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Project Overview
The analysis begins by loading the car_price.csv dataset into pandas and previewing its first rows . After checking for missing values and computing basic descriptive statistics , the features and target (price) are split into train and test sets via train_test_split . A LinearRegression 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 .
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Project Details
- Completion Date November 2024
- Category Machine Learning
- Project Type HTML File
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