Creating a Car Price Prediction Model with Linear Regression #carpricing

The project explores a Car Dataset from Kaggle, focusing on predicting car prices using Linear Regression. The dataset includes features like odometer value, year produced, and engine capacity. The dataset is imported into a Colab notebook for model creation. Duplicate entries are removed, missing values are checked, and key features are selected for regression analysis. The dataset is split into training and testing sets, and a Linear Regression model is fitted and tested. The model’s performance is evaluated using mean squared error, mean absolute error, and R² score. The results show that the model has a relatively high error rate, with some inaccuracies in predictions. The scatter plot shows a linear trend but also discrepancies from the ideal line, indicating some struggles in making accurate predictions. The project highlights the importance of clean data and choosing the right features for accurate predictions. It also emphasizes the practical need for AI and the importance of continuous learning in Machine Learning and Artificial Intelligence.

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Source link: https://medium.com/@michaelwagner21/building-a-linear-regression-model-using-a-car-dataset-to-determine-the-price-of-a-car-a6cbf29449e8?source=rss——artificial_intelligence-5