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Diwali Sales Data Analysis

A data analysis project exploring customer behavior during Diwali shopping using Python and data visualization tools. The project aims to uncover insights from customer demographics, location-based trends, spending habits, and product preferences.

Dataset Information

Dataset Name: Diwali Sales Data

Key Columns:

  • Gender: Male/Female customers
  • Age Group: Customer age distribution
  • State: Location of customers
  • Marital Status: Married/Unmarried customers
  • Occupation: Buyer professions
  • Product Category: Type of products purchased
  • Amount: Total amount spent

Tools & Technologies Used

CategoryTools
Programming LanguagePython
Libraries UsedPandas, NumPy, Matplotlib, Seaborn
Visualization ToolsSeaborn, Matplotlib
PlatformGoogle Colab / Jupyter Notebook

Key Findings from the Analysis

Customer Demographics

  • Majority of buyers are aged 26-35 years
  • Women contribute significantly to high-value purchases

Location-Based Sales

  • Highest number of orders come from:
    • Uttar Pradesh
    • Maharashtra
    • Karnataka

Spending Behavior

  • Unmarried women tend to spend more on shopping
  • Top buying professions: IT, Healthcare, and Aviation

Product Preferences

  • Most sold product categories:
    • Clothing & Apparel
    • Food
    • Electronics & Gadgets

Project Workflow

Data Preprocessing

  • Handled missing values
  • Converted data types
  • Created new features (feature engineering)

Exploratory Data Analysis (EDA)

  • Analyzed customer demographics
  • Identified spending patterns and top buyer segments

Visualization

  • Used Seaborn and Matplotlib
  • Created insightful plots: bar charts, count plots, and heatmaps

Conclusions & Business Insights

  • Derived actionable insights to assist business decision-making during festive sales

Conclusion

This project highlights how Diwali shopping behavior varies across demographics, locations, and product preferences. The analysis can help businesses optimize marketing campaigns, product inventory, and target audiences more effectively during festive seasons.

Acknowledgements

  • Dataset used for educational and analytical purposes
  • Inspired by real-world retail analytics scenarios

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

0 stars

Watchers

1 watching

Forks

0 forks
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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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Diwali Sales Data Analysis
A data analysis project exploring customer behavior during Diwali shopping using Python and data visualization tools. The project aims to uncover insights from customer demographics, location-based trends, spending habits, and product preferences.
Dataset Information
Dataset Name: Diwali Sales Data
Key Columns:
  • Gender: Male/Female customers
  • Age Group: Customer age distribution
  • State: Location of customers
  • Marital Status: Married/Unmarried customers
  • Occupation: Buyer professions
  • Product Category: Type of products purchased
  • Amount: Total amount spent
Tools & Technologies Used
CategoryTools
Programming LanguagePython
Libraries UsedPandas, NumPy, Matplotlib, Seaborn
Visualization ToolsSeaborn, Matplotlib
PlatformGoogle Colab / Jupyter Notebook
Key Findings from the Analysis
Customer Demographics
  • Majority of buyers are aged 26-35 years
  • Women contribute significantly to high-value purchases
Location-Based Sales
  • Highest number of orders come from:

    • Uttar Pradesh
    • Maharashtra
    • Karnataka
Spending Behavior
  • Unmarried women tend to spend more on shopping
  • Top buying professions: IT, Healthcare, and Aviation
Product Preferences
  • Most sold product categories:

