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UI v1.0
Readme.md
Readme.md
Team.pptx
Team.pptx
Video Tutorial.md
Video Tutorial.md

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Anomaly Detection Program using LOF Algorithm

This program is a web-based application that uses the Local Outlier Factor (LOF) algorithm to detect anomalies in a given dataset. The frontend of the application is built using React and the backend is built using Python and Flask.

How to Run Frontend

To run the frontend part of the application, navigate to the UI folder in your terminal and run the following commands: bash

npm install
npm start

This will start the frontend server on http://localhost:3000.

Backend

To run the backend part of the application, navigate to the API folder in your terminal and run the following commands to install the required packages: bash

pip install -r requirements.txt

After the packages are installed, run the Main.py file using the following command: bash

python Main.py

This will start the backend server on http://localhost:5000.

How to Use

  1. Go to http://localhost:3000 from your browser.
  2. Click on the Choose File button to select the data file you want to use to train the model. A sample data file is provided in the API folder.
  3. Click on the Train button to train the model. After the model is trained successfully, you will get an alert.
  4. Click on the Choose File button again toselect the data file you want to detect anomalies in.
  5. Click on the Check button to detect anomalies in the selected dataset. A CSV file containing the anomalies will be downloaded automatically.
  6. In the downloaded CSV file, -1 represents an anomaly and 1 represents a non-anomaly.

Note: The application encodes non-numeric values to numeric types. Any null values in the data are removed before processing. The program may crash for files that are not in the UTF-8 encoding. Please note that LOF is a density-based algorithm and may not work well with datasets that have widely varying density. In the upcoming prototype, LOF will be replaced by Principle Component Analysis (PCA), and an inbuilt null value handling function will be introduced.

About API 3.0 (beta release)

In this API api we tried to overcome the limitations of API 2.2 which was that was not considering anomalies caused by multiple columns for example: if a user usually logins from a country suddenly he changes his country than that would also be considered as an anomaly. Some featers of the API 2.2 was depricated to make it stable it currently does'nt have feature to train and detect anomalies from the two different dataset. It take data from a dataset and straight away prints anomalus values from that.

About

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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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Readme.md
Readme.md
Team.pptx
Team.pptx
Video Tutorial.md
Video Tutorial.md
Repository files navigation
Anomaly Detection Program using LOF Algorithm
This program is a web-based application that uses the Local Outlier Factor (LOF) algorithm to detect anomalies in a given dataset. The frontend of the application is built using React and the backend is built using Python and Flask.
How to Run Frontend
To run the frontend part of the application, navigate to the UI folder in your terminal and run the following commands:
bash
npm install
npm start

This will start the frontend server on http://localhost:3000.
Backend
To run the backend part of the application, navigate to the API folder in your terminal and run the following commands to install the required packages:
bash
pip install -r requirements.txt

After the packages are installed, run the Main.py file using the following command:
bash
python Main.py

This will start the backend server on http://localhost:5000.
How to Use
  1. Go to http://localhost:3000 from your browser.
  2. Click on the Choose File button to select the data file you want to use to train the model. A sample data file is provided in the API folder.
  3. Click on the Train button to train the model. After the model is trained successfully, you will get an alert.
  4. Click on the Choose File button again toselect the data file you want to detect anomalies in.
  5. Click on the Check button to detect anomalies in the selected dataset. A CSV file containing the anomalies will be downloaded automatically.
  6. In the downloaded CSV file, -1 represents an anomaly and 1 represents a non-anomaly.
Note: The application encodes non-numeric values to numeric types. Any null values in the data are removed before processing. The program may crash for files that are not in the UTF-8 encoding. Please note that LOF is a density-based algorithm and may not work well with datasets that have widely varying density. In the upcoming prototype, LOF will be replaced by Principle Component Analysis (PCA), and an inbuilt null value handling function will be introduced.
About API 3.0 (beta release)
In this API api we tried to overcome the limitations of API 2.2 which was that was not considering anomalies caused by multiple columns for example: if a user usually logins from a country suddenly he changes his country than that would also be considered as an anomaly. Some featers of the API 2.2 was depricated to make it stable it currently does'nt have feature to train and detect anomalies from the two different dataset. It take data from a dataset and straight away prints anomalus values from that.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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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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Readme.md
Readme.md
Team.pptx
Team.pptx
Video Tutorial.md
Video Tutorial.md
Repository files navigation
Anomaly Detection Program using LOF Algorithm
This program is a web-based application that uses the Local Outlier Factor (LOF) algorithm to detect anomalies in a given dataset. The frontend of the application is built using React and the backend is built using Python and Flask.
How to Run Frontend
To run the frontend part of the application, navigate to the UI folder in your terminal and run the following commands:
bash
npm install
npm start

