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Car Data Analysis

PythonJupyter NotebookLicense: MITStatusGitHub stars

⭐ “If you find this project useful, please consider giving it a star on GitHub!

Project Overview

This project analyzes a dataset of 11,914 car listings with 16 features.
The goal is to clean the data, engineer new features, perform exploratory data analysis (EDA), and create visualizations to uncover meaningful insights about pricing, performance, efficiency, and popularity.

Objectives

  • Import and explore the dataset
  • Clean and preprocess data (handling missing values, correcting errors)
  • Engineer new features (e.g., Total MPG)
  • Perform descriptive statistics and group analysis
  • Visualize key relationships between car attributes
  • Identify correlations and market trends

Tech Stack

  • Python
  • Libraries: Pandas, NumPy, Matplotlib, Seaborn
  • Jupyter Notebook

Key Insights

  • Engine HP: ranges from 55 to 1001 HP, with a skewed distribution driven by high-performance cars.
  • MSRP: spans from $2,000 to over $2M, median around $31k, showing strong skew due to luxury/exotic models.
  • Fuel Efficiency: electric vehicles stand out with extremely high MPG values, while most cars cluster around 15–25 MPG.
  • Driven Wheels: rear/all-wheel drive cars are more expensive, front-wheel drive cars are cheaper.
  • Vehicle Size: larger cars are both more popular and more expensive.
  • Correlation Analysis: Engine HP correlates positively with MSRP (0.65), negatively with MPG; City and Highway MPG strongly correlate (0.94). Popularity shows no meaningful linear relationship.

Repository Structure

  • notebooks/ -> Jupyter notebooks with analysis
  • data/ -> sample dataset + link to source
  • images/ -> charts and visualizations
  • requirements.txt -> list of dependencies

How to Run

  1. Clone the repository 
    git clone https://github.com/MapiAI/car-data-analysis.git
  2. Install requirements 
    pip install -r requirements.txt
  3. Open the notebook 
    jupyter notebook notebooks/car_data_analysis.ipynb

Visuals

Histogram: City MPG distribution

This pattern suggests that the dataset reflects the real-world market, where most cars have moderate fuel efficiency, and high efficiency vehicles are relatively rare.

Scatter Plot: Total MPG vs MSRP

Premium unleaded cars diversify mainly in price, regular unleaded in efficiency, while electric vehicles stand apart with high efficiency and consistent pricing.

Bar chart: Average MSRP by Vehicle Size

The bar plot shows a clear difference in market price between large vehicles and the other two vehicle sizes.

Line plot: City vs Highway MPG by Transmission Type

Overall, the plot highlights a tight cluster for traditional transmissions and a clear outlier formed by electric vehicles.

For the complete set of visualizations, please refer to the Car Data Analysis Notebook.

Next Steps

  • Extend analysis with machine learning models (e.g., car price prediction)

  • Publish interactive dashboard in Tableau

License

This project is licensed under the MIT License – free to use, modify, and share with attribution.

Author & Contact

Created by Maria Petralia (MaPi)

⭐ If you find this project useful, please consider giving it a star on GitHub!

Feel free to connect or reach out for collaboration opportunities!

About

Car dataset analysis using Python, Pandas, and visualization libraries. Exploratory Data Analysis (EDA) uncovering insights on pricing, performance, efficiency, and automotive market trends.

Topics

automotivecar-datacarsdata-scienceedajupyter-notebookmatplotlibmatplotlib-pyplotnumpypandasportfolio-projectpythonseabornvisualization

Resources

Readme
MIT license
Activity

Stars

2 stars

Watchers

0 watching

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0 forks
Report repository

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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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Car Data Analysis
PythonJupyter NotebookLicense: MITStatusGitHub stars
⭐ “If you find this project useful, please consider giving it a star on GitHub!
Project Overview
This project analyzes a dataset of 11,914 car listings with 16 features.

