datasets

Evalation(Evaluation.ipynb) : This contains my entire process, from pre-processing to model evaluation

Deployed(Deployed): This contains the R scripts used in deploying the model

Process Overview

1. Data Collection:

The data was gotten from Visual Crossing. It was wolverhampton, Uk data from 01-01-2000 to 16-04-2024

2. Data Merging:

The data was merged merging shows a detailed working of my process to achieve this

3. Data Cleaning

The data was carefully cleaned, and dealing with missing values. The coreect pre-processing methods were carefully carried out

4. EDA and Machine Learning Development

Data exploration adn evaluation of models were carefully carried out, the process was detailed - Evaluation

Instructions

To reproduce the results of this project, follow these steps:

Clone the repository:

git clonehttps://github.com/Uchebuzz/Weather-Prediction-Model/tree/main

Install the required libraries:

For Python:

You have to install all the Python Libraries

For R

Ensure you have R installed on your machine, and install the follow:

shiny

shinythemes

e1071

caret

zoo

About

A Weather Prediction Machine Learning model to predict weather in Wolverhampton

weatherapp.shinyapps.io/Deploy/

Resources

Readme
Activity

Stars

3 stars

Watchers

1 watching

Forks

0 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, '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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Weather Prediction Model ☀️🌧️ ⛅❄️
This is my master's project on predicting the weather using machine learning models. In this project, several models were evaluated to determine their accuracy. Each model was tested using data from a 10-day period, specifically from June 1st to June 10th, 2024.
Models Evaluated
  1. Decision Tree
  2. Random Forest
  3. Rigid Regression
  4. Linear Regression
  5. ARIMA
Python Libraries
  • numpy
  • pandas
  • scikit-learn
  • matplotlib
  • seaborn
  • matplotlib
  • datetime
  • statsmodels
Repository structure
Merging (Merging.ipynb): This contains the merging of the 24 datasets
Evalation(Evaluation.ipynb) : This contains my entire process, from pre-processing to model evaluation
Deployed(Deployed): This contains the R scripts used in deploying the model
Process Overview
1. Data Collection:
The data was gotten from Visual Crossing. It was wolverhampton, Uk data from 01-01-2000 to 16-04-2024
2. Data Merging:
The data was merged merging shows a detailed working of my process to achieve this
3. Data Cleaning
The data was carefully cleaned, and dealing with missing values. The coreect pre-processing methods were carefully carried out
4. EDA and Machine Learning Development
Data exploration adn evaluation of models were carefully carried out, the process was detailed - Evaluation
Instructions
To reproduce the results of this project, follow these steps:
Clone the repository:
git clonehttps://github.com/Uchebuzz/Weather-Prediction-Model/tree/main
Install the required libraries:
For Python:
You have to install all the Python Libraries
For R
Ensure you have R installed on your machine, and install the follow:
shiny
shinythemes
e1071
caret
zoo
About
A Weather Prediction Machine Learning model to predict weather in Wolverhampton
weatherapp.shinyapps.io/Deploy/
Resources
Readme
Activity
Stars
3 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
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Weather Prediction Model ☀️🌧️ ⛅❄️
This is my master's project on predicting the weather using machine learning models. In this project, several models were evaluated to determine their accuracy. Each model was tested using data from a 10-day period, specifically from June 1st to June 10th, 2024.
Models Evaluated
  1. Decision Tree
  2. Random Forest
  3. Rigid Regression
  4. Linear Regression
  5. ARIMA
Python Libraries
  • numpy
  • pandas
  • scikit-learn
  • matplotlib
  • seaborn
  • matplotlib
  • datetime
  • statsmodels
Repository structure
Merging (Merging.ipynb): This contains the merging of the 24 datasets
Evalation(Evaluation.ipynb) : This contains my entire process, from pre-processing to model evaluation
Deployed(Deployed): This contains the R scripts used in deploying the model
Process Overview
1. Data Collection:
The data was gotten from Visual Crossing. It was wolverhampton, Uk data from 01-01-2000 to 16-04-2024
2. Data Merging:
The data was merged merging shows a detailed working of my process to achieve this
3. Data Cleaning
The data was carefully cleaned, and dealing with missing values. The coreect pre-processing methods were carefully carried out
4. EDA and Machine Learning Development
Data exploration adn evaluation of models were carefully carried out, the process was detailed - Evaluation
Instructions
To reproduce the results of this project, follow these steps:
Clone the repository:
git clonehttps://github.com/Uchebuzz/Weather-Prediction-Model/tree/main
Install the required libraries:
For Python:
You have to install all the Python Libraries
For R
Ensure you have R installed on your machine, and install the follow:
shiny
shinythemes
e1071
caret
zoo
About
A Weather Prediction Machine Learning model to predict weather in Wolverhampton
weatherapp.shinyapps.io/Deploy/
Resources
Readme
Activity
Stars
3 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Weather Prediction Model ☀️🌧️ ⛅❄️
This is my master's project on predicting the weather using machine learning models. In this project, several models were evaluated to determine their accuracy. Each model was tested using data from a 10-day period, specifically from June 1st to June 10th, 2024.
Models Evaluated
  1. Decision Tree
  2. Random Forest
  3. Rigid Regression
  4. Linear Regression
  5. ARIMA
Python Libraries
  • numpy
  • pandas
  • scikit-learn
  • matplotlib
  • seaborn
  • matplotlib
  • datetime
  • statsmodels
Repository structure
Merging (Merging.ipynb): This contains the merging of the 24 datasets
Evalation(Evaluation.ipynb) : This contains my entire process, from pre-processing to model evaluation
Deployed(Deployed): This contains the R scripts used in deploying the model
Process Overview
1. Data Collection:
The data was gotten from Visual Crossing. It was wolverhampton, Uk data from 01-01-2000 to 16-04-2024
2. Data Merging:
The data was merged merging shows a detailed working of my process to achieve this
3. Data Cleaning
The data was carefully cleaned, and dealing with missing values. The coreect pre-processing methods were carefully carried out
4. EDA and Machine Learning Development
Data exploration adn evaluation of models were carefully carried out, the process was detailed - Evaluation
Instructions
To reproduce the results of this project, follow these steps:
Clone the repository:
git clonehttps://github.com/Uchebuzz/Weather-Prediction-Model/tree/main
Install the required libraries:
For Python:
You have to install all the Python Libraries
For R
Ensure you have R installed on your machine, and install the follow:
shiny
shinythemes
e1071
caret
zoo
About
A Weather Prediction Machine Learning model to predict weather in Wolverhampton
weatherapp.shinyapps.io/Deploy/
Resources
Readme
Activity
Stars
3 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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" + '

