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InstaCart-Python

InstaCart recognizes the need to establish a targeted marketing strategy for different consumers of their app. They want to determine whether the marketing campaigns have an effect on the sale of products. I analyzed InstaCart's data to inform this marketing strategy to target customer profiles with appropriate products.

Key Questions and Objectives

● The sales team needs to know what the busiest days of the week and hours of the day are in order to schedule ads at times when there are fewer orders.

● Are there are particular times of the day when people spend the most money?

● Marketing and sales want to use simpler price range groupings to help direct their efforts.

● Are there certain types of products that are more popular than others? The marketing and sales teams want to know which departments have the highest frequency of product orders.

● The marketing and sales teams are particularly interested in the different types of customers in their system and how their ordering behaviors differ.

Note: Instacart is a real company that’s made their data available online. However, the contents of this project brief have been fabricated for the purpose of this Achievement.

Data

Data Dictionary.

InstaCart Orders & Products Datasets

Note: Fabricated data from Career Foundry is unavailable for upload because of the size limitations of GitHub

About

Career Foundry data analytics project to provide a client recommendations on a marketing strategy. Jupyter notebooks include cleaning and merging data along with creating new columns to offer the best understanding of consumers' interactions with products.

Topics

consumer-analyticsconsumer-behaviordata-analysisdata-analysis-pythondata-analyticsdata-visualizationjupyter-notebooksnumpy-librarypandas-dataframeproduct-analyticspythonseaborn-python