The goal is to clean the data, engineer new features, perform exploratory data analysis (EDA), and create visualizations to uncover meaningful insights about pricing, performance, efficiency, and popularity.
Objectives
  • Import and explore the dataset
  • Clean and preprocess data (handling missing values, correcting errors)
  • Engineer new features (e.g., Total MPG)
  • Perform descriptive statistics and group analysis
  • Visualize key relationships between car attributes
  • Identify correlations and market trends
Tech Stack
  • Python
  • Libraries: Pandas, NumPy, Matplotlib, Seaborn
  • Jupyter Notebook
Key Insights
  • Engine HP: ranges from 55 to 1001 HP, with a skewed distribution driven by high-performance cars.
  • MSRP: spans from $2,000 to over $2M, median around $31k, showing strong skew due to luxury/exotic models.
  • Fuel Efficiency: electric vehicles stand out with extremely high MPG values, while most cars cluster around 15–25 MPG.
  • Driven Wheels: rear/all-wheel drive cars are more expensive, front-wheel drive cars are cheaper.
  • Vehicle Size: larger cars are both more popular and more expensive.
  • Correlation Analysis: Engine HP correlates positively with MSRP (0.65), negatively with MPG; City and Highway MPG strongly correlate (0.94). Popularity shows no meaningful linear relationship.
Repository Structure
  • notebooks/ -> Jupyter notebooks with analysis
  • data/ -> sample dataset + link to source
  • images/ -> charts and visualizations
  • requirements.txt -> list of dependencies
How to Run
  1. Clone the repository

    git clone https://github.com/MapiAI/car-data-analysis.git

  2. Install requirements

    pip install -r requirements.txt

  3. Open the notebook

    jupyter notebook notebooks/car_data_analysis.ipynb

Visuals
Histogram: City MPG distribution
This pattern suggests that the dataset reflects the real-world market, where most cars have moderate fuel efficiency, and high efficiency vehicles are relatively rare.
Scatter Plot: Total MPG vs MSRP
Premium unleaded cars diversify mainly in price, regular unleaded in efficiency, while electric vehicles stand apart with high efficiency and consistent pricing.
Bar chart: Average MSRP by Vehicle Size
The bar plot shows a clear difference in market price between large vehicles and the other two vehicle sizes.
Line plot: City vs Highway MPG by Transmission Type
Overall, the plot highlights a tight cluster for traditional transmissions and a clear outlier formed by electric vehicles.
For the complete set of visualizations, please refer to the Car Data Analysis Notebook.
Next Steps
  • Extend analysis with machine learning models (e.g., car price prediction)
  • Publish interactive dashboard in Tableau
License
This project is licensed under the MIT License – free to use, modify, and share with attribution.
Author & Contact
Created by Maria Petralia (MaPi)
⭐ If you find this project useful, please consider giving it a star on GitHub!
Feel free to connect or reach out for collaboration opportunities!
About
Car dataset analysis using Python, Pandas, and visualization libraries. Exploratory Data Analysis (EDA) uncovering insights on pricing, performance, efficiency, and automotive market trends.
Topics
automotivecar-datacarsdata-scienceedajupyter-notebookmatplotlibmatplotlib-pyplotnumpypandasportfolio-projectpythonseabornvisualization
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Car Data Analysis
PythonJupyter NotebookLicense: MITStatusGitHub stars
⭐ “If you find this project useful, please consider giving it a star on GitHub!
Project Overview
This project analyzes a dataset of 11,914 car listings with 16 features.

