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Content: Unsupervised Learning

Project: Creating Customer Segments

Install

This project requires Python 2.7 and the following Python libraries installed:

You will also need to have software installed to run and execute a Jupyter Notebook

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.

Code

Template code is provided in the customer_segments.ipynb notebook file. You will also be required to use the included visuals.py Python file and the customers.csv dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. Note that the code included in visuals.py is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.

Run

In a terminal or command window, navigate to the top-level project directory customer_segments/ (that contains this README) and run one of the following commands:

ipython notebook customer_segments.ipynb

or

jupyter notebook customer_segments.ipynb

This will open the Jupyter Notebook software and project file in your browser.

Data

The customer segments data is included as a selection of 440 data points collected on data found from clients of a wholesale distributor in Lisbon, Portugal. More information can be found on the UCI Machine Learning Repository.

Note (m.u.) is shorthand for monetary units.

Features

  1. Fresh: annual spending (m.u.) on fresh products (Continuous);
  2. Milk: annual spending (m.u.) on milk products (Continuous);
  3. Grocery: annual spending (m.u.) on grocery products (Continuous);
  4. Frozen: annual spending (m.u.) on frozen products (Continuous);
  5. Detergents_Paper: annual spending (m.u.) on detergents and paper products (Continuous);
  6. Delicatessen: annual spending (m.u.) on and delicatessen products (Continuous);
  7. Channel: {Hotel/Restaurant/Cafe - 1, Retail - 2} (Nominal)
  8. Region: {Lisbon - 1, Oporto - 2, or Other - 3} (Nominal)

About

Customer Segmentation project

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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" + '

Skip to content
BranchesTags
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Content: Unsupervised Learning
Project: Creating Customer Segments
Install
This project requires Python 2.7 and the following Python libraries installed:
You will also need to have software installed to run and execute a Jupyter Notebook
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.
Code
Template code is provided in the customer_segments.ipynb notebook file. You will also be required to use the included visuals.py Python file and the customers.csv dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. Note that the code included in visuals.py is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.
Run
In a terminal or command window, navigate to the top-level project directory customer_segments/ (that contains this README) and run one of the following commands:
ipython notebook customer_segments.ipynb
or
jupyter notebook customer_segments.ipynb
This will open the Jupyter Notebook software and project file in your browser.
Data
The customer segments data is included as a selection of 440 data points collected on data found from clients of a wholesale distributor in Lisbon, Portugal. More information can be found on the UCI Machine Learning Repository.
Note (m.u.) is shorthand for monetary units.
Features
  1. Fresh: annual spending (m.u.) on fresh products (Continuous);
  2. Milk: annual spending (m.u.) on milk products (Continuous);
  3. Grocery: annual spending (m.u.) on grocery products (Continuous);
  4. Frozen: annual spending (m.u.) on frozen products (Continuous);
  5. Detergents_Paper: annual spending (m.u.) on detergents and paper products (Continuous);
  6. Delicatessen: annual spending (m.u.) on and delicatessen products (Continuous);
  7. Channel: {Hotel/Restaurant/Cafe - 1, Retail - 2} (Nominal)
  8. Region: {Lisbon - 1, Oporto - 2, or Other - 3} (Nominal)
About
Customer Segmentation project
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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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('^' + ".*" + '