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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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InstaCart-Python
InstaCart recognizes the need to establish a targeted marketing strategy for different consumers of their app. They want to determine whether the marketing campaigns have an effect on the sale of products. I analyzed InstaCart's data to inform this marketing strategy to target customer profiles with appropriate products.
Key Questions and Objectives
● The sales team needs to know what the busiest days of the week and hours of the day are in order to schedule ads at times when there are fewer orders.
● Are there are particular times of the day when people spend the most money?
● Marketing and sales want to use simpler price range groupings to help direct their efforts.
● Are there certain types of products that are more popular than others? The marketing and sales teams want to know which departments have the highest frequency of product orders.
● The marketing and sales teams are particularly interested in the different types of customers in their system and how their ordering behaviors differ.
Note: Instacart is a real company that’s made their data available online. However, the contents of this project brief have been fabricated for the purpose of this Achievement.
Data
Data Dictionary.
InstaCart Orders & Products Datasets
Note: Fabricated data from Career Foundry is unavailable for upload because of the size limitations of GitHub
About
Career Foundry data analytics project to provide a client recommendations on a marketing strategy. Jupyter notebooks include cleaning and merging data along with creating new columns to offer the best understanding of consumers' interactions with products.
Topics
consumer-analyticsconsumer-behaviordata-analysisdata-analysis-pythondata-analyticsdata-visualizationjupyter-notebooksnumpy-librarypandas-dataframeproduct-analyticspythonseaborn-python
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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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InstaCart-Python
InstaCart recognizes the need to establish a targeted marketing strategy for different consumers of their app. They want to determine whether the marketing campaigns have an effect on the sale of products. I analyzed InstaCart's data to inform this marketing strategy to target customer profiles with appropriate products.
Key Questions and Objectives
● The sales team needs to know what the busiest days of the week and hours of the day are in order to schedule ads at times when there are fewer orders.
● Are there are particular times of the day when people spend the most money?
● Marketing and sales want to use simpler price range groupings to help direct their efforts.
● Are there certain types of products that are more popular than others? The marketing and sales teams want to know which departments have the highest frequency of product orders.
● The marketing and sales teams are particularly interested in the different types of customers in their system and how their ordering behaviors differ.
Note: Instacart is a real company that’s made their data available online. However, the contents of this project brief have been fabricated for the purpose of this Achievement.
Data
Data Dictionary.
InstaCart Orders & Products Datasets
Note: Fabricated data from Career Foundry is unavailable for upload because of the size limitations of GitHub
About
Career Foundry data analytics project to provide a client recommendations on a marketing strategy. Jupyter notebooks include cleaning and merging data along with creating new columns to offer the best understanding of consumers' interactions with products.
Topics
consumer-analyticsconsumer-behaviordata-analysisdata-analysis-pythondata-analyticsdata-visualizationjupyter-notebooksnumpy-librarypandas-dataframeproduct-analyticspythonseaborn-python
Resources
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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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InstaCart-Python
InstaCart recognizes the need to establish a targeted marketing strategy for different consumers of their app. They want to determine whether the marketing campaigns have an effect on the sale of products. I analyzed InstaCart's data to inform this marketing strategy to target customer profiles with appropriate products.
Key Questions and Objectives
● The sales team needs to know what the busiest days of the week and hours of the day are in order to schedule ads at times when there are fewer orders.
● Are there are particular times of the day when people spend the most money?
● Marketing and sales want to use simpler price range groupings to help direct their efforts.
● Are there certain types of products that are more popular than others? The marketing and sales teams want to know which departments have the highest frequency of product orders.
● The marketing and sales teams are particularly interested in the different types of customers in their system and how their ordering behaviors differ.
Note: Instacart is a real company that’s made their data available online. However, the contents of this project brief have been fabricated for the purpose of this Achievement.
Data
Data Dictionary.
InstaCart Orders & Products Datasets
Note: Fabricated data from Career Foundry is unavailable for upload because of the size limitations of GitHub
About
Career Foundry data analytics project to provide a client recommendations on a marketing strategy. Jupyter notebooks include cleaning and merging data along with creating new columns to offer the best understanding of consumers' interactions with products.
Topics
consumer-analyticsconsumer-behaviordata-analysisdata-analysis-pythondata-analyticsdata-visualizationjupyter-notebooksnumpy-librarypandas-dataframeproduct-analyticspythonseaborn-python
Resources
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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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InstaCart-Python
InstaCart recognizes the need to establish a targeted marketing strategy for different consumers of their app. They want to determine whether the marketing campaigns have an effect on the sale of products. I analyzed InstaCart's data to inform this marketing strategy to target customer profiles with appropriate products.
Key Questions and Objectives
● The sales team needs to know what the busiest days of the week and hours of the day are in order to schedule ads at times when there are fewer orders.
● Are there are particular times of the day when people spend the most money?
● Marketing and sales want to use simpler price range groupings to help direct their efforts.
● Are there certain types of products that are more popular than others? The marketing and sales teams want to know which departments have the highest frequency of product orders.