    • Clothing & Apparel
    • Food
    • Electronics & Gadgets
Project Workflow
Data Preprocessing
  • Handled missing values
  • Converted data types
  • Created new features (feature engineering)
Exploratory Data Analysis (EDA)
  • Analyzed customer demographics
  • Identified spending patterns and top buyer segments
Visualization
  • Used Seaborn and Matplotlib
  • Created insightful plots: bar charts, count plots, and heatmaps
Conclusions & Business Insights
  • Derived actionable insights to assist business decision-making during festive sales
Conclusion
This project highlights how Diwali shopping behavior varies across demographics, locations, and product preferences. The analysis can help businesses optimize marketing campaigns, product inventory, and target audiences more effectively during festive seasons.
Acknowledgements
  • Dataset used for educational and analytical purposes
  • Inspired by real-world retail analytics scenarios
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
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0 forks
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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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Repository files navigation
Diwali Sales Data Analysis
A data analysis project exploring customer behavior during Diwali shopping using Python and data visualization tools. The project aims to uncover insights from customer demographics, location-based trends, spending habits, and product preferences.
Dataset Information
Dataset Name: Diwali Sales Data
Key Columns:
  • Gender: Male/Female customers
  • Age Group: Customer age distribution
  • State: Location of customers
  • Marital Status: Married/Unmarried customers
  • Occupation: Buyer professions
  • Product Category: Type of products purchased
  • Amount: Total amount spent
Tools & Technologies Used
CategoryTools
Programming LanguagePython
Libraries UsedPandas, NumPy, Matplotlib, Seaborn
Visualization ToolsSeaborn, Matplotlib
PlatformGoogle Colab / Jupyter Notebook
Key Findings from the Analysis
Customer Demographics
  • Majority of buyers are aged 26-35 years
  • Women contribute significantly to high-value purchases
Location-Based Sales
  • Highest number of orders come from:

    • Uttar Pradesh
    • Maharashtra
    • Karnataka
Spending Behavior
  • Unmarried women tend to spend more on shopping
  • Top buying professions: IT, Healthcare, and Aviation
Product Preferences
  • Most sold product categories:

    • Clothing & Apparel
    • Food
    • Electronics & Gadgets
Project Workflow
Data Preprocessing
  • Handled missing values
  • Converted data types
  • Created new features (feature engineering)
Exploratory Data Analysis (EDA)
  • Analyzed customer demographics
  • Identified spending patterns and top buyer segments
Visualization
  • Used Seaborn and Matplotlib
  • Created insightful plots: bar charts, count plots, and heatmaps
Conclusions & Business Insights
  • Derived actionable insights to assist business decision-making during festive sales
Conclusion
This project highlights how Diwali shopping behavior varies across demographics, locations, and product preferences. The analysis can help businesses optimize marketing campaigns, product inventory, and target audiences more effectively during festive seasons.
Acknowledgements
  • Dataset used for educational and analytical purposes
  • Inspired by real-world retail analytics scenarios
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
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Watchers
1 watching
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0 forks
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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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Repository files navigation
Diwali Sales Data Analysis
A data analysis project exploring customer behavior during Diwali shopping using Python and data visualization tools. The project aims to uncover insights from customer demographics, location-based trends, spending habits, and product preferences.
Dataset Information
Dataset Name: Diwali Sales Data
Key Columns:
  • Gender: Male/Female customers
  • Age Group: Customer age distribution
  • State: Location of customers
  • Marital Status: Married/Unmarried customers
  • Occupation: Buyer professions
  • Product Category: Type of products purchased
  • Amount: Total amount spent
Tools & Technologies Used
CategoryTools
Programming LanguagePython
Libraries UsedPandas, NumPy, Matplotlib, Seaborn
Visualization ToolsSeaborn, Matplotlib
PlatformGoogle Colab / Jupyter Notebook
Key Findings from the Analysis
Customer Demographics
  • Majority of buyers are aged 26-35 years
  • Women contribute significantly to high-value purchases
Location-Based Sales
  • Highest number of orders come from:

    • Uttar Pradesh
    • Maharashtra
    • Karnataka
Spending Behavior
  • Unmarried women tend to spend more on shopping
  • Top buying professions: IT, Healthcare, and Aviation
Product Preferences
  • Most sold product categories:

