In this project, we'll build a neural network that predicts used car sale prices based on vehicle attributes and buyer characteristics. Using TensorFlow and Keras, we'll create a regression model that learns from historical sales data to estimate prices for new listings. This machine learning for price prediction project covers data preprocessing, exploratory visualization, and model training with performance evaluation using Python.
We'll start by loading the dataset, handling missing values, and performing categorical to numerical conversion for seamless neural network processing. Using seaborn data visualization, we'll create pairplots and comparative charts to explore correlations between car features, buyer demographics, and sale prices through correlation analysis. Next, we'll build a Sequential model architecture with Dense layers and a linear output for continuous value prediction, compile it with mean squared error optimization, and train while monitoring for overfitting detection. Finally, we'll evaluate the model on testing data using mean absolute error and R-squared score metrics to measure prediction accuracy.
By the end, we'll have a working automated car pricing system demonstrating TensorFlow/Keras deep learning for regression techniques, data-driven price estimation, seaborn visualization, and model performance metrics applicable to any predictive analytics problem involving continuous values.