The goal is to clean the data, engineer new features, perform exploratory data analysis (EDA), and create visualizations to uncover meaningful insights about pricing, performance, efficiency, and popularity.
Objectives
  • Import and explore the dataset
  • Clean and preprocess data (handling missing values, correcting errors)
  • Engineer new features (e.g., Total MPG)
  • Perform descriptive statistics and group analysis
  • Visualize key relationships between car attributes
  • Identify correlations and market trends
Tech Stack
  • Python
  • Libraries: Pandas, NumPy, Matplotlib, Seaborn
  • Jupyter Notebook
Key Insights
  • Engine HP: ranges from 55 to 1001 HP, with a skewed distribution driven by high-performance cars.
  • MSRP: spans from $2,000 to over $2M, median around $31k, showing strong skew due to luxury/exotic models.
  • Fuel Efficiency: electric vehicles stand out with extremely high MPG values, while most cars cluster around 15–25 MPG.
  • Driven Wheels: rear/all-wheel drive cars are more expensive, front-wheel drive cars are cheaper.
  • Vehicle Size: larger cars are both more popular and more expensive.
  • Correlation Analysis: Engine HP correlates positively with MSRP (0.65), negatively with MPG; City and Highway MPG strongly correlate (0.94). Popularity shows no meaningful linear relationship.
Repository Structure
  • notebooks/ -> Jupyter notebooks with analysis
  • data/ -> sample dataset + link to source
  • images/ -> charts and visualizations
  • requirements.txt -> list of dependencies
How to Run
  1. Clone the repository

    git clone https://github.com/MapiAI/car-data-analysis.git

  2. Install requirements

    pip install -r requirements.txt

  3. Open the notebook

    jupyter notebook notebooks/car_data_analysis.ipynb

Visuals
Histogram: City MPG distribution
This pattern suggests that the dataset reflects the real-world market, where most cars have moderate fuel efficiency, and high efficiency vehicles are relatively rare.
Scatter Plot: Total MPG vs MSRP
Premium unleaded cars diversify mainly in price, regular unleaded in efficiency, while electric vehicles stand apart with high efficiency and consistent pricing.
Bar chart: Average MSRP by Vehicle Size
The bar plot shows a clear difference in market price between large vehicles and the other two vehicle sizes.
Line plot: City vs Highway MPG by Transmission Type
Overall, the plot highlights a tight cluster for traditional transmissions and a clear outlier formed by electric vehicles.
For the complete set of visualizations, please refer to the Car Data Analysis Notebook.
Next Steps
  • Extend analysis with machine learning models (e.g., car price prediction)
  • Publish interactive dashboard in Tableau
License
This project is licensed under the MIT License – free to use, modify, and share with attribution.
Author & Contact
Created by Maria Petralia (MaPi)
⭐ If you find this project useful, please consider giving it a star on GitHub!
Feel free to connect or reach out for collaboration opportunities!
About
Car dataset analysis using Python, Pandas, and visualization libraries. Exploratory Data Analysis (EDA) uncovering insights on pricing, performance, efficiency, and automotive market trends.
Topics
automotivecar-datacarsdata-scienceedajupyter-notebookmatplotlibmatplotlib-pyplotnumpypandasportfolio-projectpythonseabornvisualization
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
0 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('^' + ".*" + '

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Repository files navigation
Car Data Analysis
PythonJupyter NotebookLicense: MITStatusGitHub stars
⭐ “If you find this project useful, please consider giving it a star on GitHub!
Project Overview
This project analyzes a dataset of 11,914 car listings with 16 features.

The goal is to clean the data, engineer new features, perform exploratory data analysis (EDA), and create visualizations to uncover meaningful insights about pricing, performance, efficiency, and popularity.
Objectives
  • Import and explore the dataset
  • Clean and preprocess data (handling missing values, correcting errors)
  • Engineer new features (e.g., Total MPG)
  • Perform descriptive statistics and group analysis
  • Visualize key relationships between car attributes
  • Identify correlations and market trends
Tech Stack
  • Python
  • Libraries: Pandas, NumPy, Matplotlib, Seaborn
  • Jupyter Notebook
Key Insights
  • Engine HP: ranges from 55 to 1001 HP, with a skewed distribution driven by high-performance cars.
  • MSRP: spans from $2,000 to over $2M, median around $31k, showing strong skew due to luxury/exotic models.
  • Fuel Efficiency: electric vehicles stand out with extremely high MPG values, while most cars cluster around 15–25 MPG.
  • Driven Wheels: rear/all-wheel drive cars are more expensive, front-wheel drive cars are cheaper.
  • Vehicle Size: larger cars are both more popular and more expensive.
  • Correlation Analysis: Engine HP correlates positively with MSRP (0.65), negatively with MPG; City and Highway MPG strongly correlate (0.94). Popularity shows no meaningful linear relationship.
Repository Structure
  • notebooks/ -> Jupyter notebooks with analysis
  • data/ -> sample dataset + link to source
  • images/ -> charts and visualizations
  • requirements.txt -> list of dependencies
How to Run
  1. Clone the repository