● The marketing and sales teams are particularly interested in the different types of customers in their system and how their ordering behaviors differ.
Note: Instacart is a real company that’s made their data available online. However, the contents of this project brief have been fabricated for the purpose of this Achievement.
Data
Data Dictionary.
InstaCart Orders & Products Datasets
Note: Fabricated data from Career Foundry is unavailable for upload because of the size limitations of GitHub
About
Career Foundry data analytics project to provide a client recommendations on a marketing strategy. Jupyter notebooks include cleaning and merging data along with creating new columns to offer the best understanding of consumers' interactions with products.
Topics
consumer-analyticsconsumer-behaviordata-analysisdata-analysis-pythondata-analyticsdata-visualizationjupyter-notebooksnumpy-librarypandas-dataframeproduct-analyticspythonseaborn-python
Resources
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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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InstaCart-Python
InstaCart recognizes the need to establish a targeted marketing strategy for different consumers of their app. They want to determine whether the marketing campaigns have an effect on the sale of products. I analyzed InstaCart's data to inform this marketing strategy to target customer profiles with appropriate products.
Key Questions and Objectives
● The sales team needs to know what the busiest days of the week and hours of the day are in order to schedule ads at times when there are fewer orders.
● Are there are particular times of the day when people spend the most money?
● Marketing and sales want to use simpler price range groupings to help direct their efforts.
● Are there certain types of products that are more popular than others? The marketing and sales teams want to know which departments have the highest frequency of product orders.
● The marketing and sales teams are particularly interested in the different types of customers in their system and how their ordering behaviors differ.
Note: Instacart is a real company that’s made their data available online. However, the contents of this project brief have been fabricated for the purpose of this Achievement.
Data
Data Dictionary.
InstaCart Orders & Products Datasets
Note: Fabricated data from Career Foundry is unavailable for upload because of the size limitations of GitHub
About
Career Foundry data analytics project to provide a client recommendations on a marketing strategy. Jupyter notebooks include cleaning and merging data along with creating new columns to offer the best understanding of consumers' interactions with products.
Topics
consumer-analyticsconsumer-behaviordata-analysisdata-analysis-pythondata-analyticsdata-visualizationjupyter-notebooksnumpy-librarypandas-dataframeproduct-analyticspythonseaborn-python
Resources
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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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InstaCart-Python
InstaCart recognizes the need to establish a targeted marketing strategy for different consumers of their app. They want to determine whether the marketing campaigns have an effect on the sale of products. I analyzed InstaCart's data to inform this marketing strategy to target customer profiles with appropriate products.
Key Questions and Objectives
● The sales team needs to know what the busiest days of the week and hours of the day are in order to schedule ads at times when there are fewer orders.
● Are there are particular times of the day when people spend the most money?
● Marketing and sales want to use simpler price range groupings to help direct their efforts.
● Are there certain types of products that are more popular than others? The marketing and sales teams want to know which departments have the highest frequency of product orders.
● The marketing and sales teams are particularly interested in the different types of customers in their system and how their ordering behaviors differ.
Note: Instacart is a real company that’s made their data available online. However, the contents of this project brief have been fabricated for the purpose of this Achievement.
Data
Data Dictionary.
InstaCart Orders & Products Datasets
Note: Fabricated data from Career Foundry is unavailable for upload because of the size limitations of GitHub
About
Career Foundry data analytics project to provide a client recommendations on a marketing strategy. Jupyter notebooks include cleaning and merging data along with creating new columns to offer the best understanding of consumers' interactions with products.
Topics
consumer-analyticsconsumer-behaviordata-analysisdata-analysis-pythondata-analyticsdata-visualizationjupyter-notebooksnumpy-librarypandas-dataframeproduct-analyticspythonseaborn-python
Resources
Readme
Activity
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1 watching
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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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InstaCart-Python
InstaCart recognizes the need to establish a targeted marketing strategy for different consumers of their app. They want to determine whether the marketing campaigns have an effect on the sale of products. I analyzed InstaCart's data to inform this marketing strategy to target customer profiles with appropriate products.
Key Questions and Objectives
● The sales team needs to know what the busiest days of the week and hours of the day are in order to schedule ads at times when there are fewer orders.
● Are there are particular times of the day when people spend the most money?
● Marketing and sales want to use simpler price range groupings to help direct their efforts.
● Are there certain types of products that are more popular than others? The marketing and sales teams want to know which departments have the highest frequency of product orders.
● The marketing and sales teams are particularly interested in the different types of customers in their system and how their ordering behaviors differ.
Note: Instacart is a real company that’s made their data available online. However, the contents of this project brief have been fabricated for the purpose of this Achievement.
Data
Data Dictionary.
InstaCart Orders & Products Datasets
Note: Fabricated data from Career Foundry is unavailable for upload because of the size limitations of GitHub
About
Career Foundry data analytics project to provide a client recommendations on a marketing strategy. Jupyter notebooks include cleaning and merging data along with creating new columns to offer the best understanding of consumers' interactions with products.
Topics
consumer-analyticsconsumer-behaviordata-analysisdata-analysis-pythondata-analyticsdata-visualizationjupyter-notebooksnumpy-librarypandas-dataframeproduct-analyticspythonseaborn-python
Resources
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