Purchase Price Prediction for Used Cars Using Deep Learning

In this project, we’ll preprocess the data, address the missing values, and convert categorical variables into numerical representations for seamless processing. Next, we’ll harness the visualization capabilities of seaborn to gain valuable insights into the data. Our visualizations will unveil correlations between car attributes and the sale price, helping us understand the factors influencing the prices.

With the data ready, we’ll construct a powerful deep learning model using TensorFlow/Keras. The Sequential model will be at the heart of our efforts, comprising Dense layers with suitable activation functions. We’ll employ a linear activation function in the output layer to predict continuous sale prices.

The model will be armed with an optimal compilation, incorporating an effective optimizer, mean squared error loss function, and mean absolute error as a metric for evaluation. Next, we’ll train the model and monitor the training process to identify any signs of overfitting or underfitting. After rigorous training, we’ll test the model’s performance using the testing data to determine how accurately it predicts the sale prices. We’ll also use the R2 score to evaluate the model’s accuracy.