    git clone https://github.com/MapiAI/car-data-analysis.git

  2. Install requirements

    pip install -r requirements.txt

  3. Open the notebook

    jupyter notebook notebooks/car_data_analysis.ipynb

Visuals
Histogram: City MPG distribution
This pattern suggests that the dataset reflects the real-world market, where most cars have moderate fuel efficiency, and high efficiency vehicles are relatively rare.
Scatter Plot: Total MPG vs MSRP
Premium unleaded cars diversify mainly in price, regular unleaded in efficiency, while electric vehicles stand apart with high efficiency and consistent pricing.
Bar chart: Average MSRP by Vehicle Size
The bar plot shows a clear difference in market price between large vehicles and the other two vehicle sizes.
Line plot: City vs Highway MPG by Transmission Type
Overall, the plot highlights a tight cluster for traditional transmissions and a clear outlier formed by electric vehicles.
For the complete set of visualizations, please refer to the Car Data Analysis Notebook.
Next Steps
  • Extend analysis with machine learning models (e.g., car price prediction)
  • Publish interactive dashboard in Tableau
License
This project is licensed under the MIT License – free to use, modify, and share with attribution.
Author & Contact
Created by Maria Petralia (MaPi)
⭐ If you find this project useful, please consider giving it a star on GitHub!
Feel free to connect or reach out for collaboration opportunities!
About
Car dataset analysis using Python, Pandas, and visualization libraries. Exploratory Data Analysis (EDA) uncovering insights on pricing, performance, efficiency, and automotive market trends.
Topics
automotivecar-datacarsdata-scienceedajupyter-notebookmatplotlibmatplotlib-pyplotnumpypandasportfolio-projectpythonseabornvisualization
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Car Data Analysis
PythonJupyter NotebookLicense: MITStatusGitHub stars
⭐ “If you find this project useful, please consider giving it a star on GitHub!
Project Overview
This project analyzes a dataset of 11,914 car listings with 16 features.

The goal is to clean the data, engineer new features, perform exploratory data analysis (EDA), and create visualizations to uncover meaningful insights about pricing, performance, efficiency, and popularity.
Objectives
  • Import and explore the dataset
  • Clean and preprocess data (handling missing values, correcting errors)
  • Engineer new features (e.g., Total MPG)
  • Perform descriptive statistics and group analysis
  • Visualize key relationships between car attributes
  • Identify correlations and market trends
Tech Stack
  • Python
  • Libraries: Pandas, NumPy, Matplotlib, Seaborn
  • Jupyter Notebook
Key Insights
  • Engine HP: ranges from 55 to 1001 HP, with a skewed distribution driven by high-performance cars.
  • MSRP: spans from $2,000 to over $2M, median around $31k, showing strong skew due to luxury/exotic models.
  • Fuel Efficiency: electric vehicles stand out with extremely high MPG values, while most cars cluster around 15–25 MPG.
  • Driven Wheels: rear/all-wheel drive cars are more expensive, front-wheel drive cars are cheaper.
  • Vehicle Size: larger cars are both more popular and more expensive.
  • Correlation Analysis: Engine HP correlates positively with MSRP (0.65), negatively with MPG; City and Highway MPG strongly correlate (0.94). Popularity shows no meaningful linear relationship.
Repository Structure
  • notebooks/ -> Jupyter notebooks with analysis
  • data/ -> sample dataset + link to source
  • images/ -> charts and visualizations
  • requirements.txt -> list of dependencies
How to Run
  1. Clone the repository

