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PROJECT
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.
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.
Skills
Machine Learning
Data Visualisation
Deep Learning
Prerequisites
Intermediate knowledge of Python
Basic understanding of Tensorflow
Basic understanding of Seaborn
Intermediate knowledge of Machine Learning models
Technologies
Python
Pandas
seaborn
Tensorflow
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
1
Introduction
Task 0: Get Started
Task 1: Import Libraries and Modules
Task 2: Load the Dataset
2
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
3
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
Congratulations!