<p>This project involved exploring and analyzing a dataset of used cars and building a predictive ML model for car selling price. The EDA process will help us to understand the dataset and identify patterns and trends,</p>
<p>&nbsp;while the ML model helped us to make predictions based on the data. The final model’s performance can be improved further by fine-tuning the hyperparameters or trying different algorithms.</p>
<h2>EDA</h2>
<p>The EDA (Exploratory Data Analysis) process involves exploring and analyzing the dataset to gain insights and understanding of the data. The EDA process includes various steps, such as data cleaning, data visualization, and statistical analysis. In this project, we have performed the following EDA tasks:</p>
<ol>
  <li>Removed the Unnamed: 0 column as it was not useful for analysis.</li>
  <li>Checked for missing values and handled them.</li>
  <li>Identified categorical variables and checked their value counts.</li>
  <li>Visualized the top and bottom 10 brands and models based on selling price.</li>
  <li>Visualized the distribution of vehicle age, selling price, and mileage.</li>
  <li>Created a box plot of selling price by brand and fuel type.</li>
  <li>Created a stacked bar chart of fuel type by seller type.</li>
</ol>

Used Cars Data EDA and ML Model.

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