    git clone https://github.com/MapiAI/car-data-analysis.git

  2. Install requirements

    pip install -r requirements.txt

  3. Open the notebook

    jupyter notebook notebooks/car_data_analysis.ipynb

Visuals
Histogram: City MPG distribution
This pattern suggests that the dataset reflects the real-world market, where most cars have moderate fuel efficiency, and high efficiency vehicles are relatively rare.
Scatter Plot: Total MPG vs MSRP
Premium unleaded cars diversify mainly in price, regular unleaded in efficiency, while electric vehicles stand apart with high efficiency and consistent pricing.
Bar chart: Average MSRP by Vehicle Size
The bar plot shows a clear difference in market price between large vehicles and the other two vehicle sizes.
Line plot: City vs Highway MPG by Transmission Type
Overall, the plot highlights a tight cluster for traditional transmissions and a clear outlier formed by electric vehicles.
For the complete set of visualizations, please refer to the Car Data Analysis Notebook.
Next Steps
  • Extend analysis with machine learning models (e.g., car price prediction)
  • Publish interactive dashboard in Tableau
License
This project is licensed under the MIT License – free to use, modify, and share with attribution.
Author & Contact
Created by Maria Petralia (MaPi)
⭐ If you find this project useful, please consider giving it a star on GitHub!
Feel free to connect or reach out for collaboration opportunities!
About
Car dataset analysis using Python, Pandas, and visualization libraries. Exploratory Data Analysis (EDA) uncovering insights on pricing, performance, efficiency, and automotive market trends.
Topics
automotivecar-datacarsdata-scienceedajupyter-notebookmatplotlibmatplotlib-pyplotnumpypandasportfolio-projectpythonseabornvisualization
Resources
Readme
MIT license
Activity
Stars
2 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); }
})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Car Data Analysis
PythonJupyter NotebookLicense: MITStatusGitHub stars
⭐ “If you find this project useful, please consider giving it a star on GitHub!
Project Overview
This project analyzes a dataset of 11,914 car listings with 16 features.

The goal is to clean the data, engineer new features, perform exploratory data analysis (EDA), and create visualizations to uncover meaningful insights about pricing, performance, efficiency, and popularity.
Objectives
  • Import and explore the dataset
  • Clean and preprocess data (handling missing values, correcting errors)
  • Engineer new features (e.g., Total MPG)
  • Perform descriptive statistics and group analysis
  • Visualize key relationships between car attributes
  • Identify correlations and market trends
Tech Stack
  • Python
  • Libraries: Pandas, NumPy, Matplotlib, Seaborn
  • Jupyter Notebook
Key Insights
  • Engine HP: ranges from 55 to 1001 HP, with a skewed distribution driven by high-performance cars.
  • MSRP: spans from $2,000 to over $2M, median around $31k, showing strong skew due to luxury/exotic models.
  • Fuel Efficiency: electric vehicles stand out with extremely high MPG values, while most cars cluster around 15–25 MPG.
  • Driven Wheels: rear/all-wheel drive cars are more expensive, front-wheel drive cars are cheaper.
  • Vehicle Size: larger cars are both more popular and more expensive.
  • Correlation Analysis: Engine HP correlates positively with MSRP (0.65), negatively with MPG; City and Highway MPG strongly correlate (0.94). Popularity shows no meaningful linear relationship.
Repository Structure
  • notebooks/ -> Jupyter notebooks with analysis
  • data/ -> sample dataset + link to source
  • images/ -> charts and visualizations
  • requirements.txt -> list of dependencies
How to Run
  1. Clone the repository

