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project_2.html
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<!DOCTYPE HTML>
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<title>Analysing EBay's Data</title>
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<span class="date">February 01, 2025</span>
<h3> Cleaning, Exploring and Initial Analysis for <span style="color: brown">eBAY Kleinanzeign</span> Cars Data </h3>
<p><b>Tools</b>: Python [ Pandas | Matplotlib | Seaborn ]</p>
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<h3>1. Introduction</h3>
<p>This project explores a dataset of used cars listed on eBay Kleinanzeigen,
a German online marketplace for classified ads. The dataset consists of secondhand car
advertisements posted between 2015 and 2016. The goal is to clean and analyze the
dataset to extract meaningful insights about car sales trends, brand popularity, and
the relationship between mileage and price.</p>
<h4>1.1 Questions</h3>
<ol>
<li>What are the popular car brands for the period of study?</li>
<li>What is the most expensive car on average during the period of study?</li>
<li>How do car prices differ by odometer readings on average?</li>
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<h3>2. Methodology</h3>
<h4>2.1 Data Exploration and Cleaning</h4>
<h4><em>Dataset Overview</em></h4>
<ul>
<li>The dataset originally contained <b>371,539 rows</b> and <b>20 columns</b>.</li>
<li>A random sample of <b>50,000 records</b> was selected for efficient processing and analysis.</li>
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<h4><em>Data Cleaning Steps</em></h4>
<ul>
<li><b>Renamed Columns:</b> Converted from camelCase to snake_case for better readability and consistency.</li>
<li><b>Handled Missing Values:</b> Replaced missing categorical values with <i>"Unknown."</i></li>
<li><b>Removed Duplicates:</b> Identified and removed 2 duplicate records.</li>
<li><b>Corrected Data Types: </b>
<ul>
<li>Converted odometer from text to integer.</li>
<li>Transformed date columns into datetime format.</li>
<li>Standardized categorical variables with German words to English equivalents.</li>
</ul>
</li>
<li><b>Filtered Outliers: </b>
<ul>
<li>Removed unrealistic registration years.</li>
<li>Adjusted price range (excluded values below $500 and above $100,000).</li>
</ul>
</li>
</ul>
<hr style="height: 2px; border-width: 0; color:rgba(190, 188, 188, 0.986); background-color: rgba(190, 188, 188, 0.986);">
<h3>3. Data Analysis and Insights</h3>
<h4>3.1 Popular Car Brands</h4>
<div class="center"><img src="Images/bar1.png" alt="" width="350" height="400" /></div>
<h4>3.2 Most Expensive Car Brands (Average Price)</h4>
<div class="center"><img src="Images/bar2.png" alt="" width="350" height="400" /></div>
<h4>3.3Car Price vs. Mileage Analysis</h4>
<p>As expected, higher mileage results into lower prices</p>
<div class="center"><img src="Images/pie.png" alt="" width="350" height="400" /></div>
<hr style="height: 2px; border-width: 0; color:rgba(190, 188, 188, 0.986); background-color: rgba(190, 188, 188, 0.986);">
<h3>4. Conclusion</h3>
<ul>
<li><b>Volkswagen</b> is the most frequently listed brand.</li>
<li><b>Porsche</b> commands the highest average price among brands.</li>
<li><b>Higher mileage vehicles generally have lower prices</b>, confirming depreciation trends.</li>
<li>The dataset has been thoroughly cleaned and pre-processed, making it suitable for further
predictive modelling and analysis.</li>
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<p style="font-weight: bold; text-align: center;">For more details, please refer to the full analysis by clicking on the following button:</p>
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