This will start the frontend server on http://localhost:3000.
Backend
To run the backend part of the application, navigate to the API folder in your terminal and run the following commands to install the required packages:
bash
pip install -r requirements.txt

After the packages are installed, run the Main.py file using the following command:
bash
python Main.py

This will start the backend server on http://localhost:5000.
How to Use
  1. Go to http://localhost:3000 from your browser.
  2. Click on the Choose File button to select the data file you want to use to train the model. A sample data file is provided in the API folder.
  3. Click on the Train button to train the model. After the model is trained successfully, you will get an alert.
  4. Click on the Choose File button again toselect the data file you want to detect anomalies in.
  5. Click on the Check button to detect anomalies in the selected dataset. A CSV file containing the anomalies will be downloaded automatically.
  6. In the downloaded CSV file, -1 represents an anomaly and 1 represents a non-anomaly.
Note: The application encodes non-numeric values to numeric types. Any null values in the data are removed before processing. The program may crash for files that are not in the UTF-8 encoding. Please note that LOF is a density-based algorithm and may not work well with datasets that have widely varying density. In the upcoming prototype, LOF will be replaced by Principle Component Analysis (PCA), and an inbuilt null value handling function will be introduced.
About API 3.0 (beta release)
In this API api we tried to overcome the limitations of API 2.2 which was that was not considering anomalies caused by multiple columns for example: if a user usually logins from a country suddenly he changes his country than that would also be considered as an anomaly. Some featers of the API 2.2 was depricated to make it stable it currently does'nt have feature to train and detect anomalies from the two different dataset. It take data from a dataset and straight away prints anomalus values from that.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 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('^' + ".*" + '

Skip to content
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API v2.2
API v3.0 (Beta)
API v3.0 (Beta)
UI v1.0
UI v1.0
Readme.md
Readme.md
Team.pptx
Team.pptx
Video Tutorial.md
Video Tutorial.md
Repository files navigation
Anomaly Detection Program using LOF Algorithm
This program is a web-based application that uses the Local Outlier Factor (LOF) algorithm to detect anomalies in a given dataset. The frontend of the application is built using React and the backend is built using Python and Flask.
How to Run Frontend
To run the frontend part of the application, navigate to the UI folder in your terminal and run the following commands:
bash
npm install
npm start

This will start the frontend server on http://localhost:3000.
Backend
To run the backend part of the application, navigate to the API folder in your terminal and run the following commands to install the required packages:
bash
pip install -r requirements.txt

After the packages are installed, run the Main.py file using the following command:
bash
python Main.py

This will start the backend server on http://localhost:5000.
How to Use
  1. Go to http://localhost:3000 from your browser.
  2. Click on the Choose File button to select the data file you want to use to train the model. A sample data file is provided in the API folder.
  3. Click on the Train button to train the model. After the model is trained successfully, you will get an alert.
  4. Click on the Choose File button again toselect the data file you want to detect anomalies in.
  5. Click on the Check button to detect anomalies in the selected dataset. A CSV file containing the anomalies will be downloaded automatically.
  6. In the downloaded CSV file, -1 represents an anomaly and 1 represents a non-anomaly.
Note: The application encodes non-numeric values to numeric types. Any null values in the data are removed before processing. The program may crash for files that are not in the UTF-8 encoding. Please note that LOF is a density-based algorithm and may not work well with datasets that have widely varying density. In the upcoming prototype, LOF will be replaced by Principle Component Analysis (PCA), and an inbuilt null value handling function will be introduced.
About API 3.0 (beta release)
In this API api we tried to overcome the limitations of API 2.2 which was that was not considering anomalies caused by multiple columns for example: if a user usually logins from a country suddenly he changes his country than that would also be considered as an anomaly. Some featers of the API 2.2 was depricated to make it stable it currently does'nt have feature to train and detect anomalies from the two different dataset. It take data from a dataset and straight away prints anomalus values from that.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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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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API v2.2
API v3.0 (Beta)
API v3.0 (Beta)
UI v1.0
UI v1.0
Readme.md
Readme.md
Team.pptx
Team.pptx
Video Tutorial.md
Video Tutorial.md
Repository files navigation
Anomaly Detection Program using LOF Algorithm
This program is a web-based application that uses the Local Outlier Factor (LOF) algorithm to detect anomalies in a given dataset. The frontend of the application is built using React and the backend is built using Python and Flask.
How to Run Frontend
To run the frontend part of the application, navigate to the UI folder in your terminal and run the following commands:
bash
npm install
npm start

This will start the frontend server on http://localhost:3000.
Backend
To run the backend part of the application, navigate to the API folder in your terminal and run the following commands to install the required packages:
bash
pip install -r requirements.txt

After the packages are installed, run the Main.py file using the following command:
bash
python Main.py

This will start the backend server on http://localhost:5000.
How to Use
  1. Go to http://localhost:3000 from your browser.
  2. Click on the Choose File button to select the data file you want to use to train the model. A sample data file is provided in the API folder.
  3. Click on the Train button to train the model. After the model is trained successfully, you will get an alert.
  4. Click on the Choose File button again toselect the data file you want to detect anomalies in.
  5. Click on the Check button to detect anomalies in the selected dataset. A CSV file containing the anomalies will be downloaded automatically.
  6. In the downloaded CSV file, -1 represents an anomaly and 1 represents a non-anomaly.
Note: The application encodes non-numeric values to numeric types. Any null values in the data are removed before processing. The program may crash for files that are not in the UTF-8 encoding. Please note that LOF is a density-based algorithm and may not work well with datasets that have widely varying density. In the upcoming prototype, LOF will be replaced by Principle Component Analysis (PCA), and an inbuilt null value handling function will be introduced.
About API 3.0 (beta release)
In this API api we tried to overcome the limitations of API 2.2 which was that was not considering anomalies caused by multiple columns for example: if a user usually logins from a country suddenly he changes his country than that would also be considered as an anomaly. Some featers of the API 2.2 was depricated to make it stable it currently does'nt have feature to train and detect anomalies from the two different dataset. It take data from a dataset and straight away prints anomalus values from that.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
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Watchers
1 watching
Forks
0 forks
Report repository
Releases
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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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NameName
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API v2.2
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API v3.0 (Beta)
UI v1.0
UI v1.0
Readme.md
Readme.md
Team.pptx
Team.pptx
Video Tutorial.md
Video Tutorial.md
Repository files navigation
Anomaly Detection Program using LOF Algorithm
This program is a web-based application that uses the Local Outlier Factor (LOF) algorithm to detect anomalies in a given dataset. The frontend of the application is built using React and the backend is built using Python and Flask.
How to Run Frontend
To run the frontend part of the application, navigate to the UI folder in your terminal and run the following commands:
bash
npm install
npm start

This will start the frontend server on http://localhost:3000.
Backend
To run the backend part of the application, navigate to the API folder in your terminal and run the following commands to install the required packages:
bash
pip install -r requirements.txt

After the packages are installed, run the Main.py file using the following command:
bash
python Main.py

This will start the backend server on http://localhost:5000.
How to Use
  1. Go to http://localhost:3000 from your browser.
  2. Click on the Choose File button to select the data file you want to use to train the model. A sample data file is provided in the API folder.
  3. Click on the Train button to train the model. After the model is trained successfully, you will get an alert.
  4. Click on the Choose File button again toselect the data file you want to detect anomalies in.
  5. Click on the Check button to detect anomalies in the selected dataset. A CSV file containing the anomalies will be downloaded automatically.
  6. In the downloaded CSV file, -1 represents an anomaly and 1 represents a non-anomaly.
Note: The application encodes non-numeric values to numeric types. Any null values in the data are removed before processing. The program may crash for files that are not in the UTF-8 encoding. Please note that LOF is a density-based algorithm and may not work well with datasets that have widely varying density. In the upcoming prototype, LOF will be replaced by Principle Component Analysis (PCA), and an inbuilt null value handling function will be introduced.
About API 3.0 (beta release)
In this API api we tried to overcome the limitations of API 2.2 which was that was not considering anomalies caused by multiple columns for example: if a user usually logins from a country suddenly he changes his country than that would also be considered as an anomaly. Some featers of the API 2.2 was depricated to make it stable it currently does'nt have feature to train and detect anomalies from the two different dataset. It take data from a dataset and straight away prints anomalus values from that.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 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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NameName
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API v2.2
API v2.2
API v3.0 (Beta)
API v3.0 (Beta)
UI v1.0
UI v1.0
Readme.md
Readme.md
Team.pptx
Team.pptx
Video Tutorial.md
Video Tutorial.md
Repository files navigation
Anomaly Detection Program using LOF Algorithm
This program is a web-based application that uses the Local Outlier Factor (LOF) algorithm to detect anomalies in a given dataset. The frontend of the application is built using React and the backend is built using Python and Flask.
How to Run Frontend
To run the frontend part of the application, navigate to the UI folder in your terminal and run the following commands:
bash
npm install
npm start

This will start the frontend server on http://localhost:3000.
Backend
To run the backend part of the application, navigate to the API folder in your terminal and run the following commands to install the required packages:
bash
pip install -r requirements.txt

After the packages are installed, run the Main.py file using the following command:
bash
python Main.py

This will start the backend server on http://localhost:5000.
How to Use
  1. Go to http://localhost:3000 from your browser.
  2. Click on the Choose File button to select the data file you want to use to train the model. A sample data file is provided in the API folder.
  3. Click on the Train button to train the model. After the model is trained successfully, you will get an alert.
  4. Click on the Choose File button again toselect the data file you want to detect anomalies in.
  5. Click on the Check button to detect anomalies in the selected dataset. A CSV file containing the anomalies will be downloaded automatically.
  6. In the downloaded CSV file, -1 represents an anomaly and 1 represents a non-anomaly.
Note: The application encodes non-numeric values to numeric types. Any null values in the data are removed before processing. The program may crash for files that are not in the UTF-8 encoding. Please note that LOF is a density-based algorithm and may not work well with datasets that have widely varying density. In the upcoming prototype, LOF will be replaced by Principle Component Analysis (PCA), and an inbuilt null value handling function will be introduced.
About API 3.0 (beta release)
In this API api we tried to overcome the limitations of API 2.2 which was that was not considering anomalies caused by multiple columns for example: if a user usually logins from a country suddenly he changes his country than that would also be considered as an anomaly. Some featers of the API 2.2 was depricated to make it stable it currently does'nt have feature to train and detect anomalies from the two different dataset. It take data from a dataset and straight away prints anomalus values from that.
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Anomaly Detection Program using LOF Algorithm
This program is a web-based application that uses the Local Outlier Factor (LOF) algorithm to detect anomalies in a given dataset. The frontend of the application is built using React and the backend is built using Python and Flask.
How to Run Frontend
To run the frontend part of the application, navigate to the UI folder in your terminal and run the following commands:
bash
npm install
npm start

This will start the frontend server on http://localhost:3000.
Backend
To run the backend part of the application, navigate to the API folder in your terminal and run the following commands to install the required packages:
bash
pip install -r requirements.txt

After the packages are installed, run the Main.py file using the following command:
bash
python Main.py

This will start the backend server on http://localhost:5000.
How to Use
  1. Go to http://localhost:3000 from your browser.
  2. Click on the Choose File button to select the data file you want to use to train the model. A sample data file is provided in the API folder.
  3. Click on the Train button to train the model. After the model is trained successfully, you will get an alert.
  4. Click on the Choose File button again toselect the data file you want to detect anomalies in.
  5. Click on the Check button to detect anomalies in the selected dataset. A CSV file containing the anomalies will be downloaded automatically.
  6. In the downloaded CSV file, -1 represents an anomaly and 1 represents a non-anomaly.
Note: The application encodes non-numeric values to numeric types. Any null values in the data are removed before processing. The program may crash for files that are not in the UTF-8 encoding. Please note that LOF is a density-based algorithm and may not work well with datasets that have widely varying density. In the upcoming prototype, LOF will be replaced by Principle Component Analysis (PCA), and an inbuilt null value handling function will be introduced.
About API 3.0 (beta release)
In this API api we tried to overcome the limitations of API 2.2 which was that was not considering anomalies caused by multiple columns for example: if a user usually logins from a country suddenly he changes his country than that would also be considered as an anomaly. Some featers of the API 2.2 was depricated to make it stable it currently does'nt have feature to train and detect anomalies from the two different dataset. It take data from a dataset and straight away prints anomalus values from that.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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