    git clone https://github.com/MapiAI/car-data-analysis.git

  2. Install requirements

    pip install -r requirements.txt

  3. Open the notebook

    jupyter notebook notebooks/car_data_analysis.ipynb

Visuals
Histogram: City MPG distribution
This pattern suggests that the dataset reflects the real-world market, where most cars have moderate fuel efficiency, and high efficiency vehicles are relatively rare.
Scatter Plot: Total MPG vs MSRP
Premium unleaded cars diversify mainly in price, regular unleaded in efficiency, while electric vehicles stand apart with high efficiency and consistent pricing.
Bar chart: Average MSRP by Vehicle Size
The bar plot shows a clear difference in market price between large vehicles and the other two vehicle sizes.
Line plot: City vs Highway MPG by Transmission Type
Overall, the plot highlights a tight cluster for traditional transmissions and a clear outlier formed by electric vehicles.
For the complete set of visualizations, please refer to the Car Data Analysis Notebook.
Next Steps
  • Extend analysis with machine learning models (e.g., car price prediction)
  • Publish interactive dashboard in Tableau
License
This project is licensed under the MIT License – free to use, modify, and share with attribution.
Author & Contact
Created by Maria Petralia (MaPi)
⭐ If you find this project useful, please consider giving it a star on GitHub!
Feel free to connect or reach out for collaboration opportunities!
About
Car dataset analysis using Python, Pandas, and visualization libraries. Exploratory Data Analysis (EDA) uncovering insights on pricing, performance, efficiency, and automotive market trends.
Topics
automotivecar-datacarsdata-scienceedajupyter-notebookmatplotlibmatplotlib-pyplotnumpypandasportfolio-projectpythonseabornvisualization
Resources
Readme
MIT license
Activity
Stars
2 stars
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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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Car Data Analysis
PythonJupyter NotebookLicense: MITStatusGitHub stars
⭐ “If you find this project useful, please consider giving it a star on GitHub!
Project Overview
This project analyzes a dataset of 11,914 car listings with 16 features.

The goal is to clean the data, engineer new features, perform exploratory data analysis (EDA), and create visualizations to uncover meaningful insights about pricing, performance, efficiency, and popularity.
Objectives
  • Import and explore the dataset
  • Clean and preprocess data (handling missing values, correcting errors)
  • Engineer new features (e.g., Total MPG)
  • Perform descriptive statistics and group analysis
  • Visualize key relationships between car attributes
  • Identify correlations and market trends
Tech Stack
  • Python
  • Libraries: Pandas, NumPy, Matplotlib, Seaborn
  • Jupyter Notebook
Key Insights
  • Engine HP: ranges from 55 to 1001 HP, with a skewed distribution driven by high-performance cars.
  • MSRP: spans from $2,000 to over $2M, median around $31k, showing strong skew due to luxury/exotic models.
  • Fuel Efficiency: electric vehicles stand out with extremely high MPG values, while most cars cluster around 15–25 MPG.
  • Driven Wheels: rear/all-wheel drive cars are more expensive, front-wheel drive cars are cheaper.
  • Vehicle Size: larger cars are both more popular and more expensive.
  • Correlation Analysis: Engine HP correlates positively with MSRP (0.65), negatively with MPG; City and Highway MPG strongly correlate (0.94). Popularity shows no meaningful linear relationship.
Repository Structure
  • notebooks/ -> Jupyter notebooks with analysis
  • data/ -> sample dataset + link to source
  • images/ -> charts and visualizations
  • requirements.txt -> list of dependencies
How to Run
  1. Clone the repository

    git clone https://github.com/MapiAI/car-data-analysis.git

  2. Install requirements

    pip install -r requirements.txt

  3. Open the notebook

    jupyter notebook notebooks/car_data_analysis.ipynb

Visuals
Histogram: City MPG distribution
This pattern suggests that the dataset reflects the real-world market, where most cars have moderate fuel efficiency, and high efficiency vehicles are relatively rare.
Scatter Plot: Total MPG vs MSRP
Premium unleaded cars diversify mainly in price, regular unleaded in efficiency, while electric vehicles stand apart with high efficiency and consistent pricing.
Bar chart: Average MSRP by Vehicle Size
The bar plot shows a clear difference in market price between large vehicles and the other two vehicle sizes.
Line plot: City vs Highway MPG by Transmission Type
Overall, the plot highlights a tight cluster for traditional transmissions and a clear outlier formed by electric vehicles.
For the complete set of visualizations, please refer to the Car Data Analysis Notebook.
Next Steps
  • Extend analysis with machine learning models (e.g., car price prediction)
  • Publish interactive dashboard in Tableau
License
This project is licensed under the MIT License – free to use, modify, and share with attribution.
Author & Contact
Created by Maria Petralia (MaPi)
⭐ If you find this project useful, please consider giving it a star on GitHub!
Feel free to connect or reach out for collaboration opportunities!
About
Car dataset analysis using Python, Pandas, and visualization libraries. Exploratory Data Analysis (EDA) uncovering insights on pricing, performance, efficiency, and automotive market trends.
Topics
automotivecar-datacarsdata-scienceedajupyter-notebookmatplotlibmatplotlib-pyplotnumpypandasportfolio-projectpythonseabornvisualization
Resources
Readme
MIT license
Activity
Stars
2 stars
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0 forks
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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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Repository files navigation
Car Data Analysis
PythonJupyter NotebookLicense: MITStatusGitHub stars
⭐ “If you find this project useful, please consider giving it a star on GitHub!
Project Overview
This project analyzes a dataset of 11,914 car listings with 16 features.