    • Clothing & Apparel
    • Food
    • Electronics & Gadgets
Project Workflow
Data Preprocessing
  • Handled missing values
  • Converted data types
  • Created new features (feature engineering)
Exploratory Data Analysis (EDA)
  • Analyzed customer demographics
  • Identified spending patterns and top buyer segments
Visualization
  • Used Seaborn and Matplotlib
  • Created insightful plots: bar charts, count plots, and heatmaps
Conclusions & Business Insights
  • Derived actionable insights to assist business decision-making during festive sales
Conclusion
This project highlights how Diwali shopping behavior varies across demographics, locations, and product preferences. The analysis can help businesses optimize marketing campaigns, product inventory, and target audiences more effectively during festive seasons.
Acknowledgements
  • Dataset used for educational and analytical purposes
  • Inspired by real-world retail analytics scenarios
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
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0 forks
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Used by
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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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SalesAnalysis.ipynb
Repository files navigation
Diwali Sales Data Analysis
A data analysis project exploring customer behavior during Diwali shopping using Python and data visualization tools. The project aims to uncover insights from customer demographics, location-based trends, spending habits, and product preferences.
Dataset Information
Dataset Name: Diwali Sales Data
Key Columns:
  • Gender: Male/Female customers
  • Age Group: Customer age distribution
  • State: Location of customers
  • Marital Status: Married/Unmarried customers
  • Occupation: Buyer professions
  • Product Category: Type of products purchased
  • Amount: Total amount spent
Tools & Technologies Used
CategoryTools
Programming LanguagePython
Libraries UsedPandas, NumPy, Matplotlib, Seaborn
Visualization ToolsSeaborn, Matplotlib
PlatformGoogle Colab / Jupyter Notebook
Key Findings from the Analysis
Customer Demographics
  • Majority of buyers are aged 26-35 years
  • Women contribute significantly to high-value purchases
Location-Based Sales
  • Highest number of orders come from:

    • Uttar Pradesh
    • Maharashtra
    • Karnataka
Spending Behavior
  • Unmarried women tend to spend more on shopping
  • Top buying professions: IT, Healthcare, and Aviation
Product Preferences
  • Most sold product categories:

    • Clothing & Apparel
    • Food
    • Electronics & Gadgets
Project Workflow
Data Preprocessing
  • Handled missing values
  • Converted data types
  • Created new features (feature engineering)
Exploratory Data Analysis (EDA)
  • Analyzed customer demographics
  • Identified spending patterns and top buyer segments
Visualization
  • Used Seaborn and Matplotlib
  • Created insightful plots: bar charts, count plots, and heatmaps
Conclusions & Business Insights
  • Derived actionable insights to assist business decision-making during festive sales
Conclusion
This project highlights how Diwali shopping behavior varies across demographics, locations, and product preferences. The analysis can help businesses optimize marketing campaigns, product inventory, and target audiences more effectively during festive seasons.
Acknowledgements
  • Dataset used for educational and analytical purposes
  • Inspired by real-world retail analytics scenarios
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
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0 forks
Report repository
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Used by
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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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SalesAnalysis.ipynb
Repository files navigation
Diwali Sales Data Analysis
A data analysis project exploring customer behavior during Diwali shopping using Python and data visualization tools. The project aims to uncover insights from customer demographics, location-based trends, spending habits, and product preferences.
Dataset Information
Dataset Name: Diwali Sales Data
Key Columns:
  • Gender: Male/Female customers
  • Age Group: Customer age distribution
  • State: Location of customers
  • Marital Status: Married/Unmarried customers
  • Occupation: Buyer professions
  • Product Category: Type of products purchased
  • Amount: Total amount spent
Tools & Technologies Used
CategoryTools
Programming LanguagePython
Libraries UsedPandas, NumPy, Matplotlib, Seaborn
Visualization ToolsSeaborn, Matplotlib
PlatformGoogle Colab / Jupyter Notebook
Key Findings from the Analysis
Customer Demographics
  • Majority of buyers are aged 26-35 years
  • Women contribute significantly to high-value purchases
Location-Based Sales
  • Highest number of orders come from:

    • Uttar Pradesh
    • Maharashtra
    • Karnataka
Spending Behavior
  • Unmarried women tend to spend more on shopping
  • Top buying professions: IT, Healthcare, and Aviation
Product Preferences
  • Most sold product categories:

    • Clothing & Apparel
    • Food
    • Electronics & Gadgets
Project Workflow
Data Preprocessing
  • Handled missing values
  • Converted data types
  • Created new features (feature engineering)
Exploratory Data Analysis (EDA)
  • Analyzed customer demographics
  • Identified spending patterns and top buyer segments
Visualization
  • Used Seaborn and Matplotlib
  • Created insightful plots: bar charts, count plots, and heatmaps
Conclusions & Business Insights
  • Derived actionable insights to assist business decision-making during festive sales
Conclusion
This project highlights how Diwali shopping behavior varies across demographics, locations, and product preferences. The analysis can help businesses optimize marketing campaigns, product inventory, and target audiences more effectively during festive seasons.
Acknowledgements
  • Dataset used for educational and analytical purposes
  • Inspired by real-world retail analytics scenarios
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
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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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README.md
SalesAnalysis.ipynb
SalesAnalysis.ipynb
Repository files navigation
Diwali Sales Data Analysis
A data analysis project exploring customer behavior during Diwali shopping using Python and data visualization tools. The project aims to uncover insights from customer demographics, location-based trends, spending habits, and product preferences.
Dataset Information
Dataset Name: Diwali Sales Data
Key Columns:
  • Gender: Male/Female customers
  • Age Group: Customer age distribution
  • State: Location of customers
  • Marital Status: Married/Unmarried customers
  • Occupation: Buyer professions
  • Product Category: Type of products purchased
  • Amount: Total amount spent
Tools & Technologies Used
CategoryTools
Programming LanguagePython
Libraries UsedPandas, NumPy, Matplotlib, Seaborn
Visualization ToolsSeaborn, Matplotlib
PlatformGoogle Colab / Jupyter Notebook
Key Findings from the Analysis
Customer Demographics
  • Majority of buyers are aged 26-35 years
  • Women contribute significantly to high-value purchases
Location-Based Sales
  • Highest number of orders come from:

    • Uttar Pradesh
    • Maharashtra
    • Karnataka
Spending Behavior
  • Unmarried women tend to spend more on shopping
  • Top buying professions: IT, Healthcare, and Aviation
Product Preferences
  • Most sold product categories:

    • Clothing & Apparel
    • Food
    • Electronics & Gadgets
Project Workflow
Data Preprocessing
  • Handled missing values
  • Converted data types
  • Created new features (feature engineering)
Exploratory Data Analysis (EDA)
  • Analyzed customer demographics
  • Identified spending patterns and top buyer segments
Visualization
  • Used Seaborn and Matplotlib
  • Created insightful plots: bar charts, count plots, and heatmaps
Conclusions & Business Insights
  • Derived actionable insights to assist business decision-making during festive sales
Conclusion
This project highlights how Diwali shopping behavior varies across demographics, locations, and product preferences. The analysis can help businesses optimize marketing campaigns, product inventory, and target audiences more effectively during festive seasons.
Acknowledgements
  • Dataset used for educational and analytical purposes
  • Inspired by real-world retail analytics scenarios
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
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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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Diwali Sales Data Analysis
A data analysis project exploring customer behavior during Diwali shopping using Python and data visualization tools. The project aims to uncover insights from customer demographics, location-based trends, spending habits, and product preferences.
Dataset Information
Dataset Name: Diwali Sales Data
Key Columns:
  • Gender: Male/Female customers
  • Age Group: Customer age distribution
  • State: Location of customers
  • Marital Status: Married/Unmarried customers
  • Occupation: Buyer professions
  • Product Category: Type of products purchased
  • Amount: Total amount spent
Tools & Technologies Used
CategoryTools
Programming LanguagePython
Libraries UsedPandas, NumPy, Matplotlib, Seaborn
Visualization ToolsSeaborn, Matplotlib
PlatformGoogle Colab / Jupyter Notebook
Key Findings from the Analysis
Customer Demographics
  • Majority of buyers are aged 26-35 years
  • Women contribute significantly to high-value purchases
Location-Based Sales
  • Highest number of orders come from:

    • Uttar Pradesh
    • Maharashtra
    • Karnataka
Spending Behavior
  • Unmarried women tend to spend more on shopping
  • Top buying professions: IT, Healthcare, and Aviation
Product Preferences
  • Most sold product categories:

    • Clothing & Apparel
    • Food
    • Electronics & Gadgets
Project Workflow
Data Preprocessing
  • Handled missing values
  • Converted data types
  • Created new features (feature engineering)
Exploratory Data Analysis (EDA)
  • Analyzed customer demographics
  • Identified spending patterns and top buyer segments
Visualization
  • Used Seaborn and Matplotlib
  • Created insightful plots: bar charts, count plots, and heatmaps
Conclusions & Business Insights
  • Derived actionable insights to assist business decision-making during festive sales
Conclusion
This project highlights how Diwali shopping behavior varies across demographics, locations, and product preferences. The analysis can help businesses optimize marketing campaigns, product inventory, and target audiences more effectively during festive seasons.
Acknowledgements
  • Dataset used for educational and analytical purposes
  • Inspired by real-world retail analytics scenarios
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