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Purchase Price Prediction for Used Cars Using Deep Learning

In this project, we’ll learn to explore the sales data of used cars by making multiple visualizations using seaborn and create a deep learning model using TensorFlow to predict the estimated amount customers would spend to buy a used car.

Purchase Price Prediction for Used Cars Using Deep Learning

You will learn to:

Explore the data using seaborn.

Build different blocks of a deep learning model.

Create a deep learning model using TensorFlow.

Make predictions using a trained deep learning model.


Machine Learning

Data Visualisation

Deep Learning


Intermediate knowledge of Python

Basic understanding of Tensorflow

Basic understanding of Seaborn

Intermediate knowledge of Machine Learning models






Project Description

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.

Project Tasks



Task 0: Get Started

Task 1: Import Libraries and Modules

Task 2: Load the Dataset


Explore the Dataset

Task 3: Create the Pairplots

Task 4: Create a Plot to View Purchasing Amount and Net Worth

Task 5: Create a Plot between Purchasing Amount and Age

Task 6: Create Plots for Comparative Analysis


Build, Train, and Validate the Model

Task 7: Create Input and Output Parameters

Task 8: Split the Training and Testing Data

Task 9: Build the Model

Task 10: Train the Model

Task 11: Validate the Model