The goal is to clean the data, engineer new features, perform exploratory data analysis (EDA), and create visualizations to uncover meaningful insights about pricing, performance, efficiency, and popularity.
Objectives
  • Import and explore the dataset
  • Clean and preprocess data (handling missing values, correcting errors)
  • Engineer new features (e.g., Total MPG)
  • Perform descriptive statistics and group analysis
  • Visualize key relationships between car attributes
  • Identify correlations and market trends
Tech Stack
  • Python
  • Libraries: Pandas, NumPy, Matplotlib, Seaborn
  • Jupyter Notebook
Key Insights
  • Engine HP: ranges from 55 to 1001 HP, with a skewed distribution driven by high-performance cars.
  • MSRP: spans from $2,000 to over $2M, median around $31k, showing strong skew due to luxury/exotic models.
  • Fuel Efficiency: electric vehicles stand out with extremely high MPG values, while most cars cluster around 15–25 MPG.
  • Driven Wheels: rear/all-wheel drive cars are more expensive, front-wheel drive cars are cheaper.
  • Vehicle Size: larger cars are both more popular and more expensive.
  • Correlation Analysis: Engine HP correlates positively with MSRP (0.65), negatively with MPG; City and Highway MPG strongly correlate (0.94). Popularity shows no meaningful linear relationship.
Repository Structure
  • notebooks/ -> Jupyter notebooks with analysis
  • data/ -> sample dataset + link to source
  • images/ -> charts and visualizations
  • requirements.txt -> list of dependencies
How to Run
  1. Clone the repository

    git clone https://github.com/MapiAI/car-data-analysis.git

  2. Install requirements

    pip install -r requirements.txt

  3. Open the notebook

    jupyter notebook notebooks/car_data_analysis.ipynb

Visuals
Histogram: City MPG distribution
This pattern suggests that the dataset reflects the real-world market, where most cars have moderate fuel efficiency, and high efficiency vehicles are relatively rare.
Scatter Plot: Total MPG vs MSRP
Premium unleaded cars diversify mainly in price, regular unleaded in efficiency, while electric vehicles stand apart with high efficiency and consistent pricing.
Bar chart: Average MSRP by Vehicle Size
The bar plot shows a clear difference in market price between large vehicles and the other two vehicle sizes.
Line plot: City vs Highway MPG by Transmission Type
Overall, the plot highlights a tight cluster for traditional transmissions and a clear outlier formed by electric vehicles.
For the complete set of visualizations, please refer to the Car Data Analysis Notebook.
Next Steps
  • Extend analysis with machine learning models (e.g., car price prediction)
  • Publish interactive dashboard in Tableau
License
This project is licensed under the MIT License – free to use, modify, and share with attribution.
Author & Contact
Created by Maria Petralia (MaPi)
⭐ If you find this project useful, please consider giving it a star on GitHub!
Feel free to connect or reach out for collaboration opportunities!
About
Car dataset analysis using Python, Pandas, and visualization libraries. Exploratory Data Analysis (EDA) uncovering insights on pricing, performance, efficiency, and automotive market trends.
Topics
automotivecar-datacarsdata-scienceedajupyter-notebookmatplotlibmatplotlib-pyplotnumpypandasportfolio-projectpythonseabornvisualization
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages