Predictive Analysis of Car Prices

Project Overview

<p>The analysis begins by loading the <code data-start=\"2199\" data-end=\"2214\">car_price.csv</code> dataset into pandas and previewing its first rows . After checking for missing values and computing basic descriptive statistics , the features and target (<code data-start=\"2519\" data-end=\"2526\">price</code>) are split into train and test sets via <code data-start=\"2567\" data-end=\"2585\">train_test_split</code> . A <code data-start=\"2664\" data-end=\"2682\">LinearRegression</code> model is then fit to the training data, predictions are made on the test set, and performance is quantified through R², MAE, and MSE metrics . Finally, results are visualized with a scatter plot of actual vs. predicted prices and feature-price relationships (e.g. City MPG vs. Price) via seaborn .</p>