Skip to content
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Content: Unsupervised Learning
Project: Creating Customer Segments
Install
This project requires Python 2.7 and the following Python libraries installed:
You will also need to have software installed to run and execute a Jupyter Notebook
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.
Code
Template code is provided in the customer_segments.ipynb notebook file. You will also be required to use the included visuals.py Python file and the customers.csv dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. Note that the code included in visuals.py is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.
Run
In a terminal or command window, navigate to the top-level project directory customer_segments/ (that contains this README) and run one of the following commands:
ipython notebook customer_segments.ipynb
or
jupyter notebook customer_segments.ipynb
This will open the Jupyter Notebook software and project file in your browser.
Data
The customer segments data is included as a selection of 440 data points collected on data found from clients of a wholesale distributor in Lisbon, Portugal. More information can be found on the UCI Machine Learning Repository.
Note (m.u.) is shorthand for monetary units.
Features
  1. Fresh: annual spending (m.u.) on fresh products (Continuous);
  2. Milk: annual spending (m.u.) on milk products (Continuous);
  3. Grocery: annual spending (m.u.) on grocery products (Continuous);
  4. Frozen: annual spending (m.u.) on frozen products (Continuous);
  5. Detergents_Paper: annual spending (m.u.) on detergents and paper products (Continuous);
  6. Delicatessen: annual spending (m.u.) on and delicatessen products (Continuous);
  7. Channel: {Hotel/Restaurant/Cafe - 1, Retail - 2} (Nominal)
  8. Region: {Lisbon - 1, Oporto - 2, or Other - 3} (Nominal)
About
Customer Segmentation project
Resources
Readme
Activity
Stars
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Watchers
0 watching
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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
BranchesTags
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Content: Unsupervised Learning
Project: Creating Customer Segments
Install
This project requires Python 2.7 and the following Python libraries installed:
You will also need to have software installed to run and execute a Jupyter Notebook
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.
Code
Template code is provided in the customer_segments.ipynb notebook file. You will also be required to use the included visuals.py Python file and the customers.csv dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. Note that the code included in visuals.py is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.
Run
In a terminal or command window, navigate to the top-level project directory customer_segments/ (that contains this README) and run one of the following commands:
ipython notebook customer_segments.ipynb
or
jupyter notebook customer_segments.ipynb
This will open the Jupyter Notebook software and project file in your browser.
Data
The customer segments data is included as a selection of 440 data points collected on data found from clients of a wholesale distributor in Lisbon, Portugal. More information can be found on the UCI Machine Learning Repository.
Note (m.u.) is shorthand for monetary units.
Features
  1. Fresh: annual spending (m.u.) on fresh products (Continuous);
  2. Milk: annual spending (m.u.) on milk products (Continuous);
  3. Grocery: annual spending (m.u.) on grocery products (Continuous);
  4. Frozen: annual spending (m.u.) on frozen products (Continuous);
  5. Detergents_Paper: annual spending (m.u.) on detergents and paper products (Continuous);
  6. Delicatessen: annual spending (m.u.) on and delicatessen products (Continuous);
  7. Channel: {Hotel/Restaurant/Cafe - 1, Retail - 2} (Nominal)
  8. Region: {Lisbon - 1, Oporto - 2, or Other - 3} (Nominal)
About
Customer Segmentation project
Resources
Readme
Activity
Stars
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Watchers
0 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" + '