Skip to content
BranchesTags
Repository files navigation
Weather Prediction Model ☀️🌧️ ⛅❄️
This is my master's project on predicting the weather using machine learning models. In this project, several models were evaluated to determine their accuracy. Each model was tested using data from a 10-day period, specifically from June 1st to June 10th, 2024.
Models Evaluated
  1. Decision Tree
  2. Random Forest
  3. Rigid Regression
  4. Linear Regression
  5. ARIMA
Python Libraries
  • numpy
  • pandas
  • scikit-learn
  • matplotlib
  • seaborn
  • matplotlib
  • datetime
  • statsmodels
Repository structure
Merging (Merging.ipynb): This contains the merging of the 24 datasets
Evalation(Evaluation.ipynb) : This contains my entire process, from pre-processing to model evaluation
Deployed(Deployed): This contains the R scripts used in deploying the model
Process Overview
1. Data Collection:
The data was gotten from Visual Crossing. It was wolverhampton, Uk data from 01-01-2000 to 16-04-2024
2. Data Merging:
The data was merged merging shows a detailed working of my process to achieve this
3. Data Cleaning
The data was carefully cleaned, and dealing with missing values. The coreect pre-processing methods were carefully carried out
4. EDA and Machine Learning Development
Data exploration adn evaluation of models were carefully carried out, the process was detailed - Evaluation
Instructions
To reproduce the results of this project, follow these steps:
Clone the repository:
git clonehttps://github.com/Uchebuzz/Weather-Prediction-Model/tree/main
Install the required libraries:
For Python:
You have to install all the Python Libraries
For R
Ensure you have R installed on your machine, and install the follow:
shiny
shinythemes
e1071
caret
zoo
About
A Weather Prediction Machine Learning model to predict weather in Wolverhampton
weatherapp.shinyapps.io/Deploy/
Resources
Readme
Activity
Stars
3 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Weather Prediction Model ☀️🌧️ ⛅❄️
This is my master's project on predicting the weather using machine learning models. In this project, several models were evaluated to determine their accuracy. Each model was tested using data from a 10-day period, specifically from June 1st to June 10th, 2024.
Models Evaluated
  1. Decision Tree
  2. Random Forest
  3. Rigid Regression
  4. Linear Regression
  5. ARIMA
Python Libraries
  • numpy
  • pandas
  • scikit-learn
  • matplotlib
  • seaborn
  • matplotlib
  • datetime
  • statsmodels
Repository structure
Merging (Merging.ipynb): This contains the merging of the 24 datasets
Evalation(Evaluation.ipynb) : This contains my entire process, from pre-processing to model evaluation
Deployed(Deployed): This contains the R scripts used in deploying the model
Process Overview
1. Data Collection:
The data was gotten from Visual Crossing. It was wolverhampton, Uk data from 01-01-2000 to 16-04-2024
2. Data Merging:
The data was merged merging shows a detailed working of my process to achieve this
3. Data Cleaning
The data was carefully cleaned, and dealing with missing values. The coreect pre-processing methods were carefully carried out
4. EDA and Machine Learning Development
Data exploration adn evaluation of models were carefully carried out, the process was detailed - Evaluation
Instructions
To reproduce the results of this project, follow these steps:
Clone the repository:
git clonehttps://github.com/Uchebuzz/Weather-Prediction-Model/tree/main
Install the required libraries:
For Python:
You have to install all the Python Libraries
For R
Ensure you have R installed on your machine, and install the follow:
shiny
shinythemes
e1071
caret
zoo
About
A Weather Prediction Machine Learning model to predict weather in Wolverhampton
weatherapp.shinyapps.io/Deploy/
Resources
Readme
Activity
Stars
3 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