Skip to content
BranchesTags
Repository files navigation
Content: Unsupervised Learning
Project: Creating Customer Segments
Install
This project requires Python 2.7 and the following Python libraries installed:
You will also need to have software installed to run and execute a Jupyter Notebook
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.
Code
Template code is provided in the customer_segments.ipynb notebook file. You will also be required to use the included visuals.py Python file and the customers.csv dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. Note that the code included in visuals.py is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.
Run
In a terminal or command window, navigate to the top-level project directory customer_segments/ (that contains this README) and run one of the following commands:
ipython notebook customer_segments.ipynb
or
jupyter notebook customer_segments.ipynb
This will open the Jupyter Notebook software and project file in your browser.
Data
The customer segments data is included as a selection of 440 data points collected on data found from clients of a wholesale distributor in Lisbon, Portugal. More information can be found on the UCI Machine Learning Repository.
Note (m.u.) is shorthand for monetary units.
Features
  1. Fresh: annual spending (m.u.) on fresh products (Continuous);
  2. Milk: annual spending (m.u.) on milk products (Continuous);
  3. Grocery: annual spending (m.u.) on grocery products (Continuous);
  4. Frozen: annual spending (m.u.) on frozen products (Continuous);
  5. Detergents_Paper: annual spending (m.u.) on detergents and paper products (Continuous);
  6. Delicatessen: annual spending (m.u.) on and delicatessen products (Continuous);
  7. Channel: {Hotel/Restaurant/Cafe - 1, Retail - 2} (Nominal)
  8. Region: {Lisbon - 1, Oporto - 2, or Other - 3} (Nominal)
About
Customer Segmentation project
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Content: Unsupervised Learning
Project: Creating Customer Segments
Install
This project requires Python 2.7 and the following Python libraries installed:
You will also need to have software installed to run and execute a Jupyter Notebook
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.
Code
Template code is provided in the customer_segments.ipynb notebook file. You will also be required to use the included visuals.py Python file and the customers.csv dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. Note that the code included in visuals.py is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.
Run
In a terminal or command window, navigate to the top-level project directory customer_segments/ (that contains this README) and run one of the following commands:
ipython notebook customer_segments.ipynb
or
jupyter notebook customer_segments.ipynb
This will open the Jupyter Notebook software and project file in your browser.
Data
The customer segments data is included as a selection of 440 data points collected on data found from clients of a wholesale distributor in Lisbon, Portugal. More information can be found on the UCI Machine Learning Repository.
Note (m.u.) is shorthand for monetary units.
Features
  1. Fresh: annual spending (m.u.) on fresh products (Continuous);
  2. Milk: annual spending (m.u.) on milk products (Continuous);
  3. Grocery: annual spending (m.u.) on grocery products (Continuous);
  4. Frozen: annual spending (m.u.) on frozen products (Continuous);
  5. Detergents_Paper: annual spending (m.u.) on detergents and paper products (Continuous);
  6. Delicatessen: annual spending (m.u.) on and delicatessen products (Continuous);
  7. Channel: {Hotel/Restaurant/Cafe - 1, Retail - 2} (Nominal)
  8. Region: {Lisbon - 1, Oporto - 2, or Other - 3} (Nominal)
About
Customer Segmentation project
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("// 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
Repository files navigation
Content: Unsupervised Learning
Project: Creating Customer Segments
Install
This project requires Python 2.7 and the following Python libraries installed:
You will also need to have software installed to run and execute a Jupyter Notebook
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.
Code
Template code is provided in the customer_segments.ipynb notebook file. You will also be required to use the included visuals.py Python file and the customers.csv dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. Note that the code included in visuals.py is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.
Run
In a terminal or command window, navigate to the top-level project directory customer_segments/ (that contains this README) and run one of the following commands:
ipython notebook customer_segments.ipynb
or
jupyter notebook customer_segments.ipynb
This will open the Jupyter Notebook software and project file in your browser.
Data
The customer segments data is included as a selection of 440 data points collected on data found from clients of a wholesale distributor in Lisbon, Portugal. More information can be found on the UCI Machine Learning Repository.
Note (m.u.) is shorthand for monetary units.
Features
  1. Fresh: annual spending (m.u.) on fresh products (Continuous);
  2. Milk: annual spending (m.u.) on milk products (Continuous);
  3. Grocery: annual spending (m.u.) on grocery products (Continuous);
  4. Frozen: annual spending (m.u.) on frozen products (Continuous);
  5. Detergents_Paper: annual spending (m.u.) on detergents and paper products (Continuous);
  6. Delicatessen: annual spending (m.u.) on and delicatessen products (Continuous);
  7. Channel: {Hotel/Restaurant/Cafe - 1, Retail - 2} (Nominal)
  8. Region: {Lisbon - 1, Oporto - 2, or Other - 3} (Nominal)
About
Customer Segmentation project
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)) {
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Content: Unsupervised Learning
Project: Creating Customer Segments
Install
This project requires Python 2.7 and the following Python libraries installed:
You will also need to have software installed to run and execute a Jupyter Notebook
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.
Code
Template code is provided in the customer_segments.ipynb notebook file. You will also be required to use the included visuals.py Python file and the customers.csv dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. Note that the code included in visuals.py is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.
Run
In a terminal or command window, navigate to the top-level project directory customer_segments/ (that contains this README) and run one of the following commands:
ipython notebook customer_segments.ipynb
or
jupyter notebook customer_segments.ipynb
This will open the Jupyter Notebook software and project file in your browser.
Data
The customer segments data is included as a selection of 440 data points collected on data found from clients of a wholesale distributor in Lisbon, Portugal. More information can be found on the UCI Machine Learning Repository.
Note (m.u.) is shorthand for monetary units.
Features
  1. Fresh: annual spending (m.u.) on fresh products (Continuous);
  2. Milk: annual spending (m.u.) on milk products (Continuous);
  3. Grocery: annual spending (m.u.) on grocery products (Continuous);
  4. Frozen: annual spending (m.u.) on frozen products (Continuous);
  5. Detergents_Paper: annual spending (m.u.) on detergents and paper products (Continuous);
  6. Delicatessen: annual spending (m.u.) on and delicatessen products (Continuous);
  7. Channel: {Hotel/Restaurant/Cafe - 1, Retail - 2} (Nominal)
  8. Region: {Lisbon - 1, Oporto - 2, or Other - 3} (Nominal)
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Customer Segmentation project
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