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Weather Prediction Model ☀️🌧️ ⛅❄️
This is my master's project on predicting the weather using machine learning models. In this project, several models were evaluated to determine their accuracy. Each model was tested using data from a 10-day period, specifically from June 1st to June 10th, 2024.
Models Evaluated
  1. Decision Tree
  2. Random Forest
  3. Rigid Regression
  4. Linear Regression
  5. ARIMA
Python Libraries
  • numpy
  • pandas
  • scikit-learn
  • matplotlib
  • seaborn
  • matplotlib
  • datetime
  • statsmodels
Repository structure
Merging (Merging.ipynb): This contains the merging of the 24 datasets
Evalation(Evaluation.ipynb) : This contains my entire process, from pre-processing to model evaluation
Deployed(Deployed): This contains the R scripts used in deploying the model
Process Overview
1. Data Collection:
The data was gotten from Visual Crossing. It was wolverhampton, Uk data from 01-01-2000 to 16-04-2024
2. Data Merging:
The data was merged merging shows a detailed working of my process to achieve this
3. Data Cleaning
The data was carefully cleaned, and dealing with missing values. The coreect pre-processing methods were carefully carried out
4. EDA and Machine Learning Development
Data exploration adn evaluation of models were carefully carried out, the process was detailed - Evaluation
Instructions
To reproduce the results of this project, follow these steps:
Clone the repository:
git clonehttps://github.com/Uchebuzz/Weather-Prediction-Model/tree/main
Install the required libraries:
For Python:
You have to install all the Python Libraries
For R
Ensure you have R installed on your machine, and install the follow:
shiny
shinythemes
e1071
caret
zoo
About
A Weather Prediction Machine Learning model to predict weather in Wolverhampton
weatherapp.shinyapps.io/Deploy/
Resources
Readme
Activity
Stars
3 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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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Weather Prediction Model ☀️🌧️ ⛅❄️
This is my master's project on predicting the weather using machine learning models. In this project, several models were evaluated to determine their accuracy. Each model was tested using data from a 10-day period, specifically from June 1st to June 10th, 2024.
Models Evaluated
  1. Decision Tree
  2. Random Forest
  3. Rigid Regression
  4. Linear Regression
  5. ARIMA
Python Libraries
  • numpy
  • pandas
  • scikit-learn
  • matplotlib
  • seaborn
  • matplotlib
  • datetime
  • statsmodels
Repository structure
Merging (Merging.ipynb): This contains the merging of the 24 datasets
Evalation(Evaluation.ipynb) : This contains my entire process, from pre-processing to model evaluation
Deployed(Deployed): This contains the R scripts used in deploying the model
Process Overview
1. Data Collection:
The data was gotten from Visual Crossing. It was wolverhampton, Uk data from 01-01-2000 to 16-04-2024
2. Data Merging:
The data was merged merging shows a detailed working of my process to achieve this
3. Data Cleaning
The data was carefully cleaned, and dealing with missing values. The coreect pre-processing methods were carefully carried out
4. EDA and Machine Learning Development
Data exploration adn evaluation of models were carefully carried out, the process was detailed - Evaluation
Instructions
To reproduce the results of this project, follow these steps:
Clone the repository:
git clonehttps://github.com/Uchebuzz/Weather-Prediction-Model/tree/main
Install the required libraries:
For Python:
You have to install all the Python Libraries
For R
Ensure you have R installed on your machine, and install the follow:
shiny
shinythemes
e1071
caret
zoo
About
A Weather Prediction Machine Learning model to predict weather in Wolverhampton
weatherapp.shinyapps.io